Columbus, Ohio Urban Tree Canopy Assessment by Plan-It Geo

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SPRING G | 20115

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“Someone is sitting in the shade today because someone planted a tree a long time ago.” -Warren Buffet

An A Assesssme nt off Urb ban T Tree C Cano opy in th i umbu us, O hio e Citty of Colu Ap ril 20155

Prep pared By Plaan‐It Geo, LL LC, Arvada, C Colorado Acknowledg A gements This projecct was made p possible by t the City of C Columbus Recreattion and Parks Departmeent, Forestryy Section. Core parrtners includ de Mayor Colleman’s Greeen Team, Green Space Worrking Group. In addition n, special thanks g go to Joe Sulaak, Maureen n Lorenz, Dreew Todd, David d Celebrezze,, Erin Neeb, Paul Freedm man, Rick Hicks,, Anita Mussser, Erin Milller, Greg Horrch, Tina Mohn n, John Wood ds, Sandy Freey, Laura Fayy, Shoreh Elhami, Crraig Seeds, Jiim Gates, Jim m Long, Karl Hoessle, Chad Hoff, John Bowers, and Ryan Pilewski. The U.S. Departmentt of Agricultu ure (USDA) p prohibits discrimin nation in all i its programs and activitiees on the basis of race, color, n national orig gin, sex, relig gion, age, disabiility, sexual o orientation, m marital statu us, family status, status as a p parent (in ed ducation and d training grams and acctivities), beccause all or p part of an prog in ndividual’s in ncome is derrived from an ny public assistance pro ogram, or rettaliation. Prepa ared For City o of Columbus Recreation & & Parks Dep partment, Forestryy Section 1533 Alum m Industrial D Dr. West Columbus, O C OH 43209 Cove er Credit Photo o by Randall Schieber

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CONT TENTS EEXECUTIVE SUM MMARY ......................................................................... .................................................... 4 P PROJECT BACK KGROUND ....................................................................... .................................................... 7 P PROJECT FUND DAMENTALS & METHODOLO OGY ..................................... .................................................... 8 MAPPING LAND COVER ............................................................................... .............................................................................. 8 IDENTIFYING POSSIBLE PLANTING AREAS ......................................................... .............................................................................. 8 OPY RESULTS.................................................... .............................................................................. 9 VISUALIZING URBAN TREE CANO DEFINING ASSEESSMENT LEVELS ..................................................................... ............................................................................ 10 ECOSYSTEM SERVICES E .................................................................................. ............................................................................ 11 PRIORITIZING PLANTING AREAS ..................................................................... ............................................................................ 12 DETECTING AND ANALYZING URBAN R TREE CANOPPY CHANGE ............................... ............................................................................ 13 A ASSESSMENT R RESULTS & KEY Y FINDINGS .............................................. .................................................. 14 LAND COVER .............................................................................................. ............................................................................ 14 CITYWIDE STUD DY AREA ................................................................................ ............................................................................ 15 ZONING ..................................................................................................... ............................................................................ 16 PLANNING AREEAS ....................................................................................... ............................................................................ 17 FORESTRY MANAGEMENT ZONEES .................................................................. ............................................................................ 18 RIGHT‐OF‐WAY A ......................................................................................... ............................................................................ 19 PARKS AND PARKLAND A ................................................................................ ............................................................................ 20 WATERSHEDS ............................................................................................. ............................................................................ 22 BLUEPRINT COLUMBUS O PROJECT AREAS ......................................................... ............................................................................ 23 NEIGHBORHOO ODS ....................................................................................... ............................................................................ 24 CENSUS BLOCKK GROUPS AND SOCIO O ‐DEMOGRAP PHICS ....................................... ............................................................................ 25 ECOSYSTEM SERVICES E .................................................................................. ............................................................................ 26 A ANALYZING TH HREATS TO CAN NOPY ....................................................... .................................................. 29 EMERALD ASH BORER ................................................................................. ............................................................................ 29 ASIAN LONG‐HORNED H BEETLE ....................................................................... ............................................................................ 30 BUSH HONEYSUCKLE ................................................................................... ............................................................................ 31 R RECOMMENDA ATIONS & STRA ATEGIES ................................................... .................................................. 33 RECOMMENDAATION 1: COLUMBBUS FORESTRY TAKES THE A LEAD IN GROWING URBAAN CANOPY ............................................................. 33 RECOMMENDAATION 2: DEVELOP FUTURE CANOPPY GOALS CITYWIDE AND BY ZONINNG USE CLASS ......................................................... 33 RECOMMENDAATION 3: TARGET NEW PLANTINGSS TO ADDRESS CITTY PRIORITIES ..... ............................................................................ 35 RECOMMENDAATION 4: DEVELOP AN URBAN FORREST MANAGEMENT PLAN ........... ............................................................................ 39 RECOMMENDAATION 5: ENGAGEE THE COMMUNITTY ............................................ ............................................................................ 39 RECOMMENDAATION 6: UTILIZE TTHIS ASSESSMENTT AND ASSOCIATEED TOOLS .......... ............................................................................ 39 A APPENDIX ............................................................................................. .................................................. 40 ACCURACY ASSSESSMENT .............................................................................. ............................................................................ 40 ASSOCIATED TOOLS O .................................................................................... ............................................................................ 42 COMPREHENSIVE ASSESSMENT AREA RESULTS ................................................. ............................................................................ 44 COMPREHENSIVE ECOSYSTEM SERVICES RESULTS .............................................. ............................................................................ 85 R REFERENCES ......................................................................................... .................................................. 88

C CONTENTS

FIGURES & TABLES FIGURES Figure 1: Five Primary Land Cover Classes were generated from Aerial Imagery‐based Analysis ........................................ 8 Figure 2: Examples of Urban Tree Canopy Coverage by Neighborhood ............................................................................... 9 Figure 3: Examples of Assessment Geographies ................................................................................................................. 10 Figure 4: Ecosystem Services and Benefits provided by Urban Tree Canopy ..................................................................... 11 Figure 5: Distribution of Canopy Sample Points ................................................................................................................. 13 Figure 6: Detailed Land Cover ............................................................................................................................................. 14 Figure 7: Percent Urban Tree Canopy by Parcel ................................................................................................................. 15 Figure 8: Zoning Use Classes and Associated Distributions ................................................................................................ 16 Figure 9: Acres of Urban Tree Canopy and Possible Planting Area by Planning Area ......................................................... 18 Figure 10: Planning Areas and Forestry Management Zones ............................................................................................. 18 Figure 11: Percent Total Possible Planting Area within the ROW of Forestry Management Zone ..................................... 19 Figure 12: Sports fields, such as this baseball diamond, are considered unsuitable for planting ...................................... 20 Figure 13: Percent Urban Tree Canopy in Parks and Parkland. .......................................................................................... 21 Figure 14: Images of Parks or Parkland with High PPA ....................................................................................................... 21 Figure 15: Percent Urban Tree Canopy by Watershed ....................................................................................................... 22 Figure 16: Percent Urban Tree Canopy within BluePrint Columbus Project Areas ............................................................ 23 Figure 17: Percent Impervious Possible Planting Area within BluePrint Columbus Project Areas ..................................... 23 Figure 18: Neighborhoods with Less than the Citywide Average Urban Tree Canopy ....................................................... 24 Figure 19: Graphic showing the hydrologic processes involving trees/tree canopy .......................................................... 27 Figure 20: Emerald Ash Borer ............................................................................................................................................. 29 Figure 21: Asian Long Horned Beetle .................................................................................................................................. 30 Figure 22: Honeysuckle Field Verification Sites in Tuttle Park against 3‐inch and 2‐foot Resolution CIR Imagery ............ 31 Figure 23: Honeysuckle Field Verification Sites in Alum Creek Parkland COH .................................................................... 32 Figure 24: Percent Impervious by Parcel Illustrates the Urban Heat Island ....................................................................... 35 Figure 25: Target Parking Lot Lacking Trees in Columbus, Ohio ......................................................................................... 36 Figure 26: Parking Lots with Tree Plantings ........................................................................................................................ 36 Figure 27: Census Block Groups Below Median Income and less than Average UTC ......................................................... 37 Figure 28: Census Block Groups Below Median Home Values and less than Average UTC ................................................ 37 Figure 29: Priority Parcels Total Score ................................................................................................................................ 38 Figure 30: Priority Parcels Weighted Score ........................................................................................................................ 38 Figure 31: The Canopy Calculator Tool Populated with Columbus Metrics........................................................................ 42 Figure 32: Image of the Canopy Planner Web Application, ColumbusCanopy.com ........................................................... 43 Figure 33: Planning Area Assessment Results .................................................................................................................... 45 Figure 34: Forestry Management Zone Assessment Results .............................................................................................. 47 Figure 35: ROW within Forestry Management Zone Assessment Results .......................................................................... 50 Figure 36: Parks and Parkland Assessment Results ............................................................................................................ 53 Figure 37: Watershed Assessment Results ......................................................................................................................... 62 Figure 38: BluePrint Columbus Project Area Assessment Results ...................................................................................... 64 Figure 39: Neighborhood Assessment Results ................................................................................................................... 66 Figure 40: Census Block Group Assessment Results ........................................................................................................... 73 Figure 41: Median Household Income and Urban Tree Canopy ......................................................................................... 74 Figure 42: Median Home Value and Urban Tree Canopy ................................................................................................... 75

CONTENTS

Figure 43: Education Level and Urban Tree Canopy ........................................................................................................... 76 Figure 44: Owner Occupancy and Urban Tree Canopy ....................................................................................................... 77 Figure 45: Minority Populations and Urban Tree Canopy .................................................................................................. 78 Figure 46: Ash Tree Canopy on Public Land by Planning Area ............................................................................................ 79 Figure 47: Planning Areas with More than 100 Acres of Privately Owned Ash Tree Canopy ............................................. 80 Figure 48: ALB‐Sensitive Tree Canopy on Public Land by Planning Area ............................................................................ 82 Figure 49: Planning Areas with More than 200 Acres of Privately Owned ALB‐Sensitive Tree Canopy ............................. 83 Figure 50: Weather stations and stream gauges inside city limits, with overlapping hydrologic boundaries ................... 87

TABLES Table 1: Citywide Goals Guiding the Identification of Priority Planting Areas .................................................................... 12 Table 2: Urban Tree Canopy Metrics by Zoning Use Class .................................................................................................. 17 Table 3: Top 3 Planning Areas for Total Acres of PPA ......................................................................................................... 17 Table 4: Top 3 Forestry Management Zones for Total PPA Acres ...................................................................................... 19 Table 5: Top 3 Forest Management Zones for Total PPA Acres within the Right‐of‐Way .................................................. 19 Table 6: Top 5 Parks or Parkland for Total UTC Acres ........................................................................................................ 20 Table 7: Top 5 Parks or Parkland for Total PPA Acres ......................................................................................................... 20 Table 8: Top 3 Watersheds for Total PPA Acres ................................................................................................................. 22 Table 9: Top 3 BluePrint Columbus Project Areas for Acres of Impervious PPA ................................................................ 23 Table 10: Socio‐Demographic data as they relate to Percent Urban Tree Canopy ............................................................ 25 Table 11: Urban Tree Canopy and Associated Ecosystem Service Values .......................................................................... 26 Table 12: Top 5 Planning Areas for Ecosystem Services Benefits ....................................................................................... 26 Table 13: Summarized Hydro results for five separate land cover scenarios. .................................................................... 28 Table 14: Estimated Canopy of Ash Trees on Public and Private Lands ............................................................................. 29 Table 15: Estimated Canopy of ALB‐Sensitive Trees on Public and Private Lands .............................................................. 30 Table 16: Planting Time Table to Reach a 27% UTC Goal ................................................................................................... 34 Table 17: Planting Time Table to Reach a 40% UTC Goal ................................................................................................... 34 Table 18: Sample error matrix for land cover classification within municipal areas in Columbus, OH .............................. 41 Table 19: Planning Area Assessment Results ...................................................................................................................... 46 Table 20: Forestry Management Zone Assessment Results ............................................................................................... 48 Table 21: ROW within Forestry Management Zone Assessment Results ........................................................................... 51 Table 22: Parks and Parkland Assessment Results ............................................................................................................. 54 Table 23: Watershed Assessment Results .......................................................................................................................... 63 Table 24: BluePrint Columbus Project Area Assessment Results ....................................................................................... 65 Table 25: Neighborhood Assessment Results ..................................................................................................................... 67 Table 26: Ecosystem Services Benefits by Planning Area ................................................................................................... 85 Table 27: Summarized Hydro results for five separate land cover scenarios. .................................................................... 86 Table 28: Summarized annual Hydro pollution results for three major constituents, with percent change values. ......... 86

CONTENTS

EXECU UTIVE E SUMMARY Y TThe City of Co olumbus is loccated in centrral Ohio, and as the state’ss capital, it is the largest ciity in the statte with a p population ovver 800,000 p people. Its urb ban forest is ccomprised of trees, shrubss, gardens, green spaces and other n natural areas.. This “forest”” is a critical ccomponent off the City’s grreen infrastru ucture and co ontributes to eenvironmentaal quality, pub blic health, water resource e managemennt, local econ nomies, and th he beautification of o often harsh, p paved landscaapes. Recogniizing the value that trees pprovide and rrealizing the n need for urbaan forest improvementt is the first sttep in protectting this valuaable resourcee. Like other vvalued assets,, urban trees require p proper planning and manaagement to withstand presssures from uurban, suburb ban, and exurban developm ment as w well as pests, diseases, storms and pollu ution. This urban tree canoopy assessmeent provides aa top‐down vview of C Columbus’ urban forest, an nalyzed not o only citywide, but at a varieety of geograaphic scales to o inform vario ous sstakeholders, such as city o officials, city sstaff, and residents alike. Recommend dations are prrovided to guiide the C City in long te erm tree cano opy goals, pro otection of existing trees, aand target plaanting areas tthat will help aaccomplish grreater citywid de priorities such as urban heat island m mitigation.

U Urban Treee Canopy iin Columbu us U Urban tree canopy (UTC) covers 22% off Columbus, aat a total of 311,171 acres. TThese trees p provide a multtitude of eeconomic, envvironmental, and social be enefits, conse ervatively valuued at more tthan $12,151 1,446. There aare 5 57,665 additio onal acres of land available e for possible e plantings, offfering a hugee opportunityy for new treees to be aadded throughout vegetated areas, in aaddition to su urface parkingg lots.

A Assessmen nt Boundarries TThis study asssessed Urban Tree Canopyy (UTC) and Po ossible Planti ng Areas (PPA A) at multiplee geographic levels in o order to provide actionable information n to multiple audiences. M Metrics were ggenerated at the followingg scales: TThe City of Co olumbus city limits, Zoningg Use Classes, Planning Areeas, Forestry Managementt Zones, Streeet rights‐ o of‐way, Parks and Parkland d, Watersheds, BluePrint C Columbus prooject areas, Neighborhood ds, Census Blo ock G Groups, and P Parcels (indiviidual property boundaries). TThe City Zonin ng Use analyssis is particulaarly telling, identifying the residential zo oned areas ass the highest ccontributor off urban tree ccanopy, with 70% of all of the City’s UT C. The industtrial zoned areeas follow, w with 12% o of the City’s U UTC, and then n street Right‐‐of‐Way (ROW W), which conntain 11%. Th hat said, the ggreatest oppo ortunity ffor additional PPA remainss in the reside ential areas, ffollowed by inndustrial and commerciallyy zoned areass. These tthree zone uses represent the greatest land areas in Columbus asside from ROW W. As expectted, the sparssest of the u urban tree canopy is conce entrated in arreas of mixed‐use urban zooning, which aligns with th he city core, aand w where one will find the densest urban d development.

22 2 %

311,1771

Ave erage Treee Can nopy Covver acrosss Colum mbus

Acrres of Tre ee Canopy E EXECUTIVE SU UMMARY

57,,6655 Acres o of Possib ble Plantting Area a

P Planning Areaas break the ccity up into 27 7 areas, and iss the foundattion for the C Canopy Planneer tool that aaccompanies this report. EEastland/Brice e, which is the e largest Plannning Area at 9,719 acres, contains the greatest aamount of UTTC with a totaal of 2,347 acrres, as well ass the greatestt opportunityy for additionaal plantings, w with a ttotal PPA of 4 4,247 acres. Just over half of all the Cityy’s parks and parkland con ntains tree ca nopy, makingg up 12% of the City’s totaal UTC w with a total off 3,690 acres of UTC. The ggreatest oppo ortunity for inncreasing thee canopy in paarks is found in Three C Creeks Park, w with 341 of itss 1,073 acres, or 34% of th he total park sstill open for planting. R Results also re evealed that of the City’s 2 284 Neighborrhoods, close to half of theem have less than the cityw wide aaverage UTC o of 22%. While many of the ese neighborhoods contai n high levels of industrial activity, The Ohio SState University and West Campus neigghborhoods co ontain only 113% and 15% of UTC, respeectively, and have a ccombined total PPA of 789 9 acres.

SSocio‐Econ nomic Rela ationships A A comparison n of tree canopy and socio‐‐demographicc data shows that in Colum mbus, as in m many U.S. citiees, rresidents of m many lower‐in ncome neighb borhoods havve less access to the beneffits that trees provide than n rresidents of higher‐income e neighborhoods. Analysiss against detaailed census d data revealed d a clear relatiionship b between tree canopy and e economic vitaality of house eholds, as seeen not only in n income levels, but also ho ome vvalues, educational attainm ment, and rattes of owner o occupancy. Inn addition, areas with the highest levelss of tree ccanopy also te end to have ssmaller minorrity populatio ons. These finddings were ussed to inform m prioritization of tree p planting efforrts and to add dress equity isssues.

TThreats to Canopy N Natural and anthropogenicc pressures th hreaten Colum mbus’ trees aand the beneffits they provvide. The forest canopy ffrom ash trees in particular has been de eclining due to the tree peest Emerald A Ash Borer (EAB B), and the Assian LLong‐horned Beetle (ALB) tthreatens to diminish additional canoppy cover. With h the anticipaated loss of att least 2 200,000 ash trees due to the EAB otherr invasive pestts, as well as developmentt pressures, tthe urban foreest in C Columbus is d declining at a time when th he City needs it most. The estimated to otal acres of A Ash tree canopy on p public and private lands co ombined totals 2,709 acress. The estima ted total acreeage of tree ccanopy from ttrees ssensitive to th he Asian Longg‐horned Beettle is 5,381 accres.

P Planting G Goals TThree scenarios were evalu uated to guid de future tree e plantings. O ne was to esttablish no nett loss in 5 yeaars. The ssecond of 27% % UTC reflects the recomm mendation fro om the Colum mbus Green Community Plan—Green M Memo III tto increase tre ee canopy a m minimum of 1 1% annually for the next fi ve years. And d the third sceenario reflectts a UTC

$12,,151,4 446

~22,70 00

Value e of Ecosy ystem Benefits

To otal Acress of Ash Tree Canopy

E EXECUTIVE SU UMMARY

771% of Tottal UTC i is in a Residentia al Usse Zone

o of 40% as reco ommended b by American FForests. Consiidering the cuurrent distribution of UTC across the Ciity by zzoning use, go oals were sugggested for caanopy increasse within eachh class. To ach hieve no net loss in 5 yearrs’ time, tthe City of Columbus would need at least 13,000 new w large trees.. Increasing the canopy to 27% strategiically aaccording to tthe PPA availaable per zonin ng use class, tthe City woulld have to plaant a total of 238,281 new w trees o over the course of 5 years,, 291,663 new w trees over tthe course of 10 years, 3422,529 new treees over the ccourse of 1 15 years, and 391,002 new w trees over th he course of 20 years.

P Priority Pla anting Sitees P Priority Planting Areas werre recommended based on n the prioritiees reported by the City of C Columbus. Th hese include mitigaating the urbaan heat island d, saving enerrgy, reducing stormwater runoff, increaasing water q quality, ssocial equity, and canopy cconnectivity.

R Recommen ndations TThis report prresents a varie ety of UTC im mprovement sscenarios, reccommendatio ons that targeet Columbus’ broader ccitywide priorrities, and spe ecific strategies to assist in n implementaation of UTC ggoals. Strateggies utilize thee GIS d data, tools, an nd findings. A Additionally, the UTC assesssment data iss available in an online intteractive map p for p planning and prioritizing trree planting aand maintenaance.

E EXECUTIVE SU UMMARY

PROJEECT BACKG GROUN ND TThe City of Co olumbus is loccated in Frankklin County in n central O Ohio. As the sstate’s capitall, it is the larggest city in the e state w with a population over 800 0,000 people.. By signing th he 2007 U US Climate Prrotection Agre eement and sspearheadingg the G Green Space W Working Grou up (GSWG) an nd initiatives such as tthe Columbuss Green Comm munity Plan, tthe City of Co olumbus rrecognizes the e need to identify solution ns to climate cchange aand strategiess for resilienccy. With the anticipated losss of at leeast 200,000 ash trees due to the Emerrald Ash Bore er (EAB), o other invasive e pests, development presssures, and cu urrent tree p planting levels, the urban fforest in Columbus is decliining at a ttime when the City needs iit most. TTo inform management an nd planning, itt is critical to inventory annd assess treees and forestss to identify saafety cconcerns, maintenance needs, quality, aand large trends which ca n be positive or negative aand impact po olicies aand outreach. An urban tre ee canopy asssessment is a landscape‐sccale study ratther than a lo ocalized plan aand ttherefore requires “thinkin ng big” about natural resources and thee environmen nt, developmeent and regullatory p practices, locaal economies,, interdisciplinary collaborration, and thhe societal desires in our co ommunities. TThe City of Co olumbus comm missioned thiis Emerald Assh Borer and TTree Canopy Analysis in 20 014, taking a proactive aapproach to u understand th he existing conditions and vulnerability of their urbaan tree canop py. This study utilizes aaerial imageryy acquired in the summer of 2013 and p provides an uupdated analyysis of urban tree canopy aand p possible plantting areas. It q quantifies the e net outcome of canopy ggrowth from ttree planting and canopy losses ffrom storms, pests such ass the Emerald Ash Borer (EEAB), natural mortality, and developmeent. Potential losses ffrom the Eme erald Ash Bore er, the Asian Long‐horned Beetle, and tthe invasive b bush Honeysu uckle are also o aassessed. TThe results off this report w will guide and inform Colum mbus’ effortss to increase aand sustain a robust urban n forest ccanopy, deterrmine an acce eptable future e canopy goal, and strengtthen tree pro otection and p preservation on both p public and private properties.

P PROJECT BACK KGROUND

PROJEECT FUNDA U AMENT TALS & MET THOD DOLOG GY TThis section describes the ‘how’ and the e ‘why’ of thiss urban tree ccanopy assesssment. The p process begins with m mapping a lan nd cover dataset, which is then used to generate meetrics aggregaated at variou us geographicc scales, o or assessment levels, which have been defined by th he City of Coluumbus. By ideentifying whaat resources aand o opportunities exist, the current urban forest ecosysttem benefits ccan be quanttified, and futture goals can n be set.

M Mappin ng Land Cover TThe most fund damental com mponent of th his urban tree e canopy asseessment is the creation of an initial land d cover d data set. The process begaan with the acquisition of 2013 high‐ressolution (1‐m meter) aerial imagery from the U USDA’s Nation nal Agriculturral Imagery Program (NAIP P). An object‐ based image analysis (OBIIA) software p program ccalled Feature e Analyst (ArccGIS Desktop)) was used to classify featuures through an iterative aapproach, wh here o objects’ specttral signatures across four bands (blue, green, red, aand near‐infraared), texturees, and patterrn rrelationships were taken in nto account. TThis process rresulted in fivve initial land cover classes as shown in n Figure 1. A After manual classification improvemen nt, additional data layers ffrom the city, such as build dings, roads, aand aagricultural land, were utilized to capture finer featu ure detail andd further categorize the lan nd cover dataaset.

Urban n Tree Canopy:

Other Vegetatio on:

Im mperviouss Surfaces:

B Bare SSoil:

Wateer Bodies:

Tree covver when viewed an nd mapped from above

Grass and open space vegetatio on

Hard surfaces H where rainfall w can nnot permeatte

Not in ncluded in possible planting aareas

Bodies of w water removed ffrom total land ccover

Figure 1: FFive Primary Lan nd Cover Classes were generatedd from Aerial Imaagery‐based Ana alysis

IIdentifyying Posssible P Plantingg Areas O Once the land d cover mapping results we ere finalized, and the existting Urban Trree Canopy (U UTC) was estaablished, P Possible Plantting Areas (PP PA) were derived from the e Other Vegettation and Impervious land d cover classees. Areas in Columbus w where it is no ot feasible to p plant trees, such as sportss fields, airporrts, and agricultural land w were m manually map pped or incorporated from m existing dataa sources andd defined as u unsuitable plaanting areas. The rresulting posssible planting areas were id dentified as vvegetated PPA A or impervio ous PPA, with h an aggegateed value ffor total PPA.

P PROJECT FUND DAMENTALS & & METHODOL LOGY

V Visualizzing Urb ban Tre ee Canopy Resu ults M Maps showingg urban tree ccanopy (UTC)) in this reporrt express relaative levels off canopy as a percentage o of land aarea (not inclu uding water).. UTC levels are divided intto meaningfuul categories ffor each of th he assessment area b boundaries an nd may vary sslightly depen nding on the d distribution w within the target geograph hies. For n neighborhood ds, UTC levelss are broken u up into four cclasses: Less t han 20% UTC C, 21‐30% UTC C, 31‐40% UTTC, and G Greater than 40% UTC. Figure 2 provide es a visual exaamples of whhat the varying levels of UTTC look like aggainst the aaerial imageryy.

Figure 2: Exam mples of Urban TTree Canopy Covverage by Neighhborhood

P PROJECT FUND DAMENTALS & & METHODOL LOGY

D Defining Assesssment Levels In order to be etter inform vvarious stakeh holders, such as city officiaals, city staff, and citizens aalike, urban ttree ccanopy and asssociated info ormation wass calculated fo or a variety o f geographic boundaries, in addition to o the City o of Columbus’ primary study area. The ccitywide land cover dataseet serves as th he input for aanalysis at theese finer aassessment le evels, and a se eries of value es were summ marized for eaach. Outputs include total area (in acres or feet) aand relative values (as perccentages) forr tree canopy,, possible pla nting areas, impervious su urfaces, and unsuitable areas. A ssessment levels include tthe following geograaphic boundaaries: 8 Zoning Use Classees were evalu uated to identify the amou unt of tree caanopy as it reelates to the rregulatory fraamework, and d to help inform m policy devel opment. 27 Planning Areas aand 76 Foresttry Managem ment Zones are useful o manage and d develop thee City’s for cityy foresters annd planners to forest canopy resouurces.

1 C City Bounda ary

343 Paarks or Parklaand spaces were also analyyzed, identifyying opporttunities withi n the City’s p parkland. Alth hough Columb bus’s parks o often extendeed beyond citty boundariess, they were cclipped prior to analysis to provide an asssessment acccurate within n the actual study area. 25 Waatersheds (HU UC12 Drainage Basins) werre analyzed w where they overlap with CCity of Columb bus boundaryy. This assessment has implicatio ns for a varieety of groups vested in Columbus’ level h water resources.

27 P Planning Arreas

284 Neighborhoods

37 BluePrint Colum mbus Project A Areas were the focus for evaluaating where n ew plantings could support the City’s stormw water manag ement. 284 Ne eighborhood s provide actionable information to fraame public outreach effoorts, and 7466 Census Blocck Groups takke into accoun nt socio‐demoographic facttors includingg income, hom me value, educattional attainm ment, ethnicitty, race, and ttenure.

And fin nally, the mosst detailed asssessment can n be found att the parcel level. 274,5332 parcels weere evaluated d within the C City of Columbus. To moree fully utilize tthis information and inform d into priority planting areeas, zoning usse classes weere integrated wn of residential, industrial, and parcelss allowing forr a breakdow commercial use prooperties.

Figure 3: Exam mples of Assessm ment Geographiees

P PROJECT FUND DAMENTALS & & METHODOL LOGY

EEcosysttem Serrvices TThe urban forrest is an integral part of th he character ffor all those tthat live, worrk, or visit thee City of Colum mbus. B Benefits of tre ees are referrred to as “eco osystem services” and desccribe the wayys that urban forests impaact our lives and the e environment. Generally speaking, the return on invvestment of p planting a treee is nearly 200% et al., 1997). Figure 4 desccribes how tre ees can be vaalued in termss of public heealth, energy d demand, (McPherson, e aand public inffrastructure savings, and helps justify th he many reassons to promo ote, establish h, manage, an nd m maintain a robust, “workin ng” urban fore est in Columb bus. Quantifyying these ben nefits helps to o demonstratte the vvalue of urban n forests beyo ond their aesthetic appeall. To do this, tthe followingg software too ols were used d: i‐‐Tree Canopyy, part of the i‐Tree suite d developed by the USDA Foorest Service, estimates treee cover and tree b benefits for a given area w with a random m sampling pro ocess that ennables classificcation of grou und cover typ pes. This ttool was utilizzed to estimatte the carbon n storage, ann nual carbon ssequestration n, and annual air pollution removal p provided by th he urban fore est in Columb bus. i‐‐Tree Hydro, also part of tthe i‐Tree Suitte, is a model designed to estimate run noff in differeent land coverr sscenarios by ccompiling datta on various hydrologic paarameters, sooil properties,, weather datta, streamflow w data, aand more.

Air quality: A :

Pro operty valu ue:

Pub blic health::

Trees absorb, trap, offsett, and hold pollutantts such as particulates, ozone, ssulfur dioxide, carbon monoxide, and CO O2.

Each 10% % increase in treee cover increases h home prices byy $1,300+ (Sander, Polasky, & H Haight, 2010).

Wa ater qualitty:

Energyy conservaation:

Trees d diminish asthm ma symptom ms and reduce U UV‐B exposure b by about 50% (SShade: Healthy Trrees, Healthy C Cities, Health hy People, 2004 4).

Soil aeration, evapotranspiration, n by trees and rainfaall interception impro oves water quaality.

Trees lo ower energy deemand through h summer shadde and winter w wind block, offfsetting powe er plant emissioons.

Ero osion contrrol: Tree roots hold soil togetther along stream m banks and slo opes.

Wildlife habittat:

Stormw water mitiggation: Urban n forests interccept stormwatter, reducing t he need for costly gray infrastr ucture.

Trees prom mote urban bio odiversity.

Crime aand domeestic vviolence: Urban areas directly correlate with lowerr levels of fear, fewer incivilities, and less violen nt and behavior (Kuo,, 2001). aggressive b

Noisee pollution n: Trees act as a buffer, absorbing up to 50% of urban noisee (U.S. Department of Energgy).

Figure 4: Eccosystem Servicees and Benefits pprovided by Urbaan Tree Canopy

P PROJECT FUND DAMENTALS & & METHODOL LOGY

Prioritizing Planting Areas After generating metrics for UTC and PPA throughout the target geographies and considering environmental benefits, priority planting themes were identified to promote the City’s greater goals. These include mitigating the urban heat island, saving energy, reducing stormwater, improving water quality, increasing canopy connectivity, and improving social equity. Feedback was gathered from members of the Greenspace Working Group in order to prioritize the themes. Generally, priority planting locations can be identified by looking at areas with low UTC and high PPA; however, this study strategically considered the themes and their associated priority levels to target locations that will not only increase the canopy, but also contribute to these greater goals. Table 1 outlines the themes and their associated priority level. Table 1: Citywide Goals Guiding the Identification of Priority Planting Areas

Priority

Goal

Rationale

GIS Action

Very High

Urban Heat Island Mitigation

Tree canopy that covers impervious surfaces reduce the urban heat island effect, which is damaging to the environment and unhealthy for people; Disproportionately affects lower income populations

Identify areas of high UHI using percent total impervious; Locate priority planting areas in residential, commercial, and industrial areas

High

Energy Savings

Trees provide a reduction in energy use in the summer by providing shade and in the winter by reducing wind

Identify residentially zoned areas with low UTC and high PPA

High

Stormwater Reduction

Trees can be integrated to help manage stormwater, specifically when targeting impervious surfaces

Using the BluePrint Columbus project areas, identify locations with high PPA Impervious

Medium

Water Quality

Trees located adjacent to streams and water bodies improve water quality

Buffer all surface water, such as streams and ponds, by 100’ to identify riparian zones with high PPA

Medium

The presence of trees aligns with increased economic vitality and quality Socio‐demographic of life. Targeting tree plantings in low relationships income areas may also help mitigate the UHI

Connectivity

Large tracts of connected canopy cover can improve habitat for local wildlife

Consider areas where there is below average UTC, and also have lower than citywide average levels of income, education, owner‐occupancy, or age

Buffer large tracts of existing canopy (tracts of at least 5 acres) by 100’ to target PPA that may help connect them

PROJECT FUNDAMENTALS & METHODOLOGY

D Detecting and Analyzing Urb ban Treee Canop py Chan nge U Urban tree canopy (UTC) change analyssis quantifies canopy loss ddue to naturaal events (i.e. disease) and aanthropomorphic influences (i.e. develo opment) and gains due to canopy grow wth and new p plantings. Using i‐Tree C Canopy softw ware and histo orical imageryy, canopy and d non‐canopy was interpreeted for 1,501 1 points for 20 002 and 2 2014. The results yielded 2 24% for 2002 (1.1 standard d error) and 223.5% for 20114 (1.09 stand dard error). The eestimated urb ban tree cano opy derived th hrough this asssessment waas 22%. The cconsistency in n these UTC eestimates sshow that little or no canopy change haas occurred w within the pastt 12 years in Columbus.

Figure 5: Distrib bution of Canoppy Sample Pointss

P PROJECT FUND DAMENTALS & & METHODOL LOGY

ASSESSSMEN NT RES SULTS S & KE EY FIN NDING GS TThis section p presents the results of this study, includ ding the land ccover base m map as well ass canopy analyysis aacross the varrious geograp phic assessme ent boundarie es. These resuults inform a strategic app proach to futu ure p planting and p priority plantiing areas, wh hich are discusssed in the Reecommendattions section of this reportt. C Complete assessment resu ults for target geographies and additionnal maps can be found in the Appendix..

LLand Co over In 2013, 22% of Columbus was covered by tree cano opy, 31% was grass and op pen space, and d 38% impervvious. TThe detailed land cover dataset further breaks down n impervious into categories including rroads (11%), parking lo ots (6%), buildings (10%), and other impervious (11% %).

Figure 6: Detailed Land CCover Classification C

A ASSESSMENT R RESULTS & KEY Y FINDINGS

C Citywid de Studyy Area W Within the Citty of Columbu us, 22% of the e almost 140,,000 land acr es (excludingg water) is covvered by urbaan tree ccanopy (UTC),, with 41% off the remainin ng land area aavailable for nnew plantingss. Figure 7 illu ustrates relattive levels o of UTC througghout the cityy by parcel.

Fig gure 7: Percent U Urban Tree Canoopy by Parcel

A ASSESSMENT R RESULTS & KEY Y FINDINGS

ZZoning TThis study pro ocessed urban n tree canopyy (UTC) levelss and Possiblee Planting Areea (PPA) data at the level o of 8 zzoning use claasses. These cclasses group official zone districts into categories based on land use. The locaations aand city‐wide distribution of zoning use e classes acrosss Columbus are shown in n the Figure 8 map and chaarts.

Figure 8 8: Zoning Use Cla asses and Associ ciated Distributioons of Urb ban Tree Canopy and Total Possib ible Planting Areea Distribution o of Zoning Use Classes*

D Distribution of TTotal Possible Planting Area by Zo oning Use Classees

of Existing Urbann Tree Distribution o Canopy by Zoning Use Classses

*Note: Zoning C Classes and Streeet ROW do not eequal citywide to otal.

A ASSESSMENT R RESULTS & KEY Y FINDINGS

As a key regulatory tool that impacts development in Columbus, zoning is important to consider when evaluating and promoting urban tree canopy. To provide data that advances urban forest policy and management, zone districts were generalized into land use classes and the resulting 8 classes were assessed for tree canopy and Potential Planting Area (PPA). Distribution of the total UTC between Zone Use Classes ranged widely from less than 1% to 70%, with residential zoned areas being the highest contributor of urban tree canopy, while also maintaining the greatest PPA. As expected, the sparsest or the urban tree canopy is concentrated in areas of mixed‐use urban zoning, which align with the city core, and where one will find the densest development. The mixed‐use suburban areas, however, are located on the outskirts of the City and have the highest percent PPA Vegetation, with 39%. See Table 2 for more details. Table 2: Urban Tree Canopy Metrics by Zoning Use Class % UTC within Zoning Class

% PPA Vegetation

% PPA Impervious

% Total PPA

Total PPA Acres

Commercial

12%

23%

41%

63%

7,578

Industrial

13%

25%

19%

44%

11,889

Institutional

17%

29%

22%

51%

1,137

Mixed Use, Suburban

39%

46%

673

Mixed Use, Urban

12%

23%

35%

559

Parking

15%

19%

57%

76%

Residential

30%

35%

41%

30,263

Street ROW

16%

24%

0.1%

24%

5,113

Zoning Use Class

Planning Areas This study processed urban tree canopy (UTC) totals and Possible Planting Areas (Vegetation, Impervious, and Total PPA) data at the level of 27 Planning Areas. This target geography is also the foundation for data viewable on the associated Canopy Planner web tool, which is explained in greater detail in the Associated Tools section of the Appendix of this report. Eastland/Brice (Area 24) is the largest Planning Area at 9,719 acres, and contains the most UTC with a total of 2,347 acres, or 25% of its total area. This area also contains the most acreage of PPA, along with the Northland and Far South planning areas. Combined, these areas offer 11,398 acres of planting space, or 20% of the City’s entire possible planting area. Table 3 highlights these top 3 Planning Areas with the greatest potential for planting. Table 3: Top 3 Planning Areas for Total Acres of PPA Plan Area ID

Planning Area

% UTC within Zone

% PPA Vegetation

% PPA Impervious

% Total PPA

Total PPA Acres

Eastland/Brice

25%

33%

12%

45%

4,247

Northland

25%

31%

15%

46%

3,847

Far South

20%

37%

10%

47%

3,304

The complete distribution of total acreage of existing UTC, as well as total acreage of PPA, for each Planning Area is shown in Figure 9.

ASSESSMENT RESULTS & KEY FINDINGS

Figu ure 9: Acres of Urrban Tree Canop py and Possible PPlanting Area byy Planning Area

FForestryy Manaagemen nt ZZones TThis study pro ocessed urban n tree canopyy (UTC) totals and Possible P Planting Areass (Vegetation, Impervious, and Total PPA) d data at the levvel of 76 Fore estry M Management Zones. The FForestry M Management Zones are su ubsets of the P Planning Areaa assessment boundaries. TThis cconcept is illustrated in Figgure 10. In th he ssubset of this map, it is clear that the Near EEast Planning Area, or Plan nning Area 19 9, ccontains Forestry Managem ment Zones 1 19A aand 19B. Similarly, the Dow wntown Plann ning A Area, or Plann ning Area 18, contains FForestry Management Zon nes18A and 18 8B.

Figure 10: Pllanning Areas annd Forestry Man nagement Zones

A ASSESSMENT R RESULTS & KEY Y FINDINGS

FForestry Management Zon nes 10C, 05C, and 10D have e the greatesst percent UTC C, while the ggreatest amount of P PPA acreage, as shown in TTable 4, is fou und in zone 25B, followed by 12A, and 27. Completee assessment results ccan be found in the Appendix of this rep port. Table 4: To op 3 Forestry Ma anagement Zonees for Total PPA Acres Fo orestry Manage ement Zone

% UTC within Zone

% PPA A Vegetatio on

% PPA Impeervious

% Total PPA

Total PPA Acres

25B

13%

29%

99%

38%

2,161

12A

24%

38%

115%

53%

2,009

10%

38%

44%

42%

1,907

R Right‐of‐Way U Urban Tree Caanopy (UTC) aand Possible Planting Areaas (Vegetation, Impervious, and Total PPA) metrics havve also been eevaluated for City’s Right‐o of‐Way at a variety of scale es. These include citywiide, as well ass for each of tthe Planning A Areas and ffor each of the Forestry Management ZZones. The tottal land aarea of ROW iin Columbus (excluding waater) is 21,283 acres. 3 3,389 of these e acres contain UTC (or 16 6% of the ROW W), with an aadditional total PPA of 5,11 13 acres, or 2 24%. While m much of the R ROW is imperrvious road su urface, this PP PA offers an o opportunity tto help increaase air qualityy and combat the urban he eat island. In addition, th he ROW is lan nd owned and d managed byy the City, p providing opp portunity for tthe City to be e proactive in new p plantings. Tab ble 5 shows th he top 3 Forest Manageme ent Zones ffor total possiible planting aarea acres within the rightt‐of‐way. FForest Managgement Zone 24A has the ggreatest pote ential with 2 249 acres of P PPA. Additional maps can b be found in th he Appendix ssection of thiss report.

Figure 11: Percent Totall Possible Plantin ng Area within thhe ROW of Foresstry Managemen nt Zone

Table 5: Top 3 Forest M Management Zo ones for Total PPPA Acres within tthe Right‐of‐Wa ay ROW b by Forestry Manage ement Zone

% UTC within Zone

% PPA A Vegetatio on

% PPA Impeervious

% Total PPA

Total PPA Acres

24A

15%

37%

0.0003%

37%

249

04B

12%

34%

0.0002%

34%

181

12A

10%

30%

0.0006%

30%

146

A ASSESSMENT R RESULTS & KEY Y FINDINGS

P Parks and Parkkland TThis study pro ocessed urban n tree canopyy (UTC) totalss, and Possiblee P Planting Area (PPA) data fo or Parks and P Parkland thro oughout the CCity of C Columbus. Jusst over half of all the City’ss parks and parkland contaains ttree canopy, m making up 12 2% of the Cityy’s total UTC w with a total oof 3 3,690 acres off UTC. While parks are ofte en great spacces to plant trrees, tthese lands also include go olf courses and sports field ds that fall intto the u unsuitable are eas category and thus werre removed frrom PPA. Figgure 12: Sports ffields, such as th his baseball diam mond, are consid dered unsuitablee for planting

TThree Creeks Park has the greatest amo ount of urban n tree canopyy with 5 575 acres, followed by Big Run Park, Ch hampions Golf Course, Big W Walnut Park, and Griggs Paark. Table 6 lists the top 5 parks or parkkland with thee greatest am mounts of totaal UTC aacres. Table 7 7 shows the to op 5 parks or parkland with the greatesst amount of possible plan nting area in aacres. W While Three C Creeks Park co ontains the m most acres of existing UTC,, it also contains the most acres of PPA with 341 ttotal acres avvailable. Grigggs Park also co ontains a high h amount of PPPA, in additiion to having a high existin ng UTC, w with 57 acres still available e for planting. Figure 13 sh hows the parkks and parklan bus shaded byy percent nd in Columb u urban tree canopy, and callls out Three Creeks Park. Tablee 6: Top 5 Parks or Parkland for Total UTC Acress Parks orr Parkland

UTC Acress

% UTC within Area

% PPA V Vegetation

% PPA Imperviouss

% Total PPA

Three Crreeks Park

575

57 7%

33%

34% %

Big Ru un Park

231

85 5%

12%

13% %

Championss Golf Course

111

54 4%

20%

23% %

Big Walnut Park

106

70 0%

25%

28% %

Grigggs Park

59 9%

31%

36% %

Tablee 7: Top 5 Parks or Parkland for Total PPA Acress Parks orr Parkland

% UTC within Area

% P PPA Vegettation

% PPA Im mpervious

% Total PPA A

Total PPA A Acres

Three Crreeks Park

57%

33 3%

34%

341

Berliner SSports Park

26%

36 6%

41%

944

Raymond M Memorial Golf Co ourse

19%

33 3%

36%

766

Nafzgger Park

56%

43 3%

44%

644

Grigggs Park

59%

31 1%

36%

577

A ASSESSMENT R RESULTS & KEY Y FINDINGS

Figure 13 3: Percent Urban n Tree Canopy inn Parks and Parkkland. Three Creekss Park has both tthe greatest amoount of UTC acrees and the greatest amount of PPA acres

Threee Creeks Park

Raymond Mem morial Golf Coursse (area outsid de of fairways)

Berlin ner Sports Park

Parks or Parklannd with High PPA A Figuree 14: Images of P (Sourcce: Google Mapss)

A ASSESSMENT R RESULTS & KEY Y FINDINGS

W Watersheds TThis study pro ocessed urban n tree canopyy (UTC) totals, aand Possible P Planting Areaa (PPA) data forr 25 Watershe eds (or HUC12 D Drainage Basins). Findings at a watershe ed leevel can be used for any n number of d different stud dies and proje ects, includingg h hydrologic mo odeling tools,, water rresource man nagement plans, forest m management plans, water quality studies, aand more. No ote that becau use the aassessment le evels were clipped to the b boundary of C Columbus, partial w watersheds are included att this level. FFigure 15 show ws the percentage of urbaan ttree canopy b by watershed.. Table 8 show ws tthe top 3 watersheds for p potential p planting. Figure 15: Perccent Urban Tree Canopy by Watershed

Ta able 8: Top 3 Wa atersheds for Tottal PPA Acres

Wate ersheds

% UTC within Area

% P PPA Vegettation

% PPA Im mpervious

% Total PPA A

Total PPA A Acres

050600 0011602

28%

33 3%

11%

44%

10,1 176

050600 0012301

18%

27 7%

11%

38%

6,1227

050600 0010806

18%

27 7%

18%

45%

4,7999

A ASSESSMENT R RESULTS & KEY Y FINDINGS

B BluePrint Columbus P Project Areas TTo help give fo ocus to poten ntial stormwaater improvem ment zones, m metrics were generated fo or the City’s B BluePrint C Columbus pro oject areas, byy assessing urrban tree can nopy (UTC) tootals and Posssible Planting Areas (Vegettation, Impervious, and Total PPA) within each area. This w will help targett new plantin ngs where theey are needed d to m manage storm mwater. TThe results revealed that p project area FFifth by North hwest (West FFifth) has thee one of the lo owest percen ntages of U UTC at only 14 4% and the greatest amou unt of impervious PPA, witth 202 acres o or 23% of the total projectt area. H Hilltop (Early Ditch)‐4 and Hilltop (Earlyy Ditch)‐1 havve the next grreatest amou unts of impervvious PPA areea, with 1 176 and 168 aacres, respecttively. Impervvious PPA is h highlighted fo r these assesssment area b boundaries, due to the aadverse effectt of impervious surfaces on stormwater managemennt. Percent U UTC and Total PPA are also o shown in FFigures 16 and d 17. Table 9: Top 3 Blu T uePrint Columbu us Project Areas ffor Acres of Imppervious PPA BluePrint Columbus Project Area

% % UTC within Area

% PPA Vegetation

% PPA Impervious

% Total PPA

Total PPA Impervious Acres

Fifth by Northwest (W West Fifth)

14%

19%

23%

42%

202

Hilltop (Early Ditcch)‐4

16%

28%

22%

50%

176

Hilltop (Early Ditcch)‐1

19%

25%

18%

43%

168

FFigure 16: Percen nt Urban Tree Ca anopy within Blu uePrint Columbus Projectt Areas

e 17: Percent Imppervious Possiblee Planting Area within Figure BluePrintt Columbus Projeect Areas

A ASSESSMENT R RESULTS & KEY Y FINDINGS

N Neighbo orhood ds TThis study gen nerated urban n tree canopyy (UTC) totalss and Possiblee Planting Areeas (Vegetatio on, Imperviou us, and TTotal PPA) me etrics for each h the City’s 28 84 Neighborh hoods. There are a total off 133 neighbo orhoods that have less tthan the cityw wide average UTC of 22%, which is close e to half of al l the City’s neeighborhoodss. Figure 18 id dentifies tthose neighbo orhoods with less than the e average UTC C, overlaid onn the zoning u use classes. Itt is clear that many of tthe neighborh hoods with low UTC also contain high le evels of indusstrial activity. In addition, tthe “Downtown” n neighborhood d, which conssists of the do owntown core e and is zonedd for mixed‐u use urban, has barely 8% U UTC. The ““Ohio State University” and “West Cam mpus” neighbo orhoods, idenntified in Figure 18 by the blue institutio onal use zzoning, contain only 13% aand 15% UTC, respectively. Between thee two campus neighborho oods, there arre a ccombined total PPA of 789 9 acres.

Figure 18: Neighborh hoods with Less than the Citywidde Average Urbaan Tree Canopy and Zone Use

A ASSESSMENT R RESULTS & KEY Y FINDINGS

C Census Block G Groups and SSocio‐D Demograaphics

Demographic Ovverview

U Urban tree canopy (UTC) p provides a mu ultitude of direct aand indirect b benefits. Som me of these be enefits are mo ore d difficult to me easure, such aas how trees contribute to o livability, heallth, and well‐being. As parrt of this studyy, tthe relationsh hip between ttree canopy aand economicc vvitality was exxplored. Urbaan Tree Canop py was related to d data collected d through the e U.S. Census 2009‐2013 A American Com mmunity Survvey (ACS) 5‐ye ear estimatess at tthe Block Group level. The distribution o of UTC by Blo ock G Groups was analyzed again nst a variety o of ACS data, including med dian income levels, median n home value e, eeducational attainment, ow wner occupan ncy, and race. TThe study unccovered a cleaar relationship between ccanopy cover and socio‐de emographics. TThis study revvealed that ass the amount of canopy co over increased, eco onomic vitalitty also increased. Househo old home values were higher in areas with incomes and h ggreater canop py cover. Educcational attainment was also h higher, as welll as owner occcupancy whiich often indicates stab bility of a given area or neigghborhood. In aaddition, whe ere the highesst levels of tre ee canopy exiist, tthe percentagge of people w who report be eing a race otther tthan white is at its lowest, suggesting th hat not only is tthere a link be etween econo omic vitality aand trees, but aalso that mino ority populatiions may be lacking the p positive health effects of trrees. Tabular results are sshown in Tablle 10. Additio onal maps and d tables can b be ffound in the A Appendix.

The CCity of Columbuus had a total population of 787,0033, as of the 22010 U.S. Censsus. Of that popuulation, 7.6% w were under 5 yeears of age, 23.2% weree under 18 yearrs of age, and 8 8.6% were oveer the age oof 65. The Hisppanic or Latino o population wiithin Colum mbus is 5.6%, ccompared to 3 3.1% for the Sta ate of Ohio..

The ddistribution of race yields a primarily white popuulation, with 611.5% being whiite alone (as sh hown in the fifigure to the rigght). According g to the Censuss’ Amerrican Communnity Survey for 2 2009‐2013, thee popuulation of Colum mbus has a hig gher educational attain inment than thhat of Ohio as w whole, with 33.1% of persoons aged 25 annd older having g a Bachelor’s Degree or higgher, as compaared to 25.2% in all of Ohio. The mediian household income is loweer than the state’s, howeever, with an A ACS estimated $ $44,072 for Colum mbus, versus $$48,308 for Ohiio. The city’s m median valuee of owner‐occcupied housing units is about on par with Ohio at $130,7700 compared d to $130,800, respeectively.

Tab ble 10: Socio‐Demographic data a as they relate tto Percent Urbann Tree Canopy % UTC %

Avverage Median Income

Average Med dian Home Valu ue

% with a Bachelor’s Degreee or higher

% Owner Occupied

% White

0‐‐25%

46,141

138,391

34%

48%

66%

26 6‐50%

49,986

140,573

35%

56%

65%

51‐‐100%

79,307

241,534

51%

65%

79%

A ASSESSMENT R RESULTS & KEY Y FINDINGS

Ecosystem Services Tree Canopy Ecological Benefits Tree canopy and urban forests provide many benefits and ecosystem services that can be associated to economic costs or savings. While trees provide a multitude of benefits that cannot be valuated, such as wildlife habitat, mental and physical well‐being, noise abatement, and energy savings from summer cooling and winter wind block, estimates here are drawn from those which have an associated monetary value. Of these quantifiable ecosystem services, it is estimated that trees in Columbus provide approximately $12,151,446 in savings to the community each year from air pollution removal and carbon sequestration services alone. At 22%, the urban tree canopy provides an estimated value of $9,614,191 in air pollution removal by the urban forest and $2,537,254 in annual carbon storage and sequestration. These values were derived using the United States Department of Agriculture (USDA) i‐Tree Canopy software tools. Using those monetary values provided by Columbus’ current tree canopy, increases or decreases can be projected and quantified. Table 11 shows multiple scenarios of UTC change and the subsequent savings or loss in dollars associated with ecosystem service benefits. Considering the extent of benefits that cannot be measured, these estimates are very conservative. Table 11: Urban Tree Canopy and Associated Ecosystem Service Values

Percent Canopy Cover

Ecosystem Services Estimated Value

40% American Forests Recommendation

$22,502,677

27.3% Green Memo III Goal

$15,358,077

22% Current Urban Tree Canopy

$12,151,446

16.5% Potential Loss due to EAB and ALB

$9,282,354

0% No Canopy Cover

Ecosystem service benefits were also analyzed for Planning Areas. Since Eastland/Brice (Plan Area 24) has the greatest amount of UTC, it follows that it also provides the greatest value of ecosystem services, with an estimated value of $915,571. Table 12 lists the top 5 Planning Areas for value of ecosystem service benefits. The complete table can be found in the Appendix of this report on page 85. Table 12: Top 5 Planning Areas for Ecosystem Services Benefits Plan Area ID

Planning Area

UTC acres

Ecosystem Benefit Value

Eastland/Brice

2,347

$915,571

Rocky Fork‐Blacklick

2,099

$818,896

Northland

2,093

$816,695

Eastmoor/Walnut Ridge

2,079

$811,373

North Linden

1,880

$733,673

ASSESSMENT RESULTS & KEY FINDINGS

TTree Canop py Hydrolo ogical Beneefits TThis assessme ent utilized i‐TTree Hydro, aa modeling prrogram develooped by the U United SStates Departtment of Agricculture (USDA A), to estimatte streamflow w response to o land cover cchanges. Usin ng United Stattes Geologicaal Survey (USG GS) streamfloow data, hourrly weather d data, digital elevation dataa, soil propertties, and vario ous other parrameters, estiimates were ggenerated forr the change iin volume of w water due to canopy channge, as well ass for p pollution load ds. C Columbus’ urban tree cano opy provides a host of ben nefits to the ccity and its inh habitants. G Generally speaking, a healtthy tree cano opy leads to m more regulateed streamflow w by slowing d down the rate e at which sto ormwater reaaches the channel (throughh interception n and soil infiiltration). Trees along tthe banks of rrivers and stre eams help pro omote stability, mitigatingg erosion during large storrm events. Th hey also h help to promo ote general so oil structure aand quality th hrough nutrieent cycling and providing d decomposed o organic m matter for top psoil (Dunne & & Leopold, 19 978). The pho otos on the ri ght show a healthy stream m ecosystem (top) and o one that has lost bank stab bility due to e erosion (botto om). FFor the purpo oses of this stu udy, a topogrraphic index (TI) for the citty of Columbu us was used tto estimate ru unoff vvalues within the city. It sh hould be note ed that this iss a generalize d method; th here are manyy different prrocesses tthat influence e the hydrologgy in such a laarge area and d many assum mptions had to be made. A A few of the aassumptions tthat had to be e made for th his particular study can be found below w, with more d discussion in the A Appendix of this report on page 86. 



All runoff reaches treatment plaants. In realitty, not all of it will end up at the plantss due to pond ding, depre ession storage e, evaporation, etc. The chosen weather station acccurately repre esents the en ntire city. In aan ideal world d, the station would be centrally locate ed, at an averaage elevation n, and have an extensive and verified d data record. Runofff in this case was treated as just imperrvious flow. In reality, perrvious flow haas the potenttial to reach treatment plants through h subsurface fflow or overlaand flow. Mone etary benefit vvalues vary based on the sstudy area, literature values, types off stormwater treatm ment structurres, whether or not there are existin ng structures already in place, and countless other things. Furth her research/sstudy is recom mmended to o obtain more llocalized, accurate estimations. ntire Uniform soil texturre/structure aacross the en nput in i‐Tree Hydro. study area as an in

Figure 19: Graaphic showing th he hydrologic pro ocesses innvolving trees/trree canopy

A ASSESSMENT R RESULTS & KEY Y FINDINGS

Land Cover Scenarios: 1. UTC increased to 40%: Scenario in which UTC is increased to the American Forest recommended canopy value of 40%. Area was taken from impervious and vegetation land cover types based on the ratio between PPA Impervious and PPA Vegetation (determined to be approximately a 1:3 ratio). 2. UTC increased to 27.3%: Based on the Columbus’ Green Community Plan, which recommends a minimum of 1% canopy growth annually over the next five years. 3. Current Conditions (22.3%): Determined from the UTC Assessment done as part of this study. 4. Decrease to 16.5%: This scenario is meant to illustrate the change in runoff in the event that every tree susceptible to the Emerald Ash Borer and Asian Long‐horned Beetle pests were to be removed. 5. Decrease to 0%: All canopy in Columbus is removed and converted to impervious area and vegetation based on the determined ratio between PPA Impervious and PPA Vegetation.

Table 13: Summarized Hydro results for five separate land cover scenarios. Included are UTC percentages, runoff values, percent change in volume, and estimated changes to treatment costs.

Scenario

UTC* (%)

Runoff** (m3)

% Change

Change in Treatment Costs (Estimate)***

No Cover

0.0%

66,293,677

12%

$778,797,842

EAB/ALB Total Loss

16.5%

61,078,743

$226,014,903

Current Conditions

22.3%

58,946,527

N/A

Green Memo Recommendation

27.3%

57,683,053

‐2%

‐$133,928,278

American Forest Recommendation

40.0%

53,588,290

‐9%

‐$567,973,171

* Land cover percentages for this model require that water area be included. Due to Plan‐It Geo’s established practice of using total land area for UTC metrics rather than total area, the percentages vary slightly when compared to the UTC Spreadsheet. ** For the purposes of this study, runoff was defined as i‐Tree Hydro’s impervious flow output. ***Monetary values estimated at $106/cubic meter

As shown in the table above, tree cover can have a dramatic effect on the hydrology of a city. Loss of all canopy in Columbus resulted in an estimated 12% increase in the amount of runoff flowing over impervious surfaces. If all of this additional runoff were to reach treatment plants, the resulting added cost in treatment would be nearly $779 million. On the other hand, increasing canopy within the city limits to 40% would result in an estimated 9% decrease to impervious runoff, saving the city approximately $568 million.

ASSESSMENT RESULTS & KEY FINDINGS

ANALY YZING G THR REATS TO CA ANOP PY The e deleterious impacts to so ociety from thhe associated d loss of canopy include reeduced pro operty values,, less mitigation of storm w water runoff and air polluttion, and incrreased urb ban heat islan nd effect, to n name just a feew. Given thee City’s assum med decline in n UTC for sevveral decades, more needss to be done tto preserve a nd enhance tthe existing trree can nopy, and bettter information is neededd to estimate tthe impact th hat the Emeraald Ash

Fig gure 20: Emerald d Ash Borer (Source: USSFS)

Bo orer and othe er pests will h have on UTC ggoals in Colum mbus. Tablee 14: Estimated Canopy of Ash TTrees on Public a and Private Land ds

EEmerald d Ash B Borer TThe Emerald aash borer (EA AB) is one o of, if not the m most destructtive fforest pest in North American h history, and itt threatens billions of aash trees in laandscapes and d forests tthroughout Ohio and even as far w west as Coloraado. The EAB B is an A Asian tree pesst which arrivved in M Michigan in 2002 (Miller, 2 2015) and iss responsible for widespre ead d destruction off valuable tre ees.

Plan Area ID

1 2 3 4 5 6 7 8 9 10 11 12

Plann ning Area Name

Hayden Run Farr Northwest Far North Rockyy Fork‐Blacklick Northwest N Northland N Far West West Scioto W West Olentangy Clintonville C No orth Linden Northeast N Near North/ University U So outh Linden Hilltop Franklinton Greenlawn/ G Frank Road F Downtown D Near East Eastmoor/ E Walnut Ridge W Far East Near South N Far South Easstland/Brice Westland Rickenbacker Southeast S TOTALS

Acres of Public Ash Canopy 9 28 6 7 11 8 10 9 2 11 10 7

% UTC Loss if Public Ash Trees are Removed 12% 14% 7% 2% 4% 2% 16% 3% 1% 3% 2% 3%

Acres of Estimated P Private Ash Canopy (at 10.3%) 34 117 101 178 103 169 42 92 80 122 143 107

% % UTC Loss iff Public and P Private Ash Trees are Removed 13% 11% 10% 9% 9% 8% 11% 9% 8% 8% 8% 9%

TThis study anaalyzed the po otential 13 6 4% 35 8% A Ash (Faxinus sspp.) tree can nopy loss 14 6 3% 79 9% ffrom the EAB on both public and 15 30 5% 124 9% p private lands, using canopyy area 16 1 1% 19 7% aaverages from m Ash trees 17 5 4% 59 9% inventoried w within parks to o 18 1 2% 7 7% eestimate cano opy loss. The results 19 6 4% 41 8% w were reported d both citywid de as well 20 5 1% 170 8% aas by Planningg Areas. Privaate area 21 2 2% 98 9% ccanopy loss w was estimated d using 22 16 6% 89 9% tthe park inven ntory and validated by 23 4 2% 126 9% rregional i‐Tree e Eco results. Based on 24 4 0% 122 5% 25 11 8% 93 10% tthe analysis, it is estimated d that 26 0 0% 100 10% tthere are 218 acres of totaal Ash tree 27 2 3% 41 10% ccanopy on public land, and d an 218 3% 2,491 9% eestimated 2,4 491 acres of A Ash tree ccanopy on private lands. These results ssuggest that tthe Ash tree ccanopy makees up an estim mated 9% of the total u urban tree canopy.

A ANALYZING TH HREATS TO CA ANOPY

A Asian Lo ong‐horned Be eetle The e Asian Long‐‐horned Beetle (ALB) is an other seriouss threat to Co olumbus’s urb ban tree can nopy. Similarr to the analysis completedd for the EAB B, this study estimated the potential losss of urban tre ee canopy fro om the ALB onn both publicc and private lands, considering the ALB sensitive tre ee population n that is know wn to exist wiithin the City’’s parks. The nsitive trees, aas well as thee potential peercent canopyy loss was esttimated canopy of ALB sen rep ported citywid de and within n Planning Ar eas. On publiic lands, the eestimated am mount of Figure 21: Asian n Long Horned Beeetle (Source: Johnson n State College)

ALB‐sensitive trrees make up 640 acres of f tree canopy,, or 9% of thee City’s total U UTC. On private land, the e estimates yyield 4,741 accres of tree caanopy. Combiined, the estimated tottal citywide caanopy of ALB B‐sensitive treees is 17% of tthe entire urb ban tree cano opy. Ta able 15: Estimateed Canopy of ALB B‐Sensitive Treess on Public and PPrivate Lands

Plan n Area ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Plann ning Area Name

Hayden Run Farr Northwest Far North Rockyy Fork‐Blacklick Northwest N Northland N Far West West Scioto W West Olentangy Clintonville C No orth Linden Northeast N Near N North/Universityy So outh Linden Hilltop Franklinton Greenla awn/Frank Road d Downtown D Near East Eastmoor/Walnut Ridgge Far East Near South N Far South Easstland/Brice Westland W Rickenbacker Southeast S TOTALS

Acres Pu ublic ALB‐Senssitive Canop py

% UTC Loss if Publicc ALB‐ Sensitivee Trees are Rem moved

Acres of Estimated Priivate ALB‐ Sensiitive Canopy (aat 19.7%)

% UTC Loss if P Public and Privatee ALB‐Sensitive Trees are Removed

12% %

18%

25% %

224

20%

21% %

194

20%

4% %

339

17%

7% %

197

17%

15% %

323

19%

19% %

20%

5% %

176

16%

7% %

153

17%

13% %

232

18%

6% %

274

16%

3% %

205

17%

29% %

23%

10% %

151

18%

10% %

237

17%

8% %

15%

7% %

112

17%

13% %

17%

20% %

20%

15% %

324

19%

4% %

188

18%

14% %

169

18%

7% %

240

18%

1% %

233

10%

9 0 1

6% % 1% % 2% %

177 191 79

18% 18% 18%

640

9% %

4,741

17%

A ANALYZING TH HREATS TO CA ANOPY

B Bush Ho oneysuckle The invasive bush Hone eysuckle (Lonnicera Mackii)) grows rapidly and preven nts mpting to grow w underneath h it. It inhabits sunlight frrom reaching plants attem abandoned fields, road dsides, woodl ands, and edgges of marshes. Data on itts extent and distrib bution would help supportt and improvee programs th hat remove itt, such as the Colum mbus Ecologicaal Restorationn Program (C CERP), and asssist the City in n managing this invasive species. A As part of thiss assessment,, extensive exxploration waas done to invvestigate if it is possible to utilize leaf‐o off color‐ infrared (CIR) aerial photoggraphy and sp pectral remotte sensing claassification teechniques to m map areas wiith higher p probability of honeysuckle. Known hone eysuckle sitess were providded by the Citty. While no sspecific mapp ping aaccuracy could be guaranteed, the task proved more e problematicc than initiallyy understood d. One of the cchallenges waas managing tthe size of the e CIR imageryy to be used i n the classificcation. Multiple attempts were m made to resam mple the imagery to 1‐ and d 2‐foot resolution; howevver, the output imagery yiielded 3‐10 G GB size ffiles for each of 16 tiles. In addition to the resolution n being too hiigh for image classification n and pre‐pro ocessing, o other concern ns with the im magery included:     

Leaf‐o out had begun for native vvegetation Confu usion with law wn/turf grass with similar ttexture Confu usion with braanches and branch shadow ws Unkno own shrubs e exist with a sim milar texture, height and ccolor Inconsistency in im mage quality aamong flight lines, such ass graininess, sshadow length, etc.

W While masking out existingg canopy migh ht minimize cconfusion wit h conifers and branches, m much of the ttargeted h honeysuckle is below cano opy, and such an approach would excludde most of th he honeysuckkle. Another cchallenge in this analysis was having a limited num mber of trainiing samples aavailable for cclassification and verificatiion since tthey had to be e collected from public lan nds. Figure 22 2 shows areass in Tuttle Parrk with mediu um‐high (70% %) h honeysuckle d density and Euonymus forttunei ground cover. The im mage on the lleft shows thee 3‐inch resolution CIR imagery and tthe image on the right sho ows the data rresampled too 2‐foot resolu ution.

Fig gure 22: Honeyssuckle Field Veriffication Sites in TTuttle Park againnst 3‐inch and 22‐foot Resolution n CIR Imagery

A ANALYZING TH HREATS TO CA ANOPY

A Additional field verification n sites in Alum m Creek P Parkland COH H confirmed how challenging it is to d distinguish ho oneysuckle fro om other types of vvegetation in the imagery. In Figure 23, the green eellipse contain ns honeysuckkle, goldenrod d, burdock, aand mid‐storyy boxelder, black locust, ailanthus. The yyellow ellipse in the top lefft corner conttained EEuonymus forrtunei and no o honeysuckle e. The other tthree ellipses contained 50 0‐60% honeyssuckle. C Consideration ns for future iimagery colleection C Considering th he observatio ons listed abo ove, it was d determined th hat if any imaagery could w work for this cclassification, the ideal spe ecs would incllude satellite imagery betw ween 0.5‐1.0 m meter resoluttion m multispectral imagery (4‐8 common spe ectral bands) aacquired prior to native ve egetation/can nopy leaf‐out. M Many local invvasive species including ho oneysuckle le eaf‐ Figure e 23: Honeysucklle Field Verificatiion Sites in Alum m Creek Parkland COH o out roughly tw wo weeks earrlier than natiive vegetation n aand also main ntain leaf‐on rroughly two w weeks longer tthan native ve egetation. Additionally, LiD DAR data could be used too filter vegetation in the heeight range th hat h honeysuckle ggrows. Where e conifers were an issue, LiDAR could a lso help to seeparate them from honeyssuckle w with the prop per timing of imagery collection. It may also be beneeficial to collect aerial imaggery during a drought yyear so that laawn/turf grasss would be m more distinguishable from honeysuckle. A Alternative Analyses A Alternatives e exist to evaluaate the magnitude of hone eysuckle withhin Columbus. Since remotte sensing anaalysis p proved to be such a challenge, it may be more practical to perforrm an intensivve ground sam mpling of hon neysuckle in a handful o of parks and th hen extrapolaate the resultts to all otherrs accordinglyy. The park samples should d be d distributed ge eographically and in terms of geology/e ecology in ordder to create strata or stratifications. Th he City ccould use volu unteers to asssist with colle ection efforts, or create a w web applicatiion to crowd‐‐source the co ollection o of honeysucklle abundance e. Once estimates have bee en developedd, a cost/beneefit analysis sshould be don ne for rremoving and d restoring ho oneysuckle are eas.

A ANALYZING TH HREATS TO CA ANOPY

RECO OMMEN NDATIIONS & STR RATEG GIES TThe following recommendations and strategies addrress ways in w which the Cityy of Columbus can maximiize the vvalue of this rreport. The City should use e these sugge estions, along with the keyy findings in th his report, as a sstarting point for an interd disciplinary go oal‐setting pro ocess and dettermination o of priorities and strategiess.

R Recomm mendattion 1: C Columb bus Foreestry Taakes thee Lead in G Growing Urban n Canop py TThe City of Co olumbus Forestry Division of the Recreaation and Par ks Department should takke the lead in growing u urban canopyy within City o owned lands, such as Parkss and Right‐off‐Way (ROW)). Urban tree canopy in RO OW m makes up 16% % of the City’ss total UTC, but still offers 5,100 acres oof additional vegetated plaanting area. P Parks and p parkland makkes up 12% off the City’s tottal UTC, and sstill offers 2,3377 acres of aadditional veggetated planting area. C Combined, this space provvides an opportunity for th he City to plannt up to 259,1182 total treees. If just half of this aarea was plan nted with 129,591 trees, th he City could increase the UTC by 3,7388 acres and brring the canopy cover u up to 25%. Paarks and ROW W land only maakes up 21% of the area o f Columbus, h however, limiting the impact the C City can have on increasingg UTC. It is criitical that effo orts are also m made to engaage private laand owners.

R Recomm mendattion 2: D Develop p Futurre Canopy Goaals Cityw wide aand by Zoning Use Claass SSuggested tre ee planting an nd canopy goaals are presen nted citywidee and within eeach zoning u use class baseed on the d distribution of UTC and PPA across each h class. The fo ollowing urbaan tree canop py scenarios h have been evaaluated: et loss of UTC C in 5 years 1. No ne 2. 27% U UTC which refflects the recommendation from the Coolumbus Greeen Communitty Plan—Greeen Memo III to iincrease cano opy a minimum of 1% annu ually for the nnext five yearrs 3. 40% U UTC which refflects the ave erage citywide e recommenddation from A American Foreests for citiess east of the M Mississippi River TThe estimatess to achieve these goals we ere derived frrom Plan‐It G Geo’s Canopy Calculator sp preadsheet to ool. The ffollowing assu umptions werre used with ggrowth, morttality, and reggeneration rates based on urban forestt rresearch. Other parameterrs can easily b be modified in n the tool forr additional sccenarios. Avera age Tree Size at Maturity: 40‐foot crow wn spread (200‐foot radius) New T Tree Mortalitty: 3% Annual Mortality o of Existing Caanopy: 7% Annual Growth fro om Existing C Canopy: 6% Naturral Regenerattion: 3% Annual Canopy Lo oss from New w Developmen nt: 10 acres FFor the scenario of no net loss in 5 yearrs, the City of Columbus woould need a least 13,000 n new large trees based o on the assumptions above. No volume o of new tree p planting can ooffset a loss o of canopy in a 5‐year time period b because the n newly planted d trees are sm mall will not add substantiaally yet to oveerall canopy. Therefore, th he most R RECOMMENDA ATIONS & STR RATEGIES

3 33

effective means to control net loss in a short time span is by protecting existing canopy and maintaining (pruning, mulching, watering, etc.) existing trees with the potential to grow such as those planted within the past 10 or 20 years. To increase canopy across the City to 27%, the City would have to plant and grow 238,281 large trees over the course of next 5 years. Spread out over 20 years, the City would have to plant and grow closer to 391,000 trees. Table 16: Planting Time Table to Reach a 27% UTC Goal Total Possible Planting (acres)

Current % UTC by Zoning Class

Future Canopy Goals

No Net Loss 5 years (22%)

1,412

7,578

12%

20%

3,613

11,889

13%

18%

2,238

391

1,137

18%

1,464

139

673

1,578

147

Parking

Residential

73,037

Total Land Acres

UTC Acres

Commercial

11,939

Industrial

26,883

Institutional

Zoning Use Class

Mixed Use, Suburban Mixed Use, Urban

# of Trees Required to Achieve 27% UTC Goal 5 Years

10 Years

15 Years

20 Years

25,189

27,794

30,284

32,664

37,613

43,880

49,853

55,545

23%

146

4,142

5,048

5,921

6,765

10%

15%

2,655

3,141

3,616

4,080

559

13%

1,987

2,487

2,974

3,451

15%

24%

206

507

786

1,063

1,339

21,748

30,263

30%

35%

11,714

142,099

178,544

213,216

246,201

Street ROW

21,283

3,389

5,113

16%

19%

1,292

24,089

29,984

35,602

40,957

Totals

138,516

30,853

57,282

22%

27%

13,399

238,281

291,663

342,529

391,002

To increase the canopy across the City to 40% as recommended by American Forests, the City would have to plant and grow 877,738 large trees over the course of 5 years, 931,120 new trees over the course of 10 years, 981,986 new trees over the course of 15 years, and 1,030,459 new trees over the course of 20 years. Achieving 30% or higher average UTC in Columbus will likely take at least 15 to 20 years. This will be impacted by planting, maintenance, protection (through ordinances and regulations), new development, storms, pests and diseases, and, ultimately, the community. Table 17: Planting Time Table to Reach a 40% UTC Goal Total Possible Planting (acres)

Current % UTC by Zoning Class

Future Canopy Goals

No Net Loss 5 years (22%)

5 Years

10 Years

15 Years

20 Years

1,412

7,578

12%

39%

110,446

113,051

115,541

117,921

26,883

3,613

11,889

13%

32%

171,988

178,255

184,228

189,921

2,238

391

1,137

18%

39%

146

16,929

17,834

18,708

19,551

1,464

139

673

10%

29%

9,972

10,458

10,933

11,397

1,578

147

559

25%

8,746

9,245

9,733

10,209

Total Land Acres

UTC Acres

Commercial

11,939

Industrial Institutional

Zoning Use Class

Mixed Use, Suburban Mixed Use, Urban

# of Trees Required to Achieve 40% UTC Goal

Parking

15%

47%

206

1,277

1,556

1,833

2,109

Residential

73,037

21,748

30,263

30%

48%

11,714

481,098

517,544

552,216

585,201

Street ROW

21,283

3,389

5,113

16%

26%

1,292

77,282

83,176

88,795

94,150

Totals

138,516

30,853

57,282

22%

40%

13,399

877,738

931,120

981,986

1,030,459

RECOMMENDATIONS & STRATEGIES

R Recomm mendattion 3: TTarget N New Plaantingss to Add dress Ciity P Prioritie es P Priority planting themes w were develope ed by the Colu umbus Recreaation and Parrks Departmeent in conjuncction with tthe Greenspace Working G Group (GSWG G). Themes we ere ranked acccording to th he members o of the group, resulting in the followin ng order of prriorities. In ad ddition to thiss report, a Caanopy Planner website allo ows users to vview and w weight prioritties at the Planning Area and Census Blo ock Group scaales (details o on page 42), aand a GIS dattaset has b been delivere ed that integraates prioritiess into parcel llevel data. Thhe GIS datasett is discussed d in more detaail on p page 38.

P Priority 1: Urban Hea at Island M Mitigation D Development and urbanizaation is often an indicator of a healthy econ nomy. One co onsequence o of urban deve elopment is th he increase of paved ssurfaces which also include e deleterious eenvironmentaal impacts succh as increase ed storm w water runoff, flooding, and d increased ttemperaturess experienced d through the Urban H Heat Island efffect (UHI). Co olumbus’ high hest p priority for loccating potenttial planting sites are tthose that willl help mitigatte the urban heat island. M Mitigating the e impacts of u urbanization tthrough ttargeted tree planting and canopy grow wth can be aa cost‐effectivve strategy fo or governmen nt agencies, u utilities, and tthe GSWG to improve public health ffor all Columb bus residents.. This study u utilized the p percent total impervious area by parcell to illustrate the cconcentration n of heat emiitting ssurfaces. This distribution is shown in Figure 24, rrepresenting tthe Urban He eat Island. B By integratingg zone use infformation into the Figure 24: Peercent Imperviou us by Parcel Illusstrates p parcel level daata, we can highlight priorrity planting aareas that the Urban Hea at Island m may help mitigate the urbaan heat island d broken out by rresidential, industrial, and commercial u uses. This maay help the Ciity develop a more strateggic outreach aapproach tto encourage tree plantinggs on private p property, and d guide internnal policies th hat could requ uire tree plan ntings w within new de evelopments

P Priority 2: Energy Co onservation n A Another high priority when n identifying p potential plan nting sites aree those locatiions that mayy conserve en nergy. TTrees provide e a reduction in energy use e in the summ mer by provid ing shade, an nd in the wintter by reducin ng wind. B By strategically planting tre ees in residen ntial areas wh here the urbaan tree canoppy is low, heatting and cooliing costs m may be reducced, and the aamount of energy required d to achieve ddesired homee temperaturees will also bee

R RECOMMENDA ATIONS & STR RATEGIES

3 35

rreduced. A GIS dataset hass been provided with this aassessment thhat identifiess residential p properties wh here UTC iss low (less than the Citywide average o of 22%) and w where the totaal PPA is greaater than the average of reesidential p parcels (greatter than 38%).

P Priority 3: Stormwatter Reductiion TThe City of Co olumbus is addressing stormwater issue es in a vvariety of wayys. Planting trees where fe easible within n the City is o one strategy tto help manage stormwate er by increasiing aabsorption an nd reducing ru unoff. Many aareas in the C City contain w wide arterial ccorridors and d huge parkingg lots with sp prawling impervious su urfaces. By inccorporating n new plantingss throughout p parking lots and existing sidewalks, storrmwater will be intercepted and the volum me of water re eaching storm m sewers will b be reduced. For example, tthe Target paarking lot in th he Consumer SSquare West SShopping Cen nter contains very few tree es as seen in FFigure 25. Compare this to o parking lotss shown in Figgure 26, w where trees h have been integrated throu ughout. There e is a huge o opportunity fo or the City of Columbus to o integrate such plantings into the large surface parking lots. Planners and pub blic officials sshould work w with big box rretailers to inttegrate plantings into ssurface parkin ng lots, as well as work internally to add d plantings Figure 25: Tarrget Parking Lot Lacking Trees inn Columbus, Ohioo gle Maps, 2015)) (Source: Goog tto existing sid dewalks within n Rights‐of‐W Way.

Figure 26: Parkin F ng Lots with Treee Plantings Left: Trees in a P L Parking Lot in On nondaga County,, NY (Source: Savve the Rain webbsite http://savettherain.us/) Riight: Trees in a p parking lot in Arvvada, Colorado ((Source: Google Maps, 2015)

R RECOMMENDA ATIONS & STR RATEGIES

3 36

P Priority 4: Water Qua ality TTrees and veggetation locatted adjacent tto water reso ources providee a number o of significant b benefits to th he natural eenvironment. By focusing n new tree plan ntings in the aareas immediiately surroun nding rivers aand streams (riparian aareas) and oth her waterbod dies, the City ccan improve water qualityy, help stabilizze river bankss and preventt erosion, aand promote valuable habitats. As part of this assesssment, ripariaan zones werre mapped byy buffering alll surface w water by 100 feet, and posssible plantingg areas were determined w within those zones. The to otal PPA within the C City’s riparian n zones were tthen aggregated to the Plaanning Area aand Census Block Groups aand included in the C Canopy Planner application n tool. Throu ugh this tool, the City will bbe able to ideentify where tthe greatest p planting o opportunities are for impro oving water q quality.

P Priority 5: Socio‐Dem mographic Relationsh hips and SSocial Equitty TThis study ide entified a num mber of relatio onships betw ween socio‐deemographics aand urban treee canopy. Neew p plantings shou uld be targete ed in areas w with low incom me and low hoome values to o improve thee landscape aand increase the e ecosystem be enefits in the poorer areas of Columbuss. Figures 27 aand 28 show Census Blockk Groups w with below median income e and below m median home e values, as w well as less thaan the citywid de average UTC of 2 22%. The Census data was obtained from the Americcan Communnity Survey 5‐yyear estimatees from 2009‐2013. P Planning Areaa boundaries are overlaid ffor reference.

Figure 27: Censsus Block Groupss Below Median Income and lesss than Aveerage UTC

Figure 288: Census Block G Groups Below Median Home Vallues and less than Average U UTC

R RECOMMENDA ATIONS & STR RATEGIES

3 37

P Priority 6: Improve C Canopy Con nnectivity A Another priorrity for the Citty of Columbu us is to increaase connectivvity among the existing treee canopy. Plaanting n new trees adjacent to large e tracts of existing canopyy will help exppand the greaater canopy and fill in gapss over ttime. This is im mportant for promoting w wildlife migrattion and wildl ife diversity ssupported byy canopy conn nectivity. TTo guide this e effort, canopy areas greater than or eq qual to 5 acress were identified and bufffered by 100 ffeet. The P PPA within these buffer zo ones were the en summarize ed by Planningg Areas and C Census Block Groups and included in the Canopyy Planner application tool.

C Combined Priority Pllanting Areeas by Parrcel P Priority theme es were integgrated into paarcel level datta to identify where to tarrget new plan ntings based o on the ggreater citywiide goals. Eacch parcel was given a total score depen ding on how many themees it addressed d, and aalso given a w weighted score consideringg the priority level. While tthe two resulting maps aree quite similaar, the w weighted valu ues draw morre attention to the downto own area wheere the urban n heat island iis more intense, there aare more impervious areass causing pote ential stormw water concernns, and wheree there is a grreater numbeer of h households eaarning less th han the mediaan income. A GIS dataset w was provided to the City allowing them m to drill d down to specific properties and to adjust the theme weighting ass priorities change.

Fiigure 29: Priorityy Parcels Total SScore

Figure 30: PPriority Parcels W Weighted Score

R RECOMMENDA ATIONS & STR RATEGIES

3 38

Recommendation 4: Develop an Urban Forest Management Plan The City of Columbus Recreation and Parks Department should work with other city departments, partner agencies, and stakeholders to develop an official urban forest management plan to provide a shared vision and guide policy. The information presented in this report can be used to establish canopy cover goals for the short and long term, while an arborist, city forester, or urban forester will be needed to increase inspection, maintenance, and enforcement. Tree‐related policies and ordinances should also be established to achieve management planning objectives and canopy goals.

Recommendation 5: Engage the Community Community engagement is an important component of achieving planting goals and gaining support for new policies. Engage the community through continuing tree planting events targeting areas with low UTC and high PPA. Continue outreach to residents by expanding educational trainings and workshops and promoting this project’s associated public web mapping applications. The City should also foster partnerships with local businesses, big box stores such as Target, community groups, vendors, the tree service industry, and Home Owners Associations.

Recommendation 6: Utilize this Assessment and Associated Tools The results of this assessment can and should be used to encourage investment in forest monitoring, maintenance, and management, and to develop targeted presentations for city leaders, planners, engineers, resource managers, and the public on the functional benefits of trees in addressing environmental issues. The land cover data should be disseminated to diverse partners for urban forestry and other applications while the data is current and most useful for decision‐making and implementation planning. The City should also utilize the additional tools provided, including the Canopy Calculator and the Canopy Planner web application to inform decisions and engage stakeholders. Canopy cover should be re‐assessed in no less than 10‐year intervals, and use LiDAR data if available, aiming for 95% minimum overall accuracy.

RECOMMENDATIONS & STRATEGIES

APPEN NDIX A Accuraccy Assessmentt C Classification accuracy servves two main purposes: First, accuracy assessmentss provide information to teechnicians p producing the e classification n about wherre processes n need to be im mproved and where they aare effective. Secondly, m measures of aaccuracy provvide information about how w to use the classification and how well land cover classes aare expected to estimate aactual land co over on the grround. Even w with high reso olution imageery, very smalll d differences in classification n methodologgy and image quality can hhave a large im mpact on oveerall map areaa eestimations. TThe classificattion accuracyy error matrix illustrated inn Table 18 con ntains confideence intervalss that rreport the higgh and low values that cou uld be expecte ed for any co mparison bettween the claassification daata and w what actual, o on the ground d land cover w was in 2013. TThe internal aaccuracy assessment was ccompleted in five (5) stepss 1. One thousand (1,0 000) sample p points were raandomly distrributed acrosss the study area and assiggned a rando om numeric vvalue. 2. Sortin ng from lowesst random value to highest, each samp le point was rreferenced ussing the NAIP P imagery and assigned one o of the five lan nd cover classses (“Ref_ID”)) mentioned above. 3. In the e event that the reference value could n not be discernned from thee imagery, thee point was dropped from the accuracy analysis. 4. An au utomated script was then u used to assign n values from m the classificaation raster tto each point (“Eval_ID”). The claassification su upervisor pro ovides unbiaseed feedback tto quality con ntrol technicians ding the type es of correctio ons required. Misclassifiedd points (where reference ID does not eequal regard evaluation ID) and correspondin ng land coverr are inspecteed for necessaary correction ns to the land d cover1. A Accuracy is re e‐evaluated (rrepeat steps 3 3 & 4) until an n acceptable classification accuracy is aachieved.

SSample Errror Matrix x Interpreta ation SStatistical relaationships bettween the refference pixels (representinng the true co onditions on the ground) aand the intersecting classified pixels are used to o understand how closely tthe entire claassified map rrepresents the C Columbus, OH H landscape. TThe sample e error matrix (TTable 18) rep resents the in ntersection o of reference p pixels m manually iden ntified by a hu uman observe er (columns) and classificaation categoryy of pixels in the classified d image (rows). The white boxes along the diago onals of the m matrix represeent agreemen nt between th he two pixel maps. O Off‐diagonal vvalues represent the numb ber pixels manually refere nced to the ccolumn class tthat were classified as aanother categgory in the claassification im mage. Overall accuracy is ccomputed by dividing the ttotal numberr of ccorrect pixels by the total n number of pixxels reported d in the matrixx (For municipal areas: 261+311+342+1 10+28 = 9 952 / 991 = 96 6%), and the matrix can be e used to calcculate per classs accuracy p percent’s. For example, 266 points 1

Note that by ccorrecting locaations associate ed with accuraacy points, biass is introduced to the error m matrix results. TThis m means that matrix results bassed on a new sset of randomlyy collected acccuracy points m may result in siignificantly diffferent aaccuracy valuess. A APPENDIX

4 40

were manually identified in the reference map as Tree Canopy, and 261 of those pixels were classified as Tree Canopy in the classification map. This relationship is called the “Producer’s Accuracy” and is calculated by dividing the agreement pixel total (diagonal) by the reference pixel total (column total). Therefore, the Producer’s Accuracy for Tree Canopy is calculated as: (261/266 = .98), meaning that we can expect that ~98% of all tree canopy in the Columbus, OH study area were classified as Tree Canopy in the classification map. Table 18: Sample error matrix for land cover classification within municipal areas in Columbus, OH

Reference Data

Classification Data

Tree Canopy Impervious Tree Canopy Impervious Vegetation Soil/Dry Veg. Water Total

261 3 2 0 0 266

6 311 13 4 0 334

Overall Accuracy = Producer's Accuracy Tree Canopy Impervious Vegetation Soil/Dry Veg. Water

Soil / Dry Veg.

Vegetation 5 13 342 1 0 361

0 1 0 10 0 11

Total Reference Pixels 0 272 0 328 0 357 1 16 28 28 29 1001

95% User's Accuracy Tree Canopy Impervious Vegetation Soil/Dry Veg. Water

96% 95% 96% 63% 100%

Water

96% 95% 96% 63%

Conversely, the “User’s Accuracy” is calculated by dividing the number agreement pixel total by the total number of classified pixels in the row category. For example, 261 classification pixels intersecting reference pixels were classified as Tree Canopy, but 5 pixels were identified as Vegetation and 6 pixels were identified as Impervious in the reference map. Therefore, the User’s Accuracy for Tree Canopy is calculated as: (261/272 = 0.96), meaning that pixels classified as Tree Canopy in the classification were actual tree canopy in Columbus, OH. It is important to recognize the Producer’s and User’s accuracy percent values are based on a sample of the true ground cover, represented by the reference pixels at each sample point.

Results Interpretation of the sample error matrix results indicates this land cover is accurately mapped in Columbus, OH. The most reliable classes are water and canopy cover. The largest source of classification confusion exists between impervious surfaces, vegetation, and soil / dry vegetation. This confusion is largely the result of human interpretation in that the interpreter must determine when a gravel parking lot should be considered pervious or impervious, or whether existing vegetation is living (green) or dry (brown).

APPENDIX

A Associaated Too ols P Plan‐It Geo’s SStudy Landingg Page, Canopy Calculatorr, and Canopyy Planner Too ols provide additional valuee to this u urban canopyy assessment. These tools allow the gen neral public aand various sttakeholders tto not only vieew, but aalso interact w with the tree canopy data that has been n developed.

P Page and C Content M Managemen nt System TThe web toolss provided as part of this aassessment haave been linkked to through a landing page advertising the U UTC assessme ent results, baasic informatiion, photos, aand tool linkss. Visit the pagge at Columb busCanopy.co om

C Canopy Ca alculator TThe Canopy C Calculator is a tool that allo ows users to input canopy goals and asssociated timeeframes in ord der to q quantify how many new trrees are needed to reach those goals. TThe Calculatorr enables a usser to adjust tthe aaverage tree ccrown diametter, distributiion of tree sizzes, and the im mpact of treee planting, forrest regeneraation, d development,, and tree mo ortality. It can n be used to d determine (1) how many trrees are requ uired to reach h a p particular UTC C goal or (2) w what impact o on UTC will planting new ttrees have (bo oth operate ccitywide and b by land u use classes, fo orest manage ement zones, etc.). Plan‐It Geo has custtomized the C Canopy Calcullator tool for C Columbus witth current UTC and PPA me etrics accordiing to Zone U Use Classes. Itt should be ussed long‐term m to rreassess goalss as condition ns change ove er time and th he City/GSWG G need to adaapt.

Figure 31: The Canopy Calculattor Tool Populateed with Columbus Metrics

C Canopy Pla anner C Canopy Planner provides aan easily accessible web‐viewer for visuualizing and utilizing UTC d data results. TThe core C Canopy Planner tool includ des the follow wing functionaality: ■ ■ ■

Planner Dashboard d: Quickly view City‐Wide key findings tthrough the PPlanner Dashb board. Canop py Viewer: Qu uery data results by analyssis area to ga in a deeper u understandingg of key findin ngs. Mapp ping: Drill dow wn into resultts to view and d print custom m maps to faccilitate forest managemen nt planning. ■ Calculator: A web‐version of ou ur Canopy Calculator tool aallows users tto estimate trree planting aand/or tree ccanopy as the ey relate to tree canopy go oals. TThe UTC data created in th his study, i‐Tre ee benefit values, Potentiaal UTC, and p priority plantin ng areas havee been u used as inputss to Canopy P Planner, and p provide a sum mmary interfaace of ongoing canopy covver trends in aa web d dashboard. Th hese tools are e accessible, vvisual, and eaasy to use by multiple userrs, with no so oftware to insstall. They p provide easy w ways to make e custom map ps of canopy ccover and moore. A APPENDIX

Figure 32: Image off the Canopy Pla anner Web Appliication, ColumbuusCanopy.com

A APPENDIX

Comprehensive Assessment Area Results This Urban Tree Canopy (UTC) Assessment of Columbus, OH was conducted by Plan‐It Geo, LLC for the City of Columbus. This assessment examined existing urban tree canopy (UTC) in Columbus across several geographic boundaries to assist in developing an urban forest management plan. This section provides the complete results for each target geography, as well as maps showing the distribution of the results of UTC, Total PPA, PPA‐ Vegetation, and PPA‐Impervious. Analysis results are as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Planning Areas Forestry Management Zones Rights‐of‐Way within Forestry Management Zones Parks and Parkland Watersheds BluePrint Columbus Project Areas Neighborhoods Census Block Groups and Socio‐Demographics Emerald Ash Borer Tree Canopy Estimates Asian Long‐horned Beetle Tree Canopy Estimates Ecosystem Services Results

APPENDIX

City off Columbu us Geograaphic Sum mmary: Plaanning Areeas Percent U Urban Tree Canopy

Percent Possible Plan nting Areas (Total)

Pe ercent Possib ble Planting A Areas (Vege etation)

Percent Possible Plan nting Areas (Imperviouss)

Figure 33: Planni F ng Area Assessm ment Results

APPENDIX

City of Columbus Geographic Summary: Planning Areas This table summarizes the UTC metrics by Columbus, OH Planning Areas in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable) Table 19: Planning Area Assessment Results Plan Area ID

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Plan Area Name

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

Hayden Run Far Northwest Far North Rocky Fork‐ Blacklick Northwest Northland Far West West Scioto West Olentangy Clintonville North Linden Northeast Near North/ University South Linden Hilltop Franklinton Greenlawn/ Frank Road Downtown Near East Eastmoor/ Walnut Ridge Far East Near South Far South Eastland/Brice Westland Rickenbacker Southeast

2,360

327

14%

557

24%

283

12%

840

36%

1,320

53%

4,405

1,332

30%

1,352

31%

374

1,726

39%

1,379

31%

5,264

1,079

20%

1,491

28%

1,000

19%

2,491

47%

1,762

33%

6,923

2,099

30%

2,399

35%

580

2,979

43%

1,973

28%

5,603

1,301

23%

1,363

24%

726

13%

2,088

37%

2,407

42%

8,322

2,093

25%

2,603

31%

1,245

15%

3,847

46%

2,446

29%

4,542

468

10%

1,403

31%

749

16%

2,152

47%

2,008

44%

4,575

1,182

26%

1,322

29%

502

11%

1,824

40%

2,015

40%

4,432

1,001

23%

1,168

26%

751

17%

1,918

43%

1,760

38%

3,880

1,600

41%

990

26%

255

1,245

32%

1,074

27%

5,867

1,880

32%

1,835

31%

503

2,338

40%

1,670

28%

7,237

1,277

18%

2,189

30%

1,110

15%

3,299

46%

2,911

40%

2,607

515

20%

402

15%

414

16%

816

31%

1,318

50%

4,507

959

21%

1,400

31%

558

12%

1,958

43%

1,601

35%

7,807

1,777

23%

2,274

29%

949

12%

3,223

41%

2,830

36%

1,787

292

16%

471

26%

252

14%

723

40%

900

47%

3,741

713

19%

1,366

37%

330

1,696

45%

1,536

39%

1,497

108

157

10%

361

24%

518

35%

929

60%

2,310

573

25%

538

23%

211

749

32%

998

43%

6,235

2,079

33%

1,888

30%

436

2,324

37%

1,884

30%

4,449

1,074

24%

1,588

36%

635

14%

2,223

50%

1,191

27%

6,003

1,118

19%

1,416

24%

686

11%

2,102

35%

2,985

48%

7,104

1,415

20%

2,600

37%

704

10%

3,304

47%

2,739

37%

9,478

2,347

25%

3,094

33%

1,153

12%

4,247

45%

3,132

32%

7,764

1,036

13%

2,329

30%

813

10%

3,142

40%

3,730

47%

6,176 4,559

1,047 450

17% 10%

3% 1%

1,595 1,741

26% 38%

361 166

6% 4%

1,956 1,907

32% 42%

4,043 2,319

57% 50%

TOTALS

139,435

31,143

22%

100%

41,533

30%

16,106

12%

57,639

41%

54,859

38%

APPENDIX

Cityy of Colum mbus Geoggraphic Su ummary: FForestry M Managem ment Zones Percent Urban Tree anopy Ca

Percent Possiblee Planting Arreas (Total)

Possible Planting Areass (Vege etation)

Posssible Plantingg Areas (Impervious)

Figure 3 34: Forestry Man nagement Zone A Assessment Resuults

APPENDIX

City of Columbus Geographic Summary: Forestry Management Zones This table summarizes the UTC metrics by Forestry Management Zones in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 20: Forestry Management Zone Assessment Results Forestry Mgmt. Zone

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

2,360

327

14%

557

24%

283

12%

840

36%

1,320

53%

02A

976

284

29%

343

35%

408

42%

290

30%

02B

1,073

373

35%

320

30%

350

33%

356

33%

02C

1,258

279

22%

354

28%

241

19%

595

47%

397

31%

02D

1,098

396

36%

335

31%

374

34%

336

30%

03A

2,073

219

11%

607

29%

504

24%

1,111

54%

780

37%

03B

3,191

860

27%

884

28%

497

16%

1,381

43%

982

31%

04A

2,782

703

25%

941

34%

254

1,195

43%

927

33%

04B

4,142

1,396

34%

1,459

35%

325

1,784

43%

1,047

25%

05A

1,102

278

25%

280

25%

154

14%

434

39%

451

39%

05B

2,306

329

14%

485

21%

253

11%

738

32%

1,307

56%

05C

865

375

43%

243

28%

295

34%

246

27%

05D

1,329

318

24%

354

27%

267

20%

622

47%

403

30%

06A

2,348

492

21%

733

31%

441

19%

1,174

50%

696

29%

06B

1,112

319

29%

319

29%

202

18%

521

47%

280

25%

06C

3,008

708

24%

907

30%

418

14%

1,324

44%

1,003

33%

06D

1,854

574

31%

644

35%

184

10%

828

45%

467

25%

07A

1,624

170

10%

590

36%

219

13%

808

50%

665

40%

07B

2,919

298

10%

814

28%

530

18%

1,344

46%

1,343

45%

08A

915

253

28%

258

28%

110

12%

367

40%

491

44%

08B

1,935

449

23%

545

28%

192

10%

738

38%

828

41%

08C

1,725

480

28%

519

30%

200

12%

719

42%

696

37%

09A

1,057

356

34%

299

28%

108

10%

406

38%

306

29%

09B

1,465

328

22%

423

29%

223

15%

647

44%

520

35%

09C

1,910

317

17%

446

23%

420

22%

865

45%

934

44%

10A

884

323

37%

250

28%

109

12%

359

41%

207

23%

10B

841

339

40%

237

28%

269

32%

239

28%

10C

1,364

616

45%

326

24%

404

30%

361

26%

10D

790

322

41%

176

22%

213

27%

268

33%

11A

3,383

1,045

31%

1,017

30%

376

11%

1,393

41%

959

28%

11B

2,484

835

34%

818

33%

127

945

38%

711

29%

12A

3,763

912

24%

1,428

38%

581

15%

2,009

53%

893

23%

12B

3,474

366

11%

762

22%

529

15%

1,290

37%

2,018

58%

13A

538

163

30%

15%

121

23%

263

48%

13B

1,262

209

17%

181

14%

262

21%

443

35%

637

50%

13C

807

143

18%

141

17%

111

14%

252

31%

418

51%

14A

2,476

388

16%

649

26%

379

15%

1,028

42%

1,064

43%

14B

2,031

571

28%

751

37%

180

930

46%

536

26%

15A

2,379

375

16%

592

25%

560

24%

1,152

48%

858

36%

15B 15C 15D 15E

1,339 1,907 1,024 1,158

341 571 232 258

25% 30% 23% 22%

1% 2% 1% 1%

428 508 346 401

32% 27% 34% 35%

110 170 66 43

8% 9% 6% 4%

538 679 412 443

40% 36% 40% 38%

462 663 381 467

34% 35% 37% 40%

APPENDIX

Forestry Mgmt. Zone

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

16A 16B 17A 17B 18A 18B 19A 19B 20A 20B 20C 20D 21A 21B 21C 22A 22B 22C 22D 22E 22F 23A 23B 23C 24A 24B 24C 25A 25B 26A 26B 26C 27

729 1,059 1,331 2,410 1,017 480 1,191 1,118 2,227 980 1,686 1,341 2,320 1,312 817 983 862 1,112 872 844 1,329 1,866 1,570 3,669 3,610 3,561 2,306 2,055 5,709 4,805 924 446 4,559

125 167 236 478 73 35 288 284 863 304 436 476 464 395 215 172 170 274 146 127 229 356 272 787 901 1,031 414 284 752 850 83 114 450

17% 16% 18% 20% 7% 7% 24% 25% 39% 31% 26% 35% 20% 30% 26% 17% 20% 25% 17% 15% 17% 19% 17% 21% 25% 29% 18% 14% 13% 18% 9% 26% 10%

0% 1% 1% 2% 0% 0% 1% 1% 3% 1% 1% 2% 1% 1% 1% 1% 1% 1% 0% 0% 1% 1% 1% 3% 3% 3% 1% 1% 2% 3% 0% 0% 1%

218 253 509 857 118 39 280 259 661 316 539 372 910 457 221 203 188 302 144 215 363 656 614 1,330 1,190 1,160 743 698 1,631 1,208 338 49 1,741

30% 24% 38% 36% 12% 8% 23% 23% 30% 32% 32% 28% 39% 35% 27% 21% 22% 27% 17% 26% 27% 35% 39% 36% 33% 33% 32% 34% 29% 25% 37% 11% 38%

95 157 175 155 240 121 121 90 164 53 136 83 378 135 122 106 44 117 101 128 189 104 209 391 481 420 252 283 530 129 195 37 166

13% 15% 13% 6% 24% 25% 10% 8% 7% 5% 8% 6% 16% 10% 15% 11% 5% 11% 12% 15% 14% 6% 13% 11% 13% 12% 11% 14% 9% 3% 21% 8% 4%

313 410 684 1,012 359 160 401 349 824 370 675 455 1,288 592 343 309 233 419 245 344 552 760 823 1,721 1,671 1,581 996 982 2,161 1,337 533 87 1,907

43% 39% 51% 42% 35% 33% 34% 31% 37% 38% 40% 34% 56% 45% 42% 31% 27% 38% 28% 41% 42% 41% 52% 47% 46% 44% 43% 48% 38% 28% 58% 19% 42%

Unsuit able UTC (acres) 346 554 455 1,080 615 314 508 490 562 316 576 430 590 339 262 562 461 428 600 375 559 825 716 1,198 1,109 1,093 930 835 2,895 3,469 324 250 2,319

Total

139,435

31,143

22%

100%

41,533

30%

16,106

12%

57,639

41%

54,859

Unsuit able UTC % 44% 49% 33% 42% 59% 62% 43% 44% 25% 32% 34% 32% 25% 26% 32% 54% 53% 38% 61% 44% 42% 43% 40% 32% 30% 30% 40% 40% 50% 61% 34% 56% 50% 38%

Forestry Management Zone Assessment Results – Continued from previous page

APPENDIX

City o of Columbus Geograph hic Summary: ROW w within Foresstry Manageement Zones Percent U Urban Tree Can nopy

Percent Possible Planting Areeas (Total)

Possible Planting Areas (Vege etation)

Possiible Planting Areas (Imperviouss)

Figure 35: ROW W within Forestrry Management Zone Assessmennt Results

A APPENDIX

City of Columbus Geographic Summary: ROW within Forestry Management Zones This table summarizes the UTC metrics for Rights‐of‐Way within Forestry Management Zones in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 21: ROW within Forestry Management Zone Assessment Results Forestry Mgmt. Zone

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

285

13%

20%

0.28

0.10%

20%

283

75%

02A

168

21%

24%

0.03

0.02%

24%

55%

02B

142

19%

24%

0.00

0.00%

24%

57%

02C

237

14%

28%

0.02

0.01%

28%

137

58%

02D

234

24%

0.00

0.00%

24%

122

52%

03A

248

23%

0.09

0.04%

23%

186

75%

03B

471

12%

125

27%

0.11

0.02%

125

27%

290

61%

04A

333

116

35%

0.01

0.00%

116

35%

188

56%

04B

524

12%

181

34%

0.01

0.00%

181

34%

285

54%

05A

178

15%

0.00

0.00%

15%

184

78%

05B

127

14%

25%

0.00

0.00%

25%

61%

05C

123

28%

22%

0.06

0.05%

22%

51%

05D

265

30%

23%

0.01

0.00%

23%

136

49%

06A

452

15%

133

29%

0.00

0.00%

133

29%

252

56%

06B

136

18%

28%

0.05

0.04%

28%

54%

06C

558

16%

131

24%

0.07

0.01%

131

24%

336

60%

06D

309

11%

30%

0.12

0.04%

30%

182

59%

07A

233

15%

0.14

0.06%

15%

188

81%

07B

388

22%

0.05

0.01%

22%

288

74%

08A

130

24%

16%

0.07

0.05%

16%

262

83%

08B

208

20%

0.00

0.00%

20%

161

66%

08C

264

18%

23%

0.00

0.00%

23%

177

63%

09A

233

30%

22%

0.07

0.03%

22%

120

50%

09B

221

21%

23%

0.00

0.00%

23%

128

57%

09C

454

18%

111

24%

1.26

0.28%

112

25%

331

63%

10A

118

32%

23%

0.05

0.04%

23%

46%

10B

191

38%

21%

0.00

0.00%

21%

42%

10C

263

123

47%

18%

0.02

0.01%

18%

100

37%

10D

185

35%

19%

0.04

0.02%

19%

47%

11A

488

18%

117

24%

0.04

0.01%

117

24%

285

58%

11B

527

110

21%

130

25%

0.06

0.01%

130

25%

287

54%

12A

490

10%

146

30%

0.03

0.01%

146

30%

295

60%

12B

276

14%

35%

3.31

1.20%

100

36%

144

51%

13A

150

22%

14%

0.00

0.00%

14%

64%

13B

241

21%

0.06

0.02%

172

71%

13C

227

18%

10%

0.18

0.08%

10%

167

73%

14A

515

13%

102

20%

1.07

0.21%

104

20%

343

66%

14B

348

20%

28%

0.09

0.03%

28%

180

52%

15A

353

17%

22%

0.22

0.06%

22%

215

61%

15B 15C 15D 15E

290 308 230 169

58 32 42 13

20% 10% 18% 8%

2% 1% 1% 0%

78 58 65 39

27% 19% 28% 23%

0.00 0.12 0.01 0.00

0.00% 0.04% 0.00% 0.00%

78 58 65 39

27% 19% 28% 23%

154 219 123 118

53% 71% 53% 69%

APPENDIX

Forestry Mgmt. Zone

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

16A 16B 17A 17B 18A 18B 19A 19B 20A 20B 20C 20D 21A 21B 21C 22A 22B 22C 22D 22E 22F 23A 23B 23C 24A 24B 24C 25A 25B 26A 26B 26C 27

211 361 156 184 331 178 331 357 420 234 350 307 303 228 195 252 261 250 168 138 165 210 178 394 680 516 342 287 526 168 77 6 269

33 57 28 34 21 13 52 66 120 52 66 66 21 26 30 46 32 35 28 16 19 38 21 48 105 67 18 26 32 39 15 6 8

16% 16% 18% 18% 6% 7% 16% 18% 29% 22% 19% 22% 7% 11% 16% 18% 12% 14% 16% 12% 12% 18% 12% 12% 15% 13% 5% 9% 6% 24% 19% 93% 3%

1% 2% 1% 1% 1% 0% 2% 2% 4% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 0% 1% 1% 1% 1% 3% 2% 1% 1% 1% 1% 0% 0% 0%

77 68 38 44 44 14 58 55 124 64 76 70 100 74 57 24 35 62 22 30 51 48 43 96 249 127 68 78 117 49 32 0 63

37% 19% 24% 24% 13% 8% 18% 15% 30% 27% 22% 23% 33% 32% 29% 10% 13% 25% 13% 22% 31% 23% 24% 25% 37% 25% 20% 27% 22% 29% 41% 7% 23%

0.04 0.02 0.02 0.03 1.83 2.02 0.03 0.03 0.20 0.00 0.07 0.01 0.05 1.23 0.00 1.33 0.06 0.02 0.03 0.93 0.26 0.02 0.00 0.09 0.02 0.05 0.00 0.00 0.00 0.11 0.01 0.00 0.00

0.02% 0.01% 0.01% 0.02% 0.55% 1.13% 0.01% 0.01% 0.05% 0.00% 0.02% 0.00% 0.02% 0.54% 0.00% 0.53% 0.02% 0.01% 0.02% 0.67% 0.16% 0.01% 0.00% 0.02% 0.00% 0.01% 0.00% 0.00% 0.00% 0.07% 0.01% 0.00% 0.00%

77 68 38 44 46 16 58 55 124 64 76 70 100 75 57 26 35 62 22 31 51 48 43 97 249 127 68 78 117 49 32 0 63

37% 19% 24% 24% 14% 9% 18% 15% 30% 27% 22% 23% 33% 33% 29% 10% 13% 25% 13% 22% 31% 23% 24% 25% 37% 25% 20% 27% 22% 29% 41% 7% 23%

Unsuit able UTC (acres) 155 305 120 150 289 179 221 237 177 118 208 172 182 128 108 236 194 153 148 91 95 150 127 250 332 325 257 184 378 116 30 5 199

Total

21,328

3,395

16%

100%

5,100

24%

0.08%

5,116

24%

13,762

Unsuit able UTC % 58% 71% 64% 66% 81% 86% 67% 66% 42% 50% 59% 56% 60% 56% 55% 77% 74% 61% 75% 66% 58% 63% 66% 63% 48% 63% 75% 64% 72% 57% 40% 42% 74% 62%

ROW within Forestry Management Zone Assessment Results – Continued from previous page

APPENDIX

City of Co C lumbus G Geograph hic Summ mary: Parkks and Paarkland Percent U Urban Tree Canopy

Percent Possible Plan nting Areas (Total)

Pe ercent Possib ble Planting A Areas (Vege etation)

nting Areas Percent Possible Plan (Imperviouss)

Figu ure 36: Parks and d Parkland Assesssment Results

APPENDIX

City of Columbus Geographic Summary: Parks and Parkland This table summarizes the UTC metrics by Parks and Parkland in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 22: Parks and Parkland Assessment Results Park or Parkland

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Abbie Trails Parkland

68%

31%

0.0

31%

Academy Park

32%

30%

0.9

36%

34%

Airport Golf Course

128

22%

25%

26%

54%

Unsuit able UTC %

Albany Crossing Park

0.04

96%

0.3

99%

11%

Albany Crossing Wetland

94%

0.4

0.0

0.4

0.0

Albany Wetlands

96%

0.3

0.0

0.3

0.0

Alexander/AEP Park

0.4

62%

0.2

30%

0.0

0.2

30%

0.1

15%

Alkire Lakes Park

40%

59%

0.0

59%

42%

Alkire Woods Parkland Alum Creek Drive Clean Ohio Parkland Alum Creek Parkland Alum Creek Parkland COH Alum Creek/ Hanford Parkland Alum Creek/ Holtzman Parkland Alum Creek/ Koch Parkland Alum Creek/ Livingston Parkland Alum Creek/ Miller Parkland Alum Creek/Refugee Rd Parkland Alum Creek/Smith Farms Westbank Alum Crest Park

23%

54%

0.0

54%

23%

95%

0.0

65%

35%

0.0

35%

0.8

97%

0.6

0.0

0.6

0.5

100%

0.0

67%

0.6

27%

0.0

0.6

28%

0.1

87%

0.9

13%

0.0

0.9

13%

0.1

100%

0.0

0.2

98%

0.3

0.0

0.3

0.1

48%

0.9

44%

0.1

52%

0.3

13%

78%

0.9

22%

0.0

0.9

22%

0.0

30%

59%

0.5

10%

69%

0.0

American Addition Park

0.2

92%

0.1

93%

0.2

Amvet Park Anderson Farms Parkland Anheuser‐Busch Sports Park Antrim Park

0.8

33%

67%

0.0

67%

0.0

13%

84%

0.0

84%

0.7

31%

34%

10%

45%

24%

70%

19%

25%

40%

Argus Park

0.8

45%

54%

0.0

54%

0.0

Audubon Park

22%

75%

0.2

77%

0.0

Avalon Park

0.7

40%

58%

0.0

58%

0.0

Barnett Park

19%

54%

17%

72%

Battelle Riverfront Park

47%

0.8

24%

0.3

33%

32%

Beatty Park

0.1

43%

0.7

25%

68%

0.9

30%

Beechcroft Park

0.1

96%

0.0

97%

0.1

Beechwold Park

97%

0.3

0.0

0.3

Berliner Sports Park

229

26%

36%

41%

33%

Berwick Park Bicentennial/ Galbreath Park

26%

74%

0.0

74%

0.0

0.6

14%

39%

26%

65%

0.9

21%

APPENDIX

Park or Parkland

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

Big Run Park

272

231

85%

12%

13%

Big Walnut ‐ Noe Bixby Big Walnut ‐ Ottawa Parkland Big Walnut Park Big Walnut South Corridor Coh Big Walnut‐Williams Rd Parkland Coh Blackburn Park

49%

51%

0.0

51%

0.1

98%

0.0

0.4

29%

152

106

70%

25%

28%

10%

97%

0.6

0.0

0.6

99%

0.1

0.0

0.1

0.3

21%

26%

23%

49%

31%

Brandywine Park

0.3

54%

0.3

46%

0.0

0.3

46%

0.0

Brentnell Ave Parkland

96%

0.0

Brentnell Park

0.00

0.8

35%

45%

80%

0.5

20%

Brevoort Park

49%

46%

0.1

50%

0.0

Broad Street Parkland

0.2

0.1

0.0

0.1

99%

Brookside Woods Park

0.6

30%

66%

0.1

70%

0.0

Brownlee Circle Park

0.9

54%

0.8

46%

0.0

0.8

46%

0.0

Canini Park Cannongate Alum Creek Parkland Carriage Place Park

81%

0.3

19%

0.0

0.3

19%

0.0

99%

0.0

0.3

16%

68%

10%

78%

0.7

Cassady Park

94%

0.2

0.0

0.2

0.0

Casto Park

73%

18%

20%

10% 0%

Catalpa Park

100%

0.0

Cedar Run Park

29%

70%

0.0

70%

0.1

Champions Golf Course

206

111

54%

20%

23%

24%

Chaseland Park

86%

0.5

13%

0.0

0.5

13%

0.0

Chatterton Parkland

29%

69%

0.0

69%

Cherrybottom Park COH

98%

0.0

10%

City Gate Park Clean Ohio Alton Parkland Clinton‐Como Park Clover Groff Natural Area Clover Parkland

93%

0.4

0.0

0.4

0.0

67%

0.3

0.0

0.3

33%

46%

39%

0.5

42%

17%

25%

75%

0.0

75%

17%

81%

0.0

81%

30%

Clover‐Kenney Park

15%

82%

0.0

82%

0.6

Coaaa Property

0.01

0.0

0.7

76%

0.7

76%

0.2

23%

Cody Park Columbus Performing Arts Center Connor Park

0.2

71%

0.1

29%

0.0

0.1

29%

0.0

0.04

0.0

0.1

25%

0.1

25%

0.4

68%

78%

22%

0.0

22%

24%

Cooke Park

16%

54%

23%

77%

0.7

Cooper Park

34%

47%

0.8

49%

18%

Cosi Parking Lot

0.4

0.2

67%

70%

21%

Crawford Farms Park

0.3

92%

0.0

92%

0.0

Creek Ridge Parkland

96%

0.4

0.0

0.4

Creekstone Parkland

66%

34%

0.0

34%

0.0

Creekview Parkland

88%

12%

0.0

12%

0.1

Cremeans Park

0.7

11%

71%

0.8

12%

83%

0.4

Cultural Arts Center Cumberland Woods Parkland

0.1

13%

0.1

10%

0.1

19%

0.5

67%

100%

0.0

Parks and Parkland Assessment Results – Continued from previous page APPENDIX

Park or Parkland

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

Davis Property

29%

0.9

0.0

0.9

69%

Deaf School Park

38%

60%

0.0

60%

0.3

Deer Lake Parkland Deffet Rentals Watercourse Ded Deshler Park

93%

0.1

0.0

0.1

0.0

99%

0.1

0.0

0.1

0.5

12%

82%

0.5

87%

0.0

Devonshire Park

27%

67%

0.4

73%

0.0

Dexter Falls Park

19%

77%

0.1

78%

0.2

Dodge Park

0.8

43%

22%

65%

32%

Dream‐Filled Park

0.3

36%

0.4

44%

55%

Driving Park

36%

41%

48%

16%

Dry Run Levee Gate

0.1

12%

0.9

84%

0.0

0.9

84%

0.1

11%

Duranceau Park Dysart Run And E Broad Parkland Eastern Glen Parkland

68%

24%

0.9

30%

0.9

99%

0.1

0.0

0.1

0.0

95%

0.4

0.0

0.4

0.2

Easthaven Park

44%

55%

0.0

55%

0.0

Elk Run Park Elk Run/ Winchester Pike Parkland English Park

68%

31%

0.4

31%

51%

49%

0.0

49%

0.2

30%

0.3

54%

0.0

0.3

54%

0.1

16%

Fairwood Park

59%

35%

39%

0.4

Fisher Road Parkland

38%

51%

0.0

51%

11%

Flint Park

39%

48%

0.6

10%

58%

0.2

Forest Creek Park

26%

74%

0.0

74%

0.0

Forest Park East Park Frank Fetch Memorial Park Franklin Park

31%

69%

0.0

69%

0.0

0.1

32%

0.1

42%

0.0

0.1

42%

0.1

26%

32%

53%

57%

15%

Franklinton Cemetery

0.7

41%

0.9

56%

0.0

0.9

57%

0.0

Franks Park

11%

76%

79%

26%

Freedom Park

0.4

90%

0.1

91%

0.3

Galloway Ridge Park

0.1

88%

0.2

93%

0.2

Gelpi Parkland

96%

0.1

0.0

0.1

0.0

Genoa Park Georgesville Green Parkland Georgian Heights Park

0.7

35%

0.8

38%

0.0

0.8

38%

0.7

31%

80%

20%

0.0

20%

0.2

56%

44%

0.0

44%

0.0

Glen Echo Park

89%

0.7

11%

0.0

0.7

11%

0.1

Glen View Park

81%

0.7

19%

0.0

0.7

19%

0.0

Glenwood Park

69%

20%

0.9

25%

Golden Hobby Shop

0.1

16%

0.0

0.3

53%

0.3

55%

0.2

29%

Goodale Park

41%

51%

57%

Gould Park

0.1

42%

0.1

58%

0.0

0.1

58%

0.0

Gowdy Field

0.03

0.5

21%

48%

68%

0.7

31%

Granville Park

30%

70%

0.0

70%

0.0

Greene Countrie Park

20%

78%

0.0

78%

0.4

Greenlawn Park

56%

11%

31%

43%

Griggs Nature Preserve

81%

18%

0.0

18%

Griggs Park

160

59%

31%

36%

Hamilton & Spring Park

0.1

29%

0.1

54%

0.0

0.1

54%

0.0

17%

Parks and Parkland Assessment Results – Continued from previous page

APPENDIX

Park or Parkland Hamilton Avenue Street Park Hamilton Heights Parkland Hamilton Road Wetland Nature Preserve Hanford Village Park

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

0.2

41%

0.1

33%

0.0

0.1

33%

0.1

26%

0.2

95%

0.0

95%

0.0

99%

0.9

0.0

0.9

0.0

0.5

33%

0.8

52%

0.2

13%

65%

0.0

Hard Road Park

44%

43%

0.3

44%

12%

Harrison House

0.1

46%

0.1

27%

0.0

0.1

31%

0.1

23%

Harrison Park

32%

44%

0.4

54%

27%

Harrison Smith Park

64%

36%

0.1

36%

0.0

Harrison West Park

0.2

46%

0.2

51%

0.0

0.2

51%

0.0

Hauntz Park Hayden Falls Nature Preserve Hayden Park

27%

70%

0.1

72%

0.1

93%

0.1

0.0

0.1

0.2

66%

34%

0.0

34%

10%

Hayden Trail Parkland

0.2

96%

0.0

96%

0.4

Haydens Crossing Park

66%

30%

0.0

31%

10%

Heer Park

12%

56%

26%

82%

0.5

Helsel Park

68%

27%

31%

Hickory Woods Parkland

99%

0.0

Highbluffs Park

0.5

24%

73%

0.0

73%

37%

Hilliard Green Park Hilliard Green/ Baycroft Walkway Hilltonia Park

24%

72%

0.4

75%

0.2

0.01

33%

0.0

67%

0.0

67%

0.0

33%

12%

88%

0.0

88%

0.0

Holt Avenue Parkland

0.9

98%

0.0

Holton Park

55%

30%

0.8

40%

0.5

Huy Road Park Igel/Alum Creek Water Dedication Independence Park

0.4

42%

0.0

42%

53%

77%

0.5

14%

0.0

0.5

14%

0.7

17%

19%

65%

0.1

65%

15%

Indian Mound Park

11%

66%

10%

76%

14%

Indian Village Day Camp

45%

38%

0.7

44%

13%

Indianola Park

0.1

0.9

78%

0.0

0.9

78%

0.2

14%

Innis Park

75%

25%

0.5

25%

Italian Village Park

0.4

43%

0.4

43%

0.0

0.4

43%

0.1

13%

Iuka Park

91%

0.4

0.0

0.4

0.0

Jefferson Woods Park

0.1

96%

0.0

96%

0.0

Jefferson Woods Ravine

65%

35%

0.0

35%

0.1

Joan Park

0.8

21%

75%

0.2

79%

0.0

Karns Park

0.3

15%

70%

0.1

77%

0.2

Keller Park

0.2

40%

0.3

54%

0.0

0.3

56%

0.0

Kenlawn Park

0.8

20%

71%

0.3

78%

0.1

Kenney Park

79%

0.0

13%

Kilbourne Run Parkland

89%

11%

0.0

11%

0.0

Kirkwood Park

0.5

17%

83%

0.0

83%

0.0

Kobacker Park

0.1

35%

0.1

41%

0.1

18%

0.2

59%

0.0

Krumm Park

27%

59%

64%

11%

Lane Woods Parkland

97%

0.1

0.0

0.1

0.0

Lazelle Woods Park

35%

37%

11%

48%

17%

Parks and Parkland Assessment Results – Continued from previous page

APPENDIX

Park or Parkland

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

Lehman Estates Parkland

0.01

97%

0.0

98%

0.2

2% 11%

Lincoln Park

10%

62%

18%

80%

Lindbergh Park

28%

68%

0.1

69%

0.3

Linden Park

18%

41%

50%

39%

Linwood Park

0.02

22%

0.1

56%

0.0

0.1

56%

0.0

22%

Livingston Park

24%

56%

0.2

58%

18%

Livingston/1‐70 Parkland

90%

0.1

36%

Liv‐Moor Park Lockbourne Parkland COH Longwood Wetland Parkland Mackenbach Parkland Maintenance & Warehouse Operations Majestic Paint Parkland

0.2

19%

0.5

59%

0.2

18%

0.7

77%

0.0

85%

15%

0.0

15%

20%

77%

0.0

77%

23%

93%

0.0

93%

0.1

26%

0.6

28%

35%

39%

68%

30%

0.0

30%

0.1

Maloney Park

17%

74%

82%

0.4

Marion‐Franklin Park

13%

61%

19%

80%

Martin Park

0.00

0.2

100%

0.0

0.2

100%

0.0

Mason Run Parkland Coh

99%

0.2

0.0

0.2

0.0

Maybury Park

23%

73%

0.2

76%

0.1

Mayme Moore Park Maynard And Summit Park Mccoy Park

28%

32%

0.8

16%

48%

24%

0.1

23%

0.2

61%

0.0

0.2

61%

0.1

16%

58%

64%

29%

Mcferson Commons

0.6

20%

66%

0.3

11%

77%

0.1

Mckinley Park Mentel Memorial Golf Course M‐Five Parkland

16%

39%

0.6

47%

37%

144

18%

26%

31%

54%

50%

48%

0.0

48%

11%

Mifflin Park

0.6

13%

86%

0.1

87%

0.0

Millbrook Park

0.3

20%

0.8

50%

0.4

27%

77%

0.1

Milo‐Grogan Park

0.03

0.2

10%

0.6

33%

0.8

44%

55%

Mock Park

107

81%

17%

19%

Moeller Park

0.6

34%

60%

0.0

60%

0.1

Moler Street Parkland

67%

0.8

12%

0.0

0.8

12%

21%

Mt Vernon Plaza Park

0.2

21%

0.0

0.6

73%

0.6

74%

0.1

Nafzger Park

145

56%

43%

44%

12%

Nelson Park

63%

33%

0.8

36%

North Bank Park

18%

59%

0.0

60%

23%

North East Park

28%

34%

0.9

24%

58%

0.5

14%

Northcrest Park

23%

59%

0.7

62%

14%

Northern Woods Park

100%

0.0

Northgate Park

68%

25%

0.3

31%

0.0

Northmoor Park

83%

14%

0.2

17%

0.7

7% 0%

Northtowne Park

35%

63%

0.1

65%

0.0

Oak Creek Parkland

98%

0.1

0.0

0.1

0.0

Ohio Ave Street Park

0.4

66%

0.0

0.2

27%

Olde Sawmill Park

13%

73%

0.8

81%

0.5

Olenbrook Parkland Olentangy Meadows Parkland

96%

0.2

0.0

0.2

0.0

79%

20%

0.0

20%

0.1

Parks and Parkland Assessment Results – Continued from previous page APPENDIX

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

Olentangy Parkland Olentangy Parkland – Far North Olentangy River Canoe Portage Olentangy Trail – King To 5Th Olentangy‐ Broadmeadows Trail Overbrook Ravine Park

94%

0.2

91%

0.9

0.1

11%

0.9

93%

0.1

0.0

0.1

0.6

38%

44%

30%

0.3

31%

47%

0.1

27%

0.3

70%

0.0

0.3

70%

0.0

98%

0.2

0.0

0.2

0.0

Palsgrove Park

0.00

0.0

0.8

86%

0.9

91%

0.1

Park or Parkland

Parkridge Park

78%

22%

0.1

22%

Parkview Parkland

98%

0.1

0.0

0.1

0.0

Perhar Parkland

0.1

99%

0.0

99%

0.2

Pingue Park

0.8

16%

79%

0.1

81%

0.2

Portal Park

0.1

86%

0.0

Portman Park

39%

59%

61%

Preserve East Parkland Prestwick Commons Park Pride Park

97%

0.8

0.0

0.8

0.0

50%

49%

0.0

49%

0.8

17%

0.05

56%

0.0

33%

0.0

33%

0.0

Pump House Park

58%

38%

0.1

39%

0.1

Pumphrey Park Quarry Pointe B Parkland Quarry Pointe C Parkland Raymond Memorial Golf Course Redick Park

0.5

76%

0.2

24%

0.0

0.2

24%

0.0

68%

0.8

32%

0.0

0.8

32%

0.0

96%

0.2

0.0

0.2

0.0

213

19%

33%

36%

100

46%

76%

16%

0.4

18%

8% 11%

Retreat At Turnberry

65%

26%

0.8

31%

Reynolds Crossing Park

50%

49%

0.0

49%

0.0

Rhodes Park Richter Workers Memorial Park Rickenbacker House

24%

46%

53%

23%

0.1

37%

0.1

20%

0.1

17%

0.1

37%

0.1

27%

0.2

26%

0.3

40%

0.0

0.3

40%

0.2

32%

Rickenbacker Park

35%

18%

21%

39%

36%

Riverbend Park Riverfront Walkway/Bridge Riverside Green Park Riverside Green South Park Rocky Creek Parkland Rocky Fork Creek Parkland Roosevelt Park

0.9

19%

78%

0.1

81%

0.0

0.00

0.0

100%

77%

18%

0.3

22%

0.0

0.7

49%

0.7

51%

0.0

0.7

51%

0.0

85%

0.8

14%

0.0

0.8

14%

0.3

61%

39%

0.0

39%

0.2

2% 2%

0.9

31%

66%

0.0

67%

0.1

Rosemont Parkland

97%

0.1

0.0

0.1

0.0

Rush Run Parkland

0.03

100%

0.0

0.2

88%

Sancus Park

0.5

42%

0.7

56%

0.0

0.7

56%

0.0

Sater Park

0.02

25%

0.1

63%

0.0

0.1

63%

0.0

13% 25%

Saunders Park

58%

66%

Sawmill Road Parkland

45%

55%

0.0

55%

0.0

Sawyer Park

26%

0.9

17%

31%

48%

26%

Schiller Park

39%

49%

55%

10%

Parks and Parkland Assessment Results – Continued from previous page APPENDIX

Park or Parkland

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

Schirm Farms Parkland

12%

88%

0.0

88%

33%

Scioto Audubon Park

29%

46%

54%

20%

Scioto Canal Parkland Scioto Peninsula Parkland Scioto River Parkland

95%

0.2

0.0

0.2

0.1

0.3

68%

0.8

16%

84%

0.6

11%

95%

0.2

0.0

0.3

61%

Scioto Woods Park

30%

66%

0.0

66%

0.3

Sensenbrenner Park

0.1

18%

0.1

14%

0.2

23%

0.3

37%

0.4

46%

Shadeville Nursery

11%

80%

83%

13%

Shady Lane Park

45%

55%

0.0

55%

0.0

Sharon Meadows Park

27%

55%

0.2

57%

17%

Shelbourne Parkland

86%

13%

0.0

13%

0.1

Shepard Park

88%

0.4

11%

0.0

0.4

11%

0.0

Side By Side Park

0.1

68%

0.1

26%

0.0

0.1

26%

0.0

Sills Park

29%

37%

0.8

41%

32%

Smith Road School‐Park

20%

74%

0.3

80%

0.1

Sol Shenk Parkland

88%

12%

0.0

12%

Southeast Lions Park

0.3

26%

0.8

59%

0.1

0.9

68%

0.1

Southgate Park Southside Park/Head Start Facility Southwood Mileusnich Park Spindler Road Park

0.2

96%

0.0

96%

0.0

26%

62%

0.3

68%

0.3

0.00

0.3

22%

0.0

0.3

22%

0.9

78%

104

12%

49%

51%

41%

St Clair Parkland/Uirf

0.01

0.6

75%

0.0

0.6

75%

0.2

24%

Stephen Drive Park

0.2

26%

0.7

73%

0.0

0.7

73%

0.0

Stevenson Cemetery

0.2

27%

0.0

0.5

73%

Stockbridge Park

10%

86%

0.5

90%

0.1

Stonecliff Parkland

76%

24%

0.0

24%

0.0

Stoneridge Park

49%

37%

0.8

41%

10%

Strawberry Farms Park

77%

23%

0.0

23%

0.3

Sugar Run Parkland

96%

0.3

0.0

0.3

0.0

Sullivant Gardens Park

0.8

37%

0.5

24%

0.5

23%

46%

0.3

17%

Summitview Park Sycamore Bottoms Parkland Sycamore Hills Park

33%

59%

0.6

65%

0.2

99%

0.3

0.0

0.3

28%

71%

0.1

71%

0.0

Tanager Woods Parkland

93%

0.0

0.5

Teaford Parkland

98%

0.2

0.1

0.4

0.1

Thompson Park

0.7

16%

0.8

18%

33%

52%

32%

Three Creeks Park

1014

575

57%

332

33%

341

34%

158

15%

Thurber Park

0.4

55%

0.3

36%

0.0

0.3

36%

0.1

Tionda Drive Parkland

0.1

100%

0.0

Trabue Woods Park

80%

16%

0.0

16%

0.2

Turnberry Golf Course

201

32%

10%

13%

121

58%

Turnberry Parkland

67%

31%

0.0

31%

Tuttle Park Tuttle/Lane Ave Mut Connector Upper Albany School Site

61%

11%

34%

0.6

47%

0.3

22%

0.4

29%

0.6

50%

53%

58%

42%

0.0

42%

0.0

Parks and Parkland Assessment Results – Continued from previous page

APPENDIX

Park or Parkland Upper Albany West Parkland Vaughn Farm Parkland Virginia Terrace Water Ded Waggoner Chase Parkland Walden Park

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

47%

49%

0.0

49%

0.1

64%

37%

0.0

37%

0.4

78%

22%

0.0

22%

0.6

76%

24%

0.0

24%

0.0

0.2

95%

0.0

95%

0.0

Walnut Hill Park

26%

24%

26%

48%

Walnut Street Parkland

0.5

95%

0.0

95%

0.0

Walnut View Park

42%

54%

0.3

57%

0.1

Waltham Woods Park

0.3

21%

73%

0.1

77%

0.0

Wango Park Watercourse Dedication/Scioto Webster Park

68%

32%

0.0

32%

0.4

97%

0.2

0.0

0.2

41%

100%

0.0

Weinland Park

0.7

19%

67%

0.3

74%

0.3

West Bank Walkway

19%

60%

0.5

66%

20%

Westbank Park

49%

51%

0.0

51%

0.1

Westchester Parkland Westerford Village Parkland Westerville Woods Parkland Westgate Park

89%

0.2

11%

0.0

0.2

11%

0.0

99%

0.0

0.4

93%

0.1

0.0

0.1

38%

37%

11%

48%

16%

Westmoor Park

14%

77%

85%

0.2

Wexford Green Park

96%

0.4

0.0

0.4

0.0

Wheeler Memorial Park

0.9

23%

72%

0.0

73%

0.2

Whetstone Park

141

54%

31%

36%

16% 11%

White Ash Parkland

39%

50%

0.0

50%

Williams Creek Park

0.01

99%

0.0

99%

0.1

Willis Park

0.4

16%

0.7

27%

0.0

0.7

27%

58%

Willow Creek Park

65%

35%

0.0

35%

0.9

Wilson Avenue Park

0.1

75%

0.0

17%

0.0

17%

0.0

Wilson Road Parkland Winchester Bend Parkland Winchester Meadows ‐ Echelon Parkland Winchester Meadows Parkland Windsor Park

74%

25%

0.0

25%

0.4

95%

0.2

0.0

0.2

0.5

0.2

10%

89%

0.0

89%

0.0

0.8

90%

0.0

90%

44%

18%

32%

26%

58%

24%

Winward Farms Park

0.7

94%

0.0

94%

25%

Wolfe Park

38%

43%

53%

14%

Woodbridge Green Park

40%

57%

0.1

58%

0.1

Woodstream Park

71%

29%

0.0

29%

Total

7,276

3,690

51%

2,377

33%

216

2,593

36%

1,365

18%

Parks and Parkland Assessment Results – Continued from previous page

APPENDIX

City of Collumbus G Geographic Summary: HUC C 12 Wateersheds Percent Urban Tree Ca anopy

Percent Possible Plan nting Areas (Total)

Pe ercent Possib ble Planting A Areas (Vege etation)

Percent Possible Plan nting Areas (Impervious)

Figure 37: Wateershed Assessmeent Results

APPENDIX

City of Columbus Geographic Summary: HUC 12 Watersheds This table summarizes the UTC metrics by HUC12 watersheds in Columbus, OH in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 23: Watershed Assessment Results

Watershed

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

050600011405

3,105

730

24%

1,153

37%

325

10%

1,478

48%

944

30%

050600011801

568

10%

244

43%

246

43%

273

48%

050600011803

354

146

41%

26%

236

67%

112

31%

050600011601

2,503

314

13%

736

29%

584

23%

1,319

53%

910

36%

050600011204

8,775

2,433

28%

2,284

26%

1,153

13%

3,436

39%

3,058

34%

050600011401

5,198

1,677

32%

1,632

31%

536

10%

2,168

42%

1,451

27%

050600010805

6,094

1,047

17%

1,618

27%

754

12%

2,372

39%

3,109

48%

050600012201

3,167

484

15%

1,034

33%

124

1,158

37%

1,612

50%

050600011205

6,578

2,185

33%

1,831

28%

534

2,364

36%

2,090

32%

050600011404

5,684

1,054

19%

1,581

28%

945

17%

2,527

44%

2,254

40%

050600011403

9,350

2,598

28%

2,851

30%

895

10%

3,746

40%

3,241

34%

050600011206

6,176

1,069

17%

1,266

20%

1,098

18%

2,364

38%

2,839

45%

050600010806

10,587

1,858

18%

2,896

27%

1,903

18%

4,799

45%

4,441

40%

050600012301

16,028

2,839

18%

4,383

27%

1,744

11%

6,127

38%

7,880

47%

050600012302

10,105

1,973

20%

3,294

33%

994

10%

4,288

42%

3,979

39%

050600011406

4,531

639

14%

1,501

33%

525

12%

2,026

45%

1,972

43%

050600011802

2,082

210

10%

845

41%

118

964

46%

966

45%

050600011603

4,400

916

21%

1,216

28%

422

10%

1,638

37%

2,242

47%

050600012303

3,924

585

15%

1,048

27%

196

1,244

32%

2,594

59%

050600012304

141

19%

20%

106

76%

050600011203

3,602

1,229

34%

1,032

29%

300

1,332

37%

1,076

30%

050600011602

23,125

6,500

28%

21%

7,651

33%

2,525

11%

10,176

44%

6,640

29%

050600010803

518

123

24%

149

29%

17%

235

45%

164

31%

050600011402 050600011307

2,797 106

626 9

22% 8%

2% 0%

1,131 12

40% 11%

251 0

9% 0%

1,382 12

49% 11%

834 85

29% 80%

TOTAL

139,499

31,171

22%

100%

41,558

30%

16,107

12%

57,665

41%

54,872

38%

APPENDIX

City o of Columb bus Geoggraphic Su ummary:: BluePrin nt Columb bus Projeect Areas Percent Urban Tree Canopy

Percent Possiblee Planting Areeas (Total)

Possible Pllanting Areass (Vege etation)

Posssible Plantingg Areas (Imperviouss)

Figure 38: BluePrint Colum mbus Project Areea Assessment Reesults

A APPENDIX

City of Columbus Geographic Summary: BluePrint Columbus Project Areas

This table summarizes the UTC metrics by Columbus, OH BluePrint Columbus Project areas in acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 24: BluePrint Columbus Project Area Assessment Results PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

255

26%

550

56%

10%

224

35%

321

50%

BluePrint Columbus Project Area

Total Land Acres

UTC Acres

UTC %

% of Total UTC

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

Barthman Parson‐1

969

180

19%

182

19%

Barthman Parson‐2

645

100

15%

158

24%

937

133

14%

211

22%

128

14%

339

36%

468

50%

1,012

410

41%

256

25%

317

31%

287

28%

780

336

43%

222

28%

253

32%

202

26%

499

185

37%

137

28%

16%

218

44%

19%

335

158

47%

23%

27%

865

338

39%

196

23%

241

28%

289

33%

1,260

288

23%

358

28%

149

12%

507

40%

468

37%

1,320

255

19%

401

30%

129

10%

530

40%

539

41%

769

150

19%

262

34%

326

42%

295

38%

831

129

16%

278

33%

312

38%

405

49%

727

12%

291

40%

10%

363

50%

282

39%

826

140

17%

340

41%

142

17%

483

58%

206

25%

Fifth by NW (West Fifth)

876

126

14%

164

19%

202

23%

366

42%

398

45%

Franklinton (Sullivant)

159

19%

22%

16%

38%

47%

Hilltop (Early Ditch)‐1

926

176

19%

229

25%

168

18%

397

43%

353

38%

Hilltop (Early Ditch)‐2

711

167

24%

208

29%

237

33%

307

43%

Hilltop (Early Ditch)‐3

1,103

243

22%

385

35%

465

42%

396

36%

Hilltop (Early Ditch)‐4

782

125

16%

218

28%

176

22%

394

50%

267

34%

James Livingston‐1

622

149

24%

215

35%

12%

290

47%

183

29%

James Livingston‐2

722

246

34%

217

30%

255

35%

222

31%

James Livingston‐3

696

193

28%

214

31%

259

37%

244

35%

James Livingston‐4

1,220

325

27%

384

31%

470

39%

425

35%

James Livingston‐5

355

25%

113

32%

12%

154

43%

113

32%

James Livingston‐6

561 445

163 162

29% 36%

2% 2%

202 136

36% 31%

15 18

3% 4%

216 154

39% 35%

183 135

33% 30%

574

155

27%

173

30%

13%

248

43%

171

30%

Plum Ridge

619 784 497 1,154 907 1,016 901 342 229

160 247 110 335 202 264 277 89 74

26% 32% 22% 29% 22% 26% 31% 26% 32%

2% 4% 2% 5% 3% 4% 4% 1% 1%

197 242 164 489 264 316 290 79 82

32% 31% 33% 42% 29% 31% 32% 23% 36%

101 43 72 80 125 60 30 13 14

16% 6% 15% 7% 14% 6% 3% 4% 6%

298 285 236 569 389 376 320 92 97

48% 36% 48% 49% 43% 37% 35% 27% 42%

168 253 153 251 317 376 304 161 59

27% 32% 31% 22% 35% 37% 34% 47% 26%

TOTALS

27,974

6,996

25%

100%

8,302

30%

2,701

10%

11,086

40%

10,019

36%

Barthman Parson‐3 Clintonville (includes Walhalla)‐1 Clintonville (includes Walhalla)‐2 Clintonville (includes Walhalla)‐3 Clintonville (includes Walhalla)‐4 Clintonville (includes Walhalla)‐5 Driving Park Far South (Williams Road and Castle Road)‐1 Far South (Williams Road and Castle Road)‐2 Far South (Williams Road and Castle Road)‐3 Far South (Williams Road and Castle Road)‐4 Far South (Williams Road and Castle Road)‐5

James Livingston‐7 Kenny Henderson (Francisco Teteridge) Linden/NE Area (NW Alum)‐1 Linden/NE Area (NW Alum)‐2 Linden/NE Area (NW Alum)‐3 Linden/NE Area (NW Alum)‐4 Maize Morse‐1 Maize Morse‐2 Maize Morse‐3 Miller Kelton‐1

APPENDIX

City of C Columbuss Geograp phic Sum mmary: Neeighborhoods Percent U Urban Tree Can nopy

Percent Po ossible Plantiing Areas (Total)

Percent Possible Planting Arreas etation) (Vege

Percent Po ossible Plantiing Areas ((Impervious)

Fiigure 39: Neighb borhood Assessm ment Results

A APPENDIX

City of Columbus Geographic Summary: Neighborhoods This table summarizes the UTC metrics by Neighborhoods in Acres and % including Urban Tree Canopy (UTC), Possible Planting Area Vegetation (PPA Vegetation), Possible Planting Area Impervious (PPA Impervious), Total Possible Planting Area (Total PPA), and Areas Unsuitable for Planting (Unsuitable). Table 25: Neighborhood Assessment Results Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuita ble UTC (acres)

Unsuit able UTC %

Abby Trails

569

14%

326

57%

363

64%

145

25%

Airport

2,683

179

446

17%

441

16%

887

33%

1,812

67%

Albany Commons

268

144

54%

163

61%

32%

Alum Crest Acres

247

27%

100

40%

19%

146

59%

14%

Amercrest

564

17%

248

44%

291

52%

177

31%

Antrim

310

22%

105

34%

128

41%

124

39%

Appian

313

26%

108

35%

14%

152

49%

104

31%

Argyle Park

357

16%

134

37%

15%

189

53%

112

31%

Arlington Park

180

30%

40%

46%

24%

Beechwood

324

28%

105

32%

117

36%

117

36%

Berwick

814

289

35%

260

32%

298

37%

246

30%

Blendon Woods

444

127

29%

188

42%

206

46%

112

25%

Blendon Woods Metro Park

592

496

84%

14%

16%

Bluff View

38%

31%

Brandywine

371

23%

158

43%

170

46%

117

32%

Brentnell

38%

39%

23%

Brentwood Park Brewery District (including Whittier Peninsula) Brice

167

39%

33%

35%

26%

364

18%

113

31%

14%

165

45%

231

50%

475

142

30%

162

34%

274

57%

Bridgeview

549

264

48%

204

37%

239

44%

Brittany Hills

297

106

36%

114

38%

133

45%

20%

Broadleigh

420

112

27%

155

37%

12%

204

49%

105

25%

Brookhollow

286

21%

25%

12%

106

37%

140

46%

Brookshire

749

148

20%

218

29%

10%

292

39%

310

41%

Brookside Colony

324

23%

105

32%

16%

158

49%

29%

Brookside Village Brookside Woods Busch Cambria Addition Central Clintonville Central College Central Hilltop Cherry Creek Christopher Woods Clintonville Cobleton Coppertree Cranbrook Cross Creek Crossroads Crosswoods Crown Ridge Cumberland Ridge

150 522 750 8 603 654 698 565 24 720 426 42 224 525 5 233 88 579

39 128 77 1 226 98 185 134 12 295 23 11 97 59 0 21 17 170

26% 25% 10% 17% 38% 15% 27% 24% 49% 41% 5% 25% 43% 11% 4% 9% 19% 29%

55 128 237 4 148 274 233 178 6 167 180 14 61 205 0 37 23 212

37% 25% 32% 47% 24% 42% 33% 32% 26% 23% 42% 34% 27% 39% 9% 16% 25% 37%

10 113 236 0 37 124 31 60 0 30 1 3 11 22 0 112 16 89

7% 22% 31% 0% 6% 19% 5% 11% 0% 4% 0% 8% 5% 4% 0% 48% 19% 15%

65 241 473 4 185 398 264 238 6 197 181 18 72 227 0 148 39 301

44% 46% 63% 47% 31% 61% 38% 42% 26% 27% 42% 42% 32% 43% 9% 64% 44% 52%

45 159 212 3 192 169 249 195 6 250 231 15 56 238 4 64 33 109

30% 30% 28% 36% 32% 25% 36% 34% 25% 34% 53% 34% 25% 45% 87% 27% 37% 19%

APPENDIX

Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Deer Creek

422

15%

150

36%

13%

205

49%

Dennison Place

130

23%

13%

23%

Deshler Park

440

105

24%

134

30%

11%

183

Devon Triangle

224

12%

26%

12%

Devonshire

410

108

26%

130

32%

Dexter Falls

470

110

23%

156

33%

Don Scott

1,660

209

13%

272

16%

Downtown

1,535

120

164

11%

Driving Park

508

116

23%

135

East

1,065

227

21%

East Beechwold

342

112

Unsuita ble UTC (acres)

Unsuit able UTC %

157

37%

35%

44%

41%

152

35%

39%

111

49%

158

39%

144

35%

186

39%

180

38%

166

10%

438

26%

1,072

64%

364

24%

528

34%

1,005

61%

26%

165

33%

227

45%

319

30%

385

36%

469

44%

33%

103

30%

113

33%

117

34%

East Broad I

1,251

238

19%

421

34%

303

24%

724

58%

312

24%

East Broad II

1,475

364

25%

599

41%

140

739

50%

384

26%

East Columbus

789

184

23%

314

40%

11%

402

51%

204

26%

East Linden

213

38%

34%

38%

25%

Eastgate

241

38%

24%

31%

33%

Eastland

1,871

542

29%

443

24%

363

19%

806

43%

567

30%

Eastmoor 1

690

241

35%

208

30%

251

36%

199

29%

Eastmoor 2

540

140

26%

176

33%

224

42%

175

33%

Easton

1,468

280

19%

548

37%

305

21%

852

58%

346

23%

Edgewood

187

21%

34%

40%

Fairgrounds

443

110

25%

161

36%

271

61%

139

31%

Far North

49%

20%

17%

37%

18%

Flint Road

46%

18%

36%

22%

Fodor

243

23%

40%

113

47%

30%

Forest Park East Forest Park West Foxboro Framingham Franklin Park Franklinton Galloway Ridge Gateway Georgian Heights German Village Commission Glen Echo Glenbrook Glenmeadows Golfview Woods Gould Park Governours Square Grandview South Grasshopper Creek Greenbriar Farm Greenhill Acres Harrison West Hayden Falls Henderson Heights Hickory Bluff Farms Highpoint‐Glen Hilliard Green

1,499 431 282 369 358 1,057 492 164 440

329 123 42 146 105 159 58 77 101

22% 28% 15% 40% 29% 15% 12% 47% 23%

471 129 74 149 105 251 174 39 128

31% 30% 26% 40% 29% 24% 35% 24% 29%

230 41 73 24 22 140 49 29 49

15% 9% 26% 6% 6% 13% 10% 18% 11%

701 170 147 173 127 391 224 68 177

47% 39% 52% 47% 36% 37% 45% 41% 40%

471 139 98 53 131 511 218 20 162

31% 32% 34% 14% 36% 48% 44% 12% 37%

234

20%

13%

19%

143

61%

68 1,371 127 893 219 257 548 373 835 178 335 90 75 229 365 724

27 242 41 153 134 46 109 43 400 34 61 25 13 107 86 66

40% 18% 32% 17% 61% 18% 20% 11% 48% 19% 18% 28% 17% 47% 24% 9%

13 495 47 257 56 62 142 38 212 90 63 30 23 12 117 198

18% 36% 37% 29% 25% 24% 26% 10% 25% 50% 19% 33% 31% 5% 32% 27%

1 160 1 119 7 70 111 0 35 7 63 8 18 0 12 177

1% 12% 1% 13% 3% 27% 20% 0% 4% 4% 19% 9% 24% 0% 3% 24%

13 655 48 376 63 132 254 39 247 96 126 37 41 12 129 375

19% 48% 38% 42% 29% 51% 46% 10% 30% 54% 38% 41% 55% 5% 35% 52%

28 477 38 372 44 80 361 292 200 49 161 53 21 110 149 289

41% 35% 30% 41% 18% 31% 50% 78% 24% 27% 46% 46% 28% 48% 41% 40%

Neighborhood Assessment Results – Continued from previous page

APPENDIX

Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsui table UTC %

Hilltop

63%

67%

31%

Hilltop 1

367

15%

157

43%

170

46%

150

40%

Hilltop 2

366

17%

118

32%

27%

216

59%

25%

Hilltop 3

810

121

15%

191

24%

194

24%

385

48%

305

38%

Hilltop 4

372

17%

148

40%

209

56%

136

37%

Holly Hill

356

16%

27%

11%

132

37%

166

47%

Holt‐Alkire

465

11%

178

38%

11%

228

49%

193

41%

Hungarian Village

14%

23%

25%

61%

Hyde Park

118

35%

30%

13%

43%

22%

Independence Village

1,716

225

13%

576

34%

336

20%

912

53%

604

35%

Indian Hills

241

108

45%

32%

34%

21%

Indian Springs

508

236

47%

127

25%

155

31%

117

23%

Indiana Forest

24%

11%

14%

25%

50%

Indianola Terrace

149

22%

15%

11%

27%

51%

Industrial Site

186

17%

25%

133

72%

Innis Garden Village

328

20%

101

31%

13%

145

44%

118

36%

Italian Village

281

11%

20%

14%

34%

154

55%

Iuka Ravine

59%

13%

16%

26%

Kendale

197

33%

34%

41%

27%

King‐Lincoln‐Bronzeville

355

17%

19%

14%

116

33%

179

50%

Knolls‐Thomas

568

194

34%

148

26%

11%

212

37%

164

29%

Knolls West

385

25%

123

32%

12%

170

44%

121

31%

Laurel Greene

450

18%

178

40%

213

47%

158

35%

Leawood

566

174

31%

164

29%

207

37%

191

33%

Linwood

743

192

26%

242

33%

300

40%

252

34%

Linworth Village Little Turtle Livingston ‐ McNaughten Livingston Park North Madison Mills Maize‐Morse Marble Cliff Crossing Marion Franklin Merion Village Milbrook Mill Run Milo‐Grogan Minerva Park Misty Meadows Mount Vernon Necko North Campus North Franklinton North Hilltop North Linden North Rickenbacker Northbridge Northcrest Northern Woods Northgate Northmoor Northwood Park

309 641 401 228 215 1,198 1,051 2,157 739 667 206 618 3 235 371 27 91 391 624 2,669 410 85 269 1,542 995 232 26

143 228 129 43 48 289 316 412 167 96 20 95 0 23 60 8 19 63 156 743 47 41 54 412 268 123 11

46% 36% 32% 19% 22% 24% 30% 19% 23% 14% 10% 15% 2% 10% 16% 29% 21% 16% 25% 28% 11% 48% 20% 27% 27% 53% 43%

69 174 105 44 86 340 333 638 126 173 37 122 0 56 97 3 10 131 184 851 194 21 70 437 311 49 4

22% 27% 26% 19% 40% 28% 32% 30% 17% 26% 18% 20% 6% 24% 26% 12% 11% 34% 29% 32% 47% 24% 26% 28% 31% 21% 16%

24 49 62 30 14 192 147 278 71 109 78 89 0 47 36 4 16 51 61 152 64 0 69 304 128 6 0

8% 8% 15% 13% 6% 16% 14% 13% 10% 16% 38% 14% 0% 20% 10% 13% 17% 13% 10% 6% 16% 0% 26% 20% 13% 2% 1%

93 222 167 74 99 532 479 915 198 282 115 211 0 103 133 7 25 182 244 1,003 258 21 139 741 439 55 4

30% 35% 42% 32% 46% 44% 46% 42% 27% 42% 56% 34% 6% 44% 36% 25% 28% 47% 39% 38% 63% 24% 52% 48% 44% 24% 17%

119 205 105 112 72 386 431 852 525 293 82 313 2 109 178 12 46 146 225 924 105 23 76 396 289 62 10

34% 31% 26% 49% 33% 32% 35% 39% 59% 44% 38% 51% 92% 46% 48% 46% 51% 37% 36% 35% 26% 27% 28% 26% 29% 26% 40%

Neighborhood Assessment Results – Continued from previous page APPENDIX

Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

Northwoods

187

121

64%

13%

22%

15%

Norton Ridge

997

173

17%

226

23%

740

74%

Old Beechwold

147

60%

21%

22%

20%

Old North Columbus

393

128

32%

15%

22%

178

45%

Olde Orchard

940

274

29%

347

37%

101

11%

448

48%

225

24%

Olde Sawmill

451

108

24%

157

35%

11%

207

46%

142

31%

Olde Town East

497

103

21%

19%

10%

144

29%

251

50%

Olentangy

237

16%

39%

107

45%

39%

Olentangy Commons

407

122

30%

24%

20%

178

44%

108

27%

Olentangy Glade

38%

43%

48%

Olentangy High Bluffs

265

150

57%

18%

27%

19%

Olentangy Highlands

174

54%

25%

21%

Oriole Heights

155

34%

46%

50%

16%

Park Club

16%

26%

19%

45%

40%

Parkview

589

184

31%

211

36%

11%

277

47%

132

22%

Pine Hills

271

27%

32%

105

39%

35%

Pocono

33%

22%

17%

39%

28%

Polaris

1,039

247

24%

381

37%

628

60%

374

35%

Polaris North

390

19%

137

35%

12%

183

47%

138

35%

Polaris South

228

28%

22%

115

50%

107

45%

Preserve North

845

228

27%

302

36%

333

39%

295

34%

Preserve South

457

12%

176

38%

20%

265

58%

145

31%

Preston Commons

15%

48%

56%

31%

Reeb‐Hosack

127

12%

22%

10%

32%

57%

River 1

73%

21%

25%

108

72%

Riverbend Riverplace Riverside Riverview Salem Village Sanctuary Save Our Southside Sawmill Forest Sawmill Ravines Schirm Farm Schumacher Place Scioto Trace Scioto Woods Seven Oaks Shady Lane Shannon Green Shannon Heights Sharon Heights Shepard Slate Hill Smoky Mill Estates Smoky Ridge Estates Somerset South Campus South Central Hilltop South Franklinton South Hilltop

1,098 143 848 465 619 47 7,234 128 97 482 114 1,199 433 42 756 1,333 292 891 233 261 38 124 771 115 381 307 393

455 46 256 115 160 25 1,064 55 26 22 17 371 125 15 296 182 65 323 61 54 13 34 145 15 86 54 94

41% 32% 30% 25% 26% 53% 15% 43% 27% 5% 15% 31% 29% 36% 39% 14% 22% 36% 26% 20% 33% 27% 19% 13% 23% 18% 24%

286 53 217 133 175 9 2,194 39 17 181 15 332 127 12 199 404 80 250 71 87 15 50 365 10 140 85 112

26% 37% 26% 29% 28% 19% 30% 31% 17% 38% 13% 28% 29% 29% 26% 30% 28% 28% 31% 33% 39% 41% 47% 9% 37% 28% 28%

80 7 84 100 87 0 515 3 24 82 12 85 26 1 54 26 28 109 24 43 0 1 77 27 16 61 34

7% 5% 10% 22% 14% 0% 7% 3% 24% 17% 11% 7% 6% 1% 7% 2% 10% 12% 10% 17% 1% 1% 10% 24% 4% 20% 9%

366 60 301 234 262 9 2,709 43 40 263 27 417 153 13 254 431 109 359 95 130 15 51 442 38 156 146 146

33% 42% 36% 50% 42% 19% 37% 34% 42% 55% 23% 35% 35% 31% 34% 32% 37% 40% 41% 50% 39% 41% 57% 33% 41% 48% 37%

283 71 365 119 223 13 4,310 30 75 223 70 544 157 14 217 757 118 214 77 79 10 39 210 63 139 109 153

26% 40% 40% 26% 35% 28% 53% 24% 53% 44% 62% 41% 36% 33% 28% 55% 40% 24% 33% 30% 27% 32% 26% 55% 37% 35% 39%

Neighborhood Assessment Results – Continued from previous page APPENDIX

Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

South Linden

1,032

221

21%

292

28%

374

36%

437

42%

South London

790

293

37%

269

34%

285

36%

530

48%

South of Main

260

27%

22%

29%

114

44%

South Side CAN

390

19%

24%

107

28%

211

54%

Southeast 1

542

207

38%

125

23%

11%

186

34%

170

30%

Southeast 2

916

281

31%

464

51%

473

52%

203

21%

Southern Orchards

258

20%

21%

26%

140

54%

Southside 5

457

17%

122

27%

19%

208

46%

171

37%

Southwest 1

394

19%

143

36%

180

46%

140

35%

Southwest 2

1,126

162

14%

316

28%

379

34%

688

56%

Southwest 3

443

15%

202

46%

240

54%

151

33%

Southwest 4

281

20%

122

43%

141

50%

30%

Southwest 5

104

19%

31%

20%

51%

30%

Southwest Airport

1,198

123

10%

366

31%

260

22%

626

52%

470

39%

Southwest Hilltop

399

21%

135

34%

147

37%

167

42%

Springbourne

13%

18%

31%

50%

40%

St. Mary's

248

33%

30%

10%

40%

28%

Stambaugh‐Elwood

25%

40%

41%

34%

Stilson

100

31%

36%

33%

Stilson East

55%

26%

19%

Stonebridge

373

24%

174

47%

187

50%

100

27%

Strawberry Farms

807

293

36%

253

31%

10%

333

41%

191

23%

Summerwood

163

31%

35%

39%

31%

Summit View Forest

40%

39%

40%

20%

Summit View Woods

255

30%

38%

104

41%

30%

Sweetwater Sycamore Hills Teakwood The Gables The Ohio State University Three Corners Three Rivers Trabue Woods Tri‐Village Trouville Tuttle Tuttle West University Valleyview Valleyview Heights Vasser Village (Lincoln Park) Victorian Village Village at Forest Ridge Village at Worthington Walnut Creek Walnut Heights Walnut Hills Weinland Park West Albany West Campus West London

927 317 135 472 932 417 907 393 696 409 225 1,022 144 2 308

107 124 45 99 123 120 456 47 96 81 12 92 31 1 43

12% 39% 33% 21% 13% 29% 50% 12% 14% 20% 5% 9% 21% 57% 14%

294 98 38 162 221 39 185 123 107 138 46 281 14 1 80

32% 31% 28% 34% 24% 9% 20% 31% 15% 34% 20% 27% 10% 24% 26%

139 23 23 72 246 0 50 65 172 91 75 153 28 0 92

15% 7% 17% 15% 26% 0% 5% 16% 25% 22% 33% 15% 19% 0% 30%

433 121 62 234 467 39 235 188 279 229 121 434 42 1 172

47% 38% 46% 50% 50% 9% 26% 48% 40% 56% 54% 42% 29% 24% 56%

413 75 28 149 399 258 250 159 323 104 100 519 72 0 93

43% 23% 21% 31% 41% 62% 27% 40% 46% 25% 43% 50% 50% 19% 30%

233

22%

18%

40%

121

52%

285 7 99 810 985 413 203 1,274 744 924

65 2 26 384 189 86 24 330 108 214

23% 27% 27% 47% 19% 21% 12% 26% 15% 23%

47 1 32 223 418 116 42 406 225 238

16% 19% 32% 28% 42% 28% 21% 32% 30% 26%

31 2 5 56 37 61 23 73 97 74

11% 25% 5% 7% 4% 15% 12% 6% 13% 8%

77 3 37 280 455 177 65 479 322 312

27% 44% 38% 35% 46% 43% 32% 38% 43% 34%

143 2 36 153 371 153 114 479 318 432

50% 30% 36% 19% 37% 37% 56% 37% 42% 45%

Neighborhood Assessment Results – Continued from previous page

APPENDIX

Neighborhood

Total Land Acres

UTC Acres

UTC %

PPA (Veg.) Acres

PPA (Veg.) %

PPA (Imp.) Acres

PPA (Imp.) %

Total PPA Acres

Total PPA %

Unsuit able UTC (acres)

Unsuit able UTC %

Westbelt

628

10%

153

24%

177

28%

330

53%

238

38%

Westbend

221

10%

43%

114

49%

Westbrook

127

25%

24%

28%

52%

23%

Westbrooke‐Heritage Westchester‐Green Countrie Western Hills

388

194

50%

203

52%

165

41%

541

100

19%

196

36%

10%

248

46%

195

36%

206

26%

42%

32%

Westgate

553

124

22%

155

28%

201

36%

229

41%

Westside 1

868

180

21%

149

17%

328

38%

496

57%

Westside 2

804

101

13%

227

28%

164

20%

391

49%

321

40%

Westside 3

403

17%

19%

23%

249

61%

Westworth Village

117

25%

39%

41%

34%

Wexford‐Thornapple

700

121

17%

268

38%

331

47%

278

38%

Wexmoor

376

21%

117

31%

14%

171

45%

128

34%

Whetstone

391

179

46%

106

27%

133

34%

24%

White Ash

1,462

169

12%

569

39%

599

41%

718

48%

Williams Creek

324

145

45%

17%

200

62%

30%

Willow Creek

817

196

24%

346

42%

407

50%

223

27%

Winchester

621

100

16%

262

42%

14%

351

56%

217

32%

Windward Farms

489

11%

144

30%

148

30%

294

60%

Wolfe Park

50%

32%

37%

13%

Wood Bridge Green

122

24%

39%

10%

49%

28%

Woodland Holt

145

39%

34%

37%

24%

Woodland Park

243

31%

24%

32%

37%

Woods of Josephinium

363

159

44%

103

28%

125

34%

23%

Woodstream

324

113

35%

157

49%

157

49%

17%

Woodward Park Worthington Crossing Worthington Green Worthington Highlands Worthington Hills Worthington Park Worthington Village North Worthington Woods Worthingview Wynstone

427 157 132 229 139 189

87 16 31 44 62 40

20% 10% 23% 19% 45% 21%

129 57 47 65 43 69

30% 36% 36% 28% 31% 37%

60 36 5 28 1 18

14% 23% 4% 12% 0% 9%

189 94 53 93 44 87

44% 60% 40% 41% 31% 46%

151 51 49 92 34 64

35% 32% 37% 40% 24% 34%

157

26%

52%

39%

98 140 131 138,436

23 52 22 30,986

23% 37% 17% 22%

30 45 51 41,327

31% 32% 39% 30%

16 4 10 16,048

16% 3% 8% 12%

46 49 61 57,375

47% 35% 47% 41%

29 39 48 54,227

30% 28% 37% 40%

TOTAL

Neighborhood Assessment Results – Continued from previous page

APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups

Percent U Urban Tree Can nopy

Percent P Possible Planting Areas (Total)

Pe ercent Possible Planting Areas etation) (Vege

Percent P Possible Planting Areas (Imperviouss)

Figu ure 40: Census Bllock Group Assesssment Results

APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups M MEDIAN HOU USEHOLD INCOME IN THE PAST 12 MONTHS (IN 20113 INFLATION N‐ADJUSTED DOLLARS) A As the percen ntage of urban n tree canopyy increases in ccensus block ggroups, median income alsso increases. W We have highlighted in oraange those block groups tthat fall below w the City’s m median household income leevel of $44,072, and contaain less than tthe average aamount of UTTC. These are eas might be cconsidered ffor tree plantiing opportunities.

% UTC

Average Meedian Income

551‐100% 226‐50%

79,,307 49,,986

0‐25%

46,,141

Figure 41:: Median Househ hold Income andd Urban Tree Canopy

A APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups M MEDIAN VALU UE (DOLLARSS) FOR OWNEER‐OCCUPIED D HOUSING U NITS A As the percen ntage of urban n tree canopyy increases in ccensus block ggroups, median home valu ue also increases. We e have highligghted in orangge those b block groups w with median home values that fall b below the Cityy’s median ho ome value of $130,700, aand contain le ess than the aaverage amou unt of UTC. TThese areas m might be conssidered for tre ee planting o opportunities.

551‐100% 226‐50%

Average Meedian Home Vaalue 241 1,534 140 0,573

0‐25%

138 8,391

% UTC

Figure 42: Median Hom me Value and Urrban Tree Canoppy

A APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups EEDUCATIONA AL ATTAINMEENT FOR THE POPULATION N 25 YEARS A AND OVER A As one might expect based d on income aand home vvalues, the ed ducational atttainment in areas of ggreater tree ccanopy is also higher. Acro oss C Columbus, 33.1% of person ns aged 25 an nd older have e aa Bachelor’s D Degree or higher. Here, we have h highlighted th hose census b block groups w where the p percent holding a Bachelorr’s or higher is less than tthe citywide aaverage, and where there is less than aaverage UTC.

551‐100% 226‐50%

Percent with h a Bachelor’s Degree o or Higher 51 1% 35 5%

0‐25%

34 4%

% UTC

Figu ure 43: Education n Level and Urbaan Tree Canopy

A APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups TTENURE FOR OCCUPIED HOUSING UNITTS TThe rate of ow wner occupan ncy is greater in areas w with a higher percentage o of UTC. This indicator ggenerally refle ects the stability of a given n area or n neighborhood d, as it is belie eved that there is more of aa vested interrest in person nal property. For C Columbus, the e homeownership rate is 4 46.9%. W We’ve highlighted in orangge those censsus block

grouups with less tthan average owner occup pancy and less tthan averagee UTC. % UTC

Percent Own ner Occupied

551‐100% 226‐50%

65 5% 56 6%

0‐25%

48 8%

Figuree 44: Owner Occcupancy and Urbban Tree Canopyy

A APPENDIX

Ciity of Colu umbus Geographiic Summaary: Censsus Block Groups R RACE W While there iss not as clear a trend in the e distribution n o of race, with tthe 26‐50% U UTC range being slightly h higher than th he 0‐25% UTC C range, there e is notably leess of a mino ority populatio on in areas off the city w with the greattest canopy ccover. Consid dering that 6 62% of Colum mbus is white, we’ve highligghted census b block groups w where the no on‐white popu ulation (or

any m minority population) is higgher than 38% % and whe re the UTC is below average. % UTC

Percent N Non‐White

551‐100% 226‐50%

21 1% 35 5%

0‐25%

34 4%

Figure 45: Minority Pop pulations and Ur Urban Tree Canoppy

A APPENDIX

Canop py Threat: Emeraald Ash Borer (P Public Laand) EEmerald ash b borer (EAB) iss one of, if nott the most de estructive for est pest in No orth American history, and d it tthreatens billiions of ash tre ees in landscaapes and fore ests throughoout Ohio and even as far w west as Coloraado. The d deleterious im mpacts to society from the e loss of canop py include redduced properrty values, lesss mitigation of storm w water runoff aand air polluttion, and incre eased urban heat island efffect, to name just a few. However, givven the C City’s assumed decline in U UTC for several decades, m more needs too be done, an nd better information is neeeded to eestimate the iimpact EAB and other pestts will have on UTC goals iin Columbus. TThe ash population and canopy cover w was estimated d on public prroperty (ROW W and Parks). The following criteria w were used in estimating po otential loss in public spaces per Planni ng Area:   

h trees (Fraxin nus spp.) will be removed or die eventu ually. All invventoried ash Averaage canopy off 20‐year ash tree is 907 sq quare feet. This n number is totaaled for all assh trees and ssubtracted froom overall UTTC Metrics to get a loss peercentage if rem moved.

Pla anning Area: 0 07: 28% pottential loss in public areeas

Figure 446: Ash Tree Can nopy on Public La and by Plannin ng Area (H Highlighting Plan nning Area 7)

A APPENDIX

Canopyy Threatt: Emeraald Ash B Borer (Private Laand) TThe ash population and canopy cover w was estimated d for private pproperty. Thee following crriteria were u used in eestimating po otential loss on private land d in Planning Areas:  All invventoried ash h trees (Fraxin nus spp.) will be removed or die eventu ually.  Averaage percent ash within the e City’s private e lands is ~100.3% based on n Ash percentt in Columbus parks.  This p percentage will be subtractted from overall UTC metrrics within Plaanning Areas.  A pluss/minus 2% e error margin w was used in our calculationns.

Figure 47: Planning A Areas with Moree than 100 Acress of Privately O Owned Ash Treee Canopy

A APPENDIX

Canopy Threat: Emerald Ash Borer Analysis Results Plan Area ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Planning Area Name

Acres Public Ash Canopy

% UTC Loss if Public Ash Trees are Removed

Hayden Run Far Northwest Far North Rocky Fork‐Blacklick Northwest Northland Far West West Scioto West Olentangy Clintonville North Linden Northeast Near North/University South Linden Hilltop Franklinton Greenlawn/Frank Road Downtown Near East Eastmoor/Walnut Ridge Far East Near South Far South Eastland/Brice Westland Rickenbacker Southeast TOTALS

Acres of Estimated Private Ash Canopy (at 10.3%)

Est. Private Ash Canopy in Acres +2 %

Est. Private Ash Canopy in Acres ‐2 %

% UTC Loss if Public and Private Ash Trees are Removed

12%

13%

14%

117

140

11%

101

121

10%

178

212

143

103

123

169

202

136

16%

11%

110

122

145

143

171

116

107

128

124

148

100

170

203

137

117

106

126

150

101

122

146

111

0 2

0% 3%

100 41

119 49

75 81 33

10% 10% 10%

218

2,491

2,974

2,008

APPENDIX

Caanopy Th hreat: Asian Lon ng‐horneed Beetlle (Public Land) TThe tree popu ulation sensitive to the Asian Long‐horn ned Beetle waas estimated for potential tree canopy cover lo oss in public sspaces. The following crite eria were used in estimatinng potential lloss on publicc land in Plann ning A Areas:   

Preferred host list in the US acccording to USDA‐APHIS‐PPPQ (Acer, Aescculus, Betula,, Salix, and Ulmus), will eventtually die and be removed. Averaage canopy off 20‐year tree e is 907 squarre feet. This n number is totaaled for all ALLB sensitive trrees and subttracted from overall UTC M Metrics to gett a loss perce entage if remo oved.

Figure 48: ALB‐Se F ensitive Tree Cannopy on Public Land d by Planning Areea

A APPENDIX

Canopy Th hreat: Assian Long‐horneed Beetlee (Privatte Land)) TThis task involves estimatin ng the ALB se ensitive tree p population annd potential tree canopy cover loss in p private sspaces per Plaanning Area.  All invventoried ALB B hosts define ed by USDA‐A APHIS‐PPQ wiill be removed or die even ntually.  Averaage percent ash within the e City’s private e lands is ~199.7% based on n ALB hosts in n Columbus p parks.  This p percentage will be subtractted from overall UTC metrrics within Plaanning Areas for private property.  A pluss/minus 2% e error margin w was used in our calculationns.

Figu ure 49: Planning Areas with Morre than 200 Acrees of Privately Owned ALB‐Sensitivee Tree Canopy

A APPENDIX

Canopy Threat: Asian Long‐horned Beetle Analysis Results Plan Area ID

Planning Area Name

Hayden Run Far Northwest Far North Rocky Fork‐Blacklick Northwest Northland Far West West Scioto West Olentangy Clintonville North Linden Northeast Near North/University South Linden Hilltop Franklinton Greenlawn/Frank Road Downtown Near East Eastmoor/Walnut Ridge Far East Near South Far South Eastland/Brice Westland Rickenbacker Southeast

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

TOTALS

Acres Public ALB‐ Sensitive Canopy

% UTC Loss if Public ALB‐ Sensitive Trees are Removed

Acres of Estimated Private ALB‐ Sensitive Canopy (at 19.7%)

Est. Private ALB‐ Sensitive Canopy in Acres +2 %

Est. Private ALB‐Sensitive Canopy in Acres ‐2 %

% UTC Loss if Public and Private ALB‐ Sensitive Trees are Removed

12%

18%

25%

224

247

201

20%

21%

194

213

174

20%

339

374

305

17%

197

218

177

17%

15%

323

356

290

19%

20%

176

194

158

16%

153

169

138

17%

13%

232

256

209

18%

274

301

246

16%

205

225

184

17%

29%

23%

10%

151

166

136

18%

10%

237

261

213

17%

15%

112

123

100

17%

13%

17%

20%

15%

324

357

291

19%

188

207

169

18%

14%

169

187

152

18%

240

265

216

18%

233

256

209

10%

9 0 1

6% 1% 2%

177 191 79

195 210 87

159 171 71

18% 18% 18%

640

4,741

5,222

4,259

17%

APPENDIX

Comprehensive Ecosystem Services Results Tree Canopy Ecological Services Benefits Results Ecological services benefits were derived using the United States Department of Agriculture (USDA) i‐Tree Canopy software tools. The values were then summarized by Planning Areas. Table 26: Ecosystem Services Benefits by Planning Area

Plan Area ID

Planning Area Name

UTC acres

Ecosystem Benefit Value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Hayden Run Far Northwest Far North Rocky Fork‐Blacklick Northwest Northland Far West West Scioto West Olentangy Clintonville North Linden Northeast Near North/University South Linden Hilltop Franklinton Greenlawn/Frank Road Downtown Near East Eastmoor/Walnut Ridge Far East Near South Far South Eastland/Brice Westland Rickenbacker Southeast TOTALS

327 1,332 1,079 2,099 1,301 2,093 468 1,182 1,001 1,600 1,880 1,277 515 959 1,777 292 713 108 573 2,079 1,074 1,118 1,415 2,347 1,036 1,047 450 31,143

$127,624 $519,609 $420,999 $818,896 $507,596 $816,695 $182,596 $461,379 $390,557 $624,243 $733,673 $498,387 $200,946 $374,209 $693,508 $114,042 $278,225 $42,217 $223,381 $811,373 $418,982 $436,341 $552,045 $915,571 $404,147 $408,533 $175,670 $12,151,446

APPENDIX

Tree Canopy Hydrological Benefits Results Above are the full annual results for the i‐Tree Hydro model scenarios. Generally speaking, less tree cover not only leads to more runoff, but more pollution of various types as well. Table 19 shows the summarized runoff changes for the five scenarios while Table 20 shows annual pollutant loads for three relatively common water quality contaminants: Total Suspended Solids (TSS), Nitrogen, and Phosphorus. TSS is a common constituent found in water as a response to erosion in the area. With more tree cover, erosion can be mitigated and TSS concentrations can be kept at manageable levels well below EPA standards. Nitrogen and phosphorus typically come from sources including agricultural activity (fertilizers and pesticides) and livestock (decomposing waste from farm animals). Table 27: Summarized Hydro results for five separate land cover scenarios. Included are UTC percentages, runoff values, percent change in volume, and estimated changes to treatment costs.

Annual Pollutant Loads (kg) TSS* P N

Scenario

NO COVER (0%) EAB/ALB (16.5%) Current Conditions (22.3%) Green Memo (27.3%) American Forest (40%)

7,944,458 7,559,422 7,431,992 7,304,046 6,991,879

31,920 30,373 29,861 29,347 28,092

175,305 166,809 163,997 161,173 154,285

% Change 7% 2% 0% ‐2% ‐6%

*For the purposes of this study, runoff is defined as i‐Tree Hydro’s impervious flow output. **Monetary values estimated at $106/cubic meter Table 28: Summarized annual Hydro pollution results for three major constituents, with percent change values.

Scenario

UTC (%)

Runoff* (m3)

% Change

Change in Treatment Costs (Estimate)

No Cover EAB/ALB Total Loss Current Conditions

0.0% 16.5% 22.3%

66,293,677 61,078,743 58,946,527

12% 4% N/A

$778,797,842 $226,014,903 N/A

Green Memo Recommendation

27.3%

57,683,053

‐2%

‐$133,928,278

American Forest Recommendation 40.0%

53,588,290

‐9%

‐$567,973,171

*Total Suspended Solids: A measure of the amount of solid material suspended in a water sample. Generally, TSS is a byproduct of erosion.

Recent developments to i‐Tree Hydro have made it possible to estimate runoff within municipal/political boundaries (as opposed to hydrologic boundaries) through the use of topographic index (TI) files. This comes with a long list of assumptions, however. Hydro is an incredibly complex model that requires inputs ranging from soil surface texture to rooting zone to information on the connectivity of impervious areas. These kinds of values can be very difficult to characterize in the field, let alone remotely or empirically. In addition to this, using topographic indices removes the option of using USGS stream gauge data from the area to compare to the model’s outputs. Even with these limitations, this approach was favored over using one single watershed.

APPENDIX

C Characterizingg runoff withiin one watersshed or d drainage basin is the origin nal and most cconsistent waay to use i‐Tre ee Hydro. As w with any m model, howevver, it is only as good as th he data tthat is used ass an input. In the case of this p project, no full watershedss or sub‐wate ersheds w were present within the citty limits to be e used ffor modelling purposes. Ad dditionally, land ccover metrics being confined to within tthe city limits remove ed the option of using a fulll w watershed ou utside of the ccity to estimate rrunoff within the boundaryy. SSpatial variability and data availability aalso play aa huge role in the usefulne ess of Hydro’ss o outputs. In an n ideal modeling world, mu unicipal b boundaries would reflect h hydrologic b boundaries an nd a weather station with cconsistent datta would be located at an average eelevation. This is almost ne ever the case. These ccontributed to o the estimatted streamflow vvalues varyingg wildly from the actual ob bserved U USGS streamfflow values in n the area as w well as o other issues that are not as apparent orr easy to ffix (IE: Digital Elevation Mo odel resolutio on and tthe effect that this has on the model). TThe takeawayy of all of this is that i‐Tree e Hydro ccan be a powe erful tool to e estimate streaamflow, rrunoff, and po ollution changges in respon nse to laand cover chaanges. But the outputs are e eestimates, and should be rregarded as su uch. P Pictured on th he right is a m map intended to show tthe spatial refference for th he i‐Tree Hydro w watershed‐scale runs that,, ultimately, w were not u used in this sttudy. Note the spatial variaability of the w weather statio ons as well ass the city bou undary crossin ng m multiple hydrologic boundaries in the area. In red arre tthe selected w weather and sstream gauge e stations that w were used in tthe model testing as well aas the selecte ed ssub‐watershed intended to o represent th he city.

Figure 50: W Weather stations and stream gauges inside ccity limits, with overla apping hydrologic bou undaries

A APPENDIX

REFERENCES Clune, D., Endres, D., Forester, V., McCarthy, K., McCartney, R., Paasche, B., . . . VanderKool, D. (2009). City of Grand Rapids Urban Forestry Plan. Grand Rapids: City of Grand Rapids Urban Forestry Committee. Kuo, E. (2001). Environment and Crime in the Inner City: Does Vegetation Reduce Crime? Environment and Behavior, 343‐367. Leopold, Luna B., and Thomas Dunne. "Water in environmental planning." New York, 818p (1978). McPherson, E. G., Nowak, D., Heisler, G., Grimmond, S., Souch, C., Grant, R., & Rowntree, R. (1997). Quantifying Urban Forest Structure, Function, and Value. Urban Ecosystems, 49‐61. Miller, M. (2015). Battle of the Ash Borer: Decades after Beetles Arrived in Michigan, Researchers Looking to Slow Devastation . Lansing State Journal. Sander, H., Polasky, S., & Haight, R. G. (2010). The Value of Urban Tree Cover. Ecological Economics, 1646‐1656. Shade: Healthy Trees, Healthy Cities, Healthy People. (2004). Urban Forestry South. Retrieved from http://www.urbanforestrysouth.org/

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