New Green Infrastructure Opportunities Research in Detroit Metropolitan Area by Cheng Xing

New Green Infrastructure Opportunity in

Southeast Michigan State Detroit Metropolitan Area

Final Report Document

Cheng Xing NRE 687-001, Fall 2012 December 14, 2012

Content Introduction - Definition ……………………………………………………………………… 1

- Significance …………………………………………………………………... 1 - Scale and components ……………………………………………………… 2 Methodology and Result - Process ……………………………………………………………………….. 4 - Inventory ……………………………………………………………………… 6 - Suitability analysis ………………………………………………………….. 12 Inyo National Forest, U.S.

- Scenario drivers scan - Scenario 1

- Scenario 2 - Scenario 3 - Conclusion of scenario suitability analysis - Allocation ……………………………………………………………………. 33 - Stakeholder Surveys ………………………………………………………. 38 - Evaluation …………………………………………………………………… 39 - Evaluation Metric 1 - Evaluation Metric 2 - Evaluation Metric 3 Conclusion ................................................................................................ 54 Appendix ................................................................................................... 55

Wetland, Finland.

1. Introduction Definition At the scale of a city or county, the Green Infrastructure, as the Environmental Protection Agency (EPA) defines, refers to the patchwork of natural areas that provides habitat, flood protection, cleaner air, and cleaner water.â&#x20AC;? This project researched in both natural and social suitability for building new green infrastructures in the Michigan southeast area, which is the Detroit Metropolitan Region. Such suitability was then projected to three future scenarios based on different emphasis and priority. The suitability and scenario analysis and allocation used ArcMap10 GIS software to create a set of maps showing patterns as design result and recommendations for future development. Significance One of the major goals of green infrastructure is to help guide future land development and land conservation decisions to accommodate population growth and protect and preserve community assets and natural resources. In regional plan scale, green infrastructure design and allocation contribute to formulating a comprehensive approach that addresses the interrelationships among the economy, social equity and environment. For instance, green-way attempts to address these overall concerns through open space (green space) planning and improving natural resource management, thus promote removing air pollution, creating opportunities for recreation, fostering community cohesion, reducing noise and providing wildlife habitat. . Green infrastructure provides strong rationale for funding green space conservation and management by reducing the risk of flooding and contamination thus save a lot of social and environmental costs due to urban sprawl, environmental deterioration and habitat destruction. What is more, the effective network of green infrastructure will also provide a considerable effect of restoring species habitat, increasing wildlife diversity and ensuring more survival of endangered species by a series of approaches including new habitat hubs, corridors and buffers. Fixing and enhancing the existing natural ecosystems (especially those fragmental ones) will not only improve their functions and structures, but also help provide more and better eco-service (clean water and air, energy resources, food and wooden product) to human ecosystem and our society in long time. Finally, green infrastructure contributing to mitigating the release of greenhouse gas due to excessive dependence on automobile transportation system. Apart from social and ecological benefits, small scale and aesthetic green infrastructures also have a strong advantage in providing economic opportunity for increasing property value and employment by adding bioswale, raingarden and aesthetic retention pond, etc. The key here is to embrace cultural aesthetic norms of landscape into ecological and sustainable design to gain more cultural sustainability and appreciation from society thus increase the perceived attraction of properties or real estate that have those small scale infrastructures.

1. Introduction Scale and Components Specifically, content of green infrastructure in this project includes vegetated hubs, wetlands habitats, green or blue corridors, habitat and vulnerable area buffers, step stones, retention/detention infiltration pond (large scale); as well as bioswales, raingardens, green road, green/blue roofs, cisterns and rain barrels (small scale). In terms of study area, extend of this project is 75 miles from west to east and 80 miles from north to south. The whole area occupies 4599 sq. miles (see context map next page). Additionally, the resolution of this project is 100 ft by 100 ft. Therefore, this project mostly research on the large scale green infrastructure and part of the small scales such as bioswales which can be applied for long linear transportation green infrastructure, and big raingardens/infiltration ponds in communities. This project did not consider very small scale green infrastructures including small raingardens, cisterns and rain barrels, which could hardly be considered as ecologically beneficial, or have anything to do with suitability analysis. In summary, there were three components of green infrastructures in this projects:  Habitat hubs. Hubs should be bigger than 250 acres. Although this project did not provide a specification for the required effective habitat areas in terms of different species survival, “250 acres” area is used for a general recommendation given by other researches for being able to contain enough landscape heterogeneity to support effective ecological structures and functions for most of species. Meanwhile, habitats with this area could obtain enough capacity of recovering from succession and disturbance.  Networks. Connectivity is paramount for ensuring flow of energy, species and matter, thus maintaining the critical structures and functions of ecosystems. This project focus on potentials for creating corridors, step stones and buffers that link existing and new hubs.  Pollution treatment network. It consists of 1) linear green infrastructures such as bioswales and infiltration along the major arterials in transportation systems in order to infiltrate contaminant created by automobile, as PAHs, metals and sediments; and 2) treatment ponds and green patches in some pollution “hot spot” including commercial, industrial and dense residential landuse in urban areas and farmlands which contribute much amount of phosphorous and fertilizers to receiving water.

Southeast Michigan State Detroit metropolitan region • • • • • • •

Livingston County Macomb County Monroe County Oakland County St. Clair County Washtenaw County Wayne County

Overall area: 4599 sq. miles Data Time: By the year 2010 Context map

2. Methodology and Result

Process The general process is shown by the following flow chart from left to right. The first step is existing data inventory; The second is to analyze the suitability in three aspect: natural, population and pollution based on inventory map; The third step is to figure out design and patterns of green infrastructure based on different suitability thus get three scenarios. Each scenario consists of two kinds of green infrastructure: Greenway and Wetland restoration; The forth step is to project the scenario suitability maps on land, which refers to future allocation; The final step is to evaluate these three scenarios.

Greenway

Green infrastructure

Wetland restoration

Habitat patch

Evaluation

Allocation 1

Allocation 2

Allocation 3

Scenario 1

Scenario 2

Scenario 3

Natural suitability Population distribution Pollution

Soil hydric Streams Occupancy Vacancy Land use

Transportation

2. Methodology and Result Data Inventory There are five main categories of inventory data in this project: Environment, hydrology, social, development and pollution. Resources of data inventories: 1. Michigan Geographic Data Library (MIGDL) http://www.mcgi.state.mi.us/mgdl/ 2. SEMCOG Map (Data) Catalog http://www.semcog.org/MapCatalog.aspx 3. U.S. Census Bureau TIGER Product http://www.census.gov/geo/maps-data/data/tiger.html 4. American Fact Finder http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml The effective date of data is year 2010. Each main category also had two theme layers which were shown by maps using the following spatial reference: Project coordinate system name: NAD_1983_HARN_StatePlane_Michigan_South_FIPS_2113_Feet_Intl Geographic coordinate system name: GCS_North_American_1983_HARN -The Environmental Inventory includes existing vegetated reserves map, and existing and historic wetland habitat map. The historic wetland map is concluded depending on the information offered by “Vegetation circa 1800, Southeast Michigan” map from SEMCOG Catalog website. Among its 19 vegetation categories, “Black ash swamp”, “Cedar swamp”, “Mixed conifer swamp”, “Mixed hardwood swamp”, “Muskeg/Bog”, “Shrub swamp/Emergent Mash” and “Wet prairie” were considered as historic wetland areas in this project. This inventory played a role in pointing out historic suitability for wetlands habitat restoration. The natural vegetated reserves map was drawn by the information offered by “Recreational and Open Space” map from SEMCOG website. All categories of open space except “Golf course”, “Research area” and “Ski area” were considered as existing vegetated habitat and reserves. The habitat quality is determined by principle that the more forest each patch contains, the higher quality it has and the forest amount information is obtained from Land cover remote map of Michigan Geographic Data Library. Finally, this natural vegetated reserves provided foundation for greenway suitability analysis - The Hydrology Inventory includes existing streams map, and soil hydric condition map. The stream data is extracted from the hydrology data framework on Michigan Geographic Data Library. Soil hydric condition reflect the amount of water contained by the soil thus the suitability for building new wetland. Originally there was no soil hydric map within the study area. Thus the soil data of each county was firstly combined into one map layer and then the hydric information was shown.

2. Methodology and Result Data Inventory -The Social Inventory focus on population distribution reflected by house vacancy/occupancy in each block group of southeast Michigan State. The statistics of vacancy and occupancy of each block group came from American Fact Finder as table format and block group boundary map was downloaded from U.S. Census Bureau TIGER website. Both of the information was joint into one map layer. The vacancy percentage shows number of vacant houses vs. the entire number of houses in each block group. The occupancy map shared the same statistics, but had an inverse rank of categories of “vacancy percentage”. This kind of symbology will reflect the relatively real population concentration and distribution status, in order to provide factors for social suitability analysis of different scenarios.

- The Develop Inventory includes land use map of year 2010, and map of developed land by 2010. Both of the data came from SEMCOG Catalog website. These two maps helped identify available land that could be used for apply new green infrastructure and those “hot spot” with heavy development and thus serious pollution issues for desirability analysis of treatment green infrastructure. -The Pollution Inventory consisted of two maps showing land use pollution and transportation system pollution index. Depending on the landuse map in Develop Inventory, all the landuse types were divided into four categories in terms of pollution effect according to intensity and content of contaminate derived from certain types of landuse. Therefore, the most serious were industrial and airport areas, the second rank included commercial areas, the third were agricultural and multifamily residential areas, and the forth were single family residential areas, which produce the least pollution effect on surrounding land. The transportation map focus on automobile transportation systems and the main arterials, which carry the most traffic load and hence produce huge amount of contaminate such as sediment, metal, PAHs, as well as heat water and trash. There was an assumption that higher level road, which is usually wider, has more pollution effect to surrounding. This road data came from transportation framework data of Michigan Geographic Data Library. Only four categories of main arterials (1st: Interstate, 2nd: Freeway and expressway, 3rd: Principal arterials, 4th: Minor arterials) were selected. Maps in the following pages show patterns and details of inventory result. There are also specifications concerning on each map provided.

2. Methodology and Result

Inventory – Environmental • •

Historic wetland areas are based on 1800 circa data. Existing wetland pattern shows high degree of fragment.

Existing and historic wetland

•

Quality based on dominant vegetation types. Forest shows greatest quality for preserving biodiversity and eco-service.

Natural vegetated reserves

2. Methodology and Result

Inventory – Hydrology •

Streams are considerably vulnerable to non-point pollution and physical impairment thus need to be protected by green infrastructure.

Streams

•

Hydric soil shows amount of water the soil holds in specific area, so that the potential for establishing wetland.

Soil hydric

2. Methodology and Result

Inventory – Social •

Occupancy status is based on the ratio of occupant households versus entire households in each block group.

Occupancy

•

Based on vacant households percentage of entire households in each block group.

Vacancy

2. Methodology and Result

Inventory – Development •

Based on data by 2010.

Land use

• •

Based on developed land data by 2010. Shows available land for the project.

Developed land

2. Methodology and Result

Inventory – Pollution • • • •

Industrial and airport: Suspended sediment, Metal, Petroleum product. Commercial: Sediment, Flow, Metal, Trash Residential: Pesticide, Fertilizer, Farm: Soil erosion, Pesticide, Fertilizer Landuse pollution index

•

Transportation provides high concentration of contaminant and great amount of surface runoff.

Transportation

2. Methodology and Result

Scenario Drivers Scan

Scenario 1

Scenario 2

Scenario 3

•

Creating connections among natural reserves thus improve biodiversity and habitat quality;

•

Balance between development and preserving ecoservice;

•

General and big scale pollution treatment options;

•

Creating new habitat, corridor, buffers; Focus on restoring habitat integrity and continuity;

•

Create buffer, stone steps, patches more ;Focus on limited ecoservice and habitat protection;

•

Not intentionally create new habitat; Protect nature by pollution treatment ;

•

Use vacant land, prevent new development;

•

Incorporate green infrastructure into dense population areas, leave land for development;

•

Add infrastructure on developed land; Need based;

•

May prevent people from getting in;

•

May create recreational potential;

•

No intentional aesthetic and recreational value;

•

Not consider pollution treatment

•

Not consider pollution treatment, but may have some effects;

•

Focus on pollution treatment;

•

Cost high

•

Cost moderately

•

Cost low

•

Environmental Drivers

Social drivers

2. Methodology and Result Scenario 1 – Greenway Potential Analysis Natural vegetated reserves map

•

Post gradual numbers to existing patches according to their habitat quality.

•

Run focal tool to calculate summary numbers within one mile buffer around natural reserves area.

•

Higher number shows higher natural suitability/potential for creating greenway depending on proximity and reserves matrix quality.

2. Methodology and Result

Scenario 1 –Wetland Potential Analysis Soil hydric Weight: 0.3

• •

Restoration potential shows opportunity for creating and restoring wetland based on natural suitability. Map calculation formula: Soil hydric x 0.3 + Wetland effects x 0.3 + Water resource x 0.4

Wetland effects Weight: 0.3

Water resource Weight: 0.4

2. Methodology and Result

Scenario 1 – Greenway Suitability on Vacancy Emphasis Greenway potential Weight: 0.5

Vacancy status Weight: 0.5

• •

Use vacant land to protect and restore vegetation habitat in order to preserve ecological value. Higher vacancy status will shows higher suitability for this aim to prevent them from being developed.

2. Methodology and Result

Scenario 1 – Wetland Suitability on Vacancy Emphasis Wetland potential Weight: 0.5

Vacancy status Weight: 0.5

•

Process of both social emphasis analysis: 1. Use each “potential map” as input raster layer, and block group boundary as zone layer, run zonal statistic to get “Potential” map. 2. Map calculation: Potential map x 0.5 + Status map x 0.5

2. Methodology and Result Scenario 1 – Greenway Detail Patterns Vacancy

Greenway potential

•

This map generalizes both natural suitability and vacancy availability and create a optional recommendation in a relatively small scale.

•

Establishment of greenway such as corridors and buffers might be applied in phases.

2. Methodology and Result Scenario 1 â&#x20AC;&#x201C; Wetland Restoration Detail Patterns Vacancy

Wetland potential

â&#x20AC;˘

Patterns in this scenario shows a more effective approach to restoring the connection (corridors) among existing isolated natural reserves.

2. Methodology and Result

Scenario 2 – Greenway Suitability on Dense Population Emphasis Greenway potential Weight: 0.5

• • •

Occupancy status Weight: 0.5

Tries to bridge areas with high occupancy status and their natural suitability for creating green spaces. Creates recreational potential meanwhile a limited function of improving ecological quality. Same method as it is on page 16.

2. Methodology and Result

Scenario 2 – Wetland Suitability on Dense Population Emphasis Occupancy Weight: 0.5

Wetland potential Weight: 0.5

• • • •

More distributed suitability patterns in each block group depending on where people live. Patches of ponds clustering in each block group. Focus on local eco-protection and recreation. Same method as it is on page 16.

2. Methodology and Result Scenario 2 – Wetland Restoration Detail Patterns Occupancy

Wetland potential

•

Patterns in this scenario might not restore historic wetland system and connection effectively.

•

Create a patch appearance of wetland establishment.

2. Methodology and Result – Scenario 1 and 2 Model Chart

W: 0.3

W: 0.4

Proximity to existing wetland

Soil Hydric

Water resource

Environmental Inventory

Greenway restoration suitability

Existing vegetated reserves

Wetland restoration suitability

Focal : 1mi

W: 0.5

Greenway potential

W: 0.5

Scenario 1

Greenway restoration suitability

Scenario 2

Wetland potential

W: 0.5

Social Inventory

W: 0.5

• Maps show natural suitability for creating / restoring large scale habitats. • Run zonal tool to get suitability in each block group. • Combine natural suitability with land occupancy / vacancy information by map algebra.

Wetland restoration suitability

Vacancy

Occupancy

2. Methodology and Result

Specification of Scenario 1 and 2 Model 1. Use “Natural vegetated reserves” as input; Run “focal statistics”; Set “1 mile” as radius and statistic type as “MEAN”, Get “Greenway potential” as output. 2. Use “Existing and historic wetland” and “Streams” as inputs; Run “focal statistics”; Set “0.5 mile” as radius and statistic type as “MEAN”; Get “Proximity to wetlands” and “Water resource” as outputs. 3. Use “Proximity to wetlands”, “Soil hydric” and “Water resource” as input; Run “Raster calculator” and set formula as: “Proximity to wetlands” x 0.3 + “Soil hydric” x 0.3 + “Water resource” x 0.4; Get “Wetland potential” as output.

4. Use “Greenway potential” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Greenway potential in bg” as output. 5. Use “Wetland potential” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Wetland potential in bg” as output. 6. Use “Vacancy” map and “Greenway potential in bg” as inputs; Run “Raster calculator” and set formula as: “Vacancy” x 0.5 + “Greenway potential in bg” x 0.5; Get “Greenway restoration suitability” as one output of Scenario 1. 7. Use “Vacancy” map and “Wetland potential in bg” as inputs; Run “Raster calculator” and set formula as: “Vacancy” x 0.5 + “Wetland potential in bg” x 0.5; Get “Wetland restoration suitability” as one output of Scenario 1. 8. Use “Occupancy” map and “Greenway potential in bg” as inputs; Run “Raster calculator” and set formula as: “Occupancy” x 0.5 + “Greenway potential in bg” x 0.5; Get “Greenway restoration suitability” as one output of Scenario 2. 9. Use “Occupancy” map and “Wetland potential in bg” as inputs; Run “Raster calculator” and set formula as: “Occupancy” x 0.5 + “Wetland potential in bg” x 0.5; Get “Wetland restoration suitability” as one output of Scenario 2.

2. Methodology and Result

Scenario 3 – Pollution Impact Analysis Transportation

Transportation effect

•

Pollution gradual number bases on width (Level) of single road. Assume that higher level road contribute more pollution.

•

Use focal statistic tools to calculate summary number of each single place within 0.5 mile road buffer.

Land use pollution index

Contamination effect

•

Farmland is also considered as moderate level pollution for great amount of fertilizer, pesticide and sediment.

•

Also use focal tool to calculate pollution effects in 1 mile buffer surrounding pollution land use.

2. Methodology and Result

Scenario 3 – Vegetation Road Buffer Desirability Greenway potential Weight: 0.25

Transportation buffer Weight: 0.75

•

Remain value of greenway potential within transportation buffer boundary; Then use map calculation by formula:

•

Thus determine areas that most need green infrastructure to both buffer road pollution and maintain forest habitat.

Potential x 0.25 + Transportation buffer x 0.75

2. Methodology and Result

Scenario 3 – Wetland Road Buffer Desirability Wetland potential Weight: 0.25

• • •

Transportation pollution status Weight: 0.75

Combine pollution status and effects within road buffer and natural suitability (vulnerability) of wetland restoration. Thus determine areas that need treatment pond to both dispose pollution and protect aquatic habitat. Same process as it is on the previous page

2. Methodology and Result

Scenario 3 – Infrastructure Desirability in Block Groups Wetland potential Weight: 0.2

•

Greenway potential Weight: 0.2

Land use pollution concentration Weight: 0.6

Though pollution concentration and effects of different land use is the most important driver to determine green infrastructure, natural suitability is still considered to ensure effectiveness of treatment . Map calculation first, then use zonal statistic

2. Methodology and Result Scenario 3 – Green Infrastructure Detail Patterns: Farmland • Treatment ponds or/and vegetation buffer suitability pattern in farmland near stream and estuary. • It shows a scattered pattern in farmlands around stream.

2. Methodology and Result Scenario 3 â&#x20AC;&#x201C; Green Infrastructure Detail Patterns: Commercial and Industrial Area â&#x20AC;˘ Treatment ponds or/and vegetation buffer suitability pattern in industrial and commercial areas where great urban runoff pollution issues happens most. â&#x20AC;˘ It shows higher necessity and suitability to converge treatment ponds and green spaces around industrial and commercial land, especially near stream and natural reserves.

2. Methodology and Result – Scenario 3 Model Chart

W: 0.3

W: 0.4

Proximity to existing wetland

Soil Hydric

Water resource

Environmental Inventory

W: 0.3

Existing Vegetation Area

Infrastructure suitability

• Vegetation buffer • Pond / Wetland

Focal : 1mi

• Natural suitability shows convenience for green infrastructure. • Calculate overall suitability based on natural process and pollution. • Initially divide into large and small infrastructure based on pollution types.

Small scale

Wetland potential

Pollution Inventory

Greenway potential

Large scale

Contamination effect

Transportation effect

Vege buffer desirability

• Bio-swale • Rain garden • Green road

Wetland buffer desirability

2. Methodology and Result

Specification of Scenario 3 Model 1. Use “Select by attribute” to select “Airport”, “Industrial”, “Commercial”, “Agriculture”, “Multifamily” and “Single family” from “Land use” data attribute table; Export the selected features as a new shapefile named as “Pollution landuse”; Add a new field “Pollution” to “Pollution landuse” attribute table; Select “Airport” and “Industrial” landuse in attribute table, then use “Field calculator” to post value “4” to their “Pollution” field; Use the same process to get “3” in “Pollution” field of “Commercial” land use, “2” in “Pollution” field of “Multifamily” and “Agriculture” land use, “1” in “Pollution” field of “Single family” landuse in attribute table of “Pollution landuse” shapefile; Use “Pollution landuse” map as inputs; Run “Feature to raster” and set “Pollution” as conversion field; Get “Landuse pollution index” as output.

2. Use “Landuse pollution index” as input; Run “focal statistics”; Set “1 mile” as radius and statistic type as “MEAN”; Get “Contamination effect” as output. 3. Use “Contamination effect”, “Greenway potential” and “Wetland potential” as inputs; Run “Raster calculator” and set formula as: “Contamination effect” x 0.6 + “Greenway potential” x 0.2 + “Wetland potential” x 0.2; Get “GI suitability of pollution” as output. 4. Use “GI suitability of pollution” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Infrastructure suitability” as large scale output of scenario 3. 5. Use “Transportation” feature data as input; Run “Buffer”; Set “0.5 mile” as “Distance”; Get “Road buffer” as output; Use “Reclassify” to convert values of all cells outside buffer to “Nodata” and values of cells inside buffer to “0”; Get “Road boundary” raster output. 6. Add a new field “rd pollution” to attribute table of “Transportation” layer; Use “Field calculator” to post “4” to “rd pollution” of “Interstate”, “3” to “rd pollution” of “Freeways and expressways”, “2” to “rd pollution” of “Principal arterial”, “1” to “rd pollution” of “Minor arterial”; Use “Transportation” layer as input; Run “Feature to raster” and set “rd pollution” as conversion field; Get “Rd pollution” output. 7. Use “Rd pollution” as input; Run “Focal statistics”; Set “0.5 mile” as radius and statistic type as “MEAN”; Get “Transportation effect” as output. 8. Use “Greenway potential”, “Transportation effect” and “Road boundary” as inputs, Run “Raster calculator” and set formula as: Greenway potential” x 0.25 + “Transportation effect” x 0.75 + “Road boundary”; Get “Vege buffer desirability” as one of the small scale output of scenario 3.

2. Methodology and Result

Specification of Scenario 3 Model (Continuous) 9. Use “Wetland potential”, “Transportation effect” and “Road boundary” as inputs, Run “Raster calculator” and set formula as: Wetland potential” x 0.25 + “Transportation effect” x 0.75 + “Road boundary”; Get “Vege buffer desirability” as the other one of the small scale output of scenario 3. Conclusion of scenario suitability analysis According the analysis and ArcMap10 software process. Each scenario consists of a couple of maps showing both suitability and potential patterns of greenway and wetland restoration focusing on different aspects. In one word, scenario 1 tries to provide a ecological solution for increasing biodiversity and habitat quality through out the study area and limiting urban sprawl, while scenario 2 balances request of development and necessity of fixing fragmental natural ecosystem. Scenario 2 also looks for the recreational opportunity provided by small habitat patches that can fit into community and some dense residential areas, to increase both ecological and social value of green infrastructures. Scenario 3 probably has a big difference from scenario 1 and 2. It pays more attention to the pollution treatment function of green infrastructures and identifying those “hot-spot” areas with heavy pollution already or such potential, such as industrial and commercial areas, as well as agricultural land. The suitability of green infrastructure in scenario 1 and 2 is illustrated by block groups with a range of both value (0-4) and colors in terms of two aspects: greenway and wetland. Thus each scenario include two suitability maps: Greenway restoration suitability and Wetland restoration suitability. Scenario 3 includes large scale green infrastructure desirability in each block groups for area pollution treatment, which is displayed by one map with range of color and value; and the linear green infrastructures desirability for transportation pollution, which is also displayed in two categories, as the same way of scenario 1 and 2. Therefore, Scenario has three maps as final analysis result: Treatment green infrastructure desirability analysis in block groups, Vegetation road buffer desirability and Wetland road buffer desirability

2. Methodology and Result

Allocation • Use rules to combine natural suitability map (natural shape pattern) and population information (block groups) to get the suitability map showing both pattern property and trends of allocation. • Clip out those developed land and get the assumed future map.

Scenario 1 Allocation_1

Scenario 2 Allocation_2

•

Creating connections among natural reserves thus improve biodiversity and habitat quality;

Balance between development and

•

Create buffer, stone steps, patches

Creating new habitat, corridor, buffers;

more; Focus on limited eco-service and

•

Focus on restoring habitat integrity

habitat protection.

•

preserving eco-service.

•

and continuity;

Scenario 3 Allocation_3

•

dense population areas, leave land for

development.

development; •

treatment options. •

May create recreational potential.

Not intentionally create new habitat. Protect nature by pollution treatment.

•

Incorporate green infrastructure into

Use vacant land and impact new

General and big scale pollution

Add infrastructure on developed land; Need based;

•

Focus on pollution treatment and improve property living quality.

2. Methodology and Result

Specification of Allocation and Scenario Refining Reclassification preparation 1. Use “Vacancy” layer as input, run “Reclassify” to convert original four ranks values into: 10, 20, 30, 40. Higher number represents higher vacancy status on map. Name the output raster layer as “VacancyRec”. 2. Use “Occupancy” layer as input, run “Reclassify” to convert original four ranks values into: 10, 20, 30, 40. Higher number represents higher occupancy status on map. Name the output raster layer as “OccupancyRec’. 3. Use “Transportation effect” layer as input, Run “Reclassify” to convert original four ranks of values into: 10, 20, 30, 40. Higher number represents higher impact due to traffic pollution. Name the output as “RoadPollutionRec”. 4. Use “Contamination effect” layer as input, Run “Reclassify” to convert original four ranks of values into: 10, 20, 30, 40. Higher number represents higher impact due to landuse pollution. Name the output as “LandusePollutionRec”. 5. Use “Greenway potential” layer as input, Run “Reclassify” to convert original four ranks of values into: 1, 2, 3, 4. Higher number represents higher natural suitability for greenway restoration. Name the output as “GreenwayRec”. 6. Use “Wetland potential” layer as input, Run “Reclassify” to convert original four ranks of values into: 1, 2, 3, 4. Higher number represents higher natural suitability for wetland restoration. Name the output as “WetlandRec”. 7. Use “Developed land” layer as input, Run “Reclassify” to convert values of cells in “Developed areas” to “Nodata”, values of cells outside “Developed area” to “0”. Name the output as “LandLimit”.

Scenario 1 Rules of combination 1. Raster calculator: “VacancyRec” + “GreenwayRec”; Name output as “1Greenway_bg”. 2. Raster calculator: “VacancyRec” + “WetlandRec”; Name output as “1Wetland_bg”. 3. Reclassify “1Greenway_bg”: (14, 23) -> 1, (24, 33) -> 2, (34, 43, 44) -> 3. Convert other values to “0”. Name the output as “1GreenwayPriority”. Higher values represent higher priority of building greenway infrastructure in vacant land. 4. Reclassify “1Wetland_bg”: (14, 23) -> 10, (24) -> 20, (33, 34, 43, 44) -> 30. Convert other values to “0”. Name the output as “1WetlandPriority”. Higher values represent higher priority of building wetland infrastructure in vacant land.

2. Methodology and Result Specification of Allocation and Scenario Refining (Continuous) Scenario 1 rules of combination (Continuous) 5. Raster calculator: “1GreenwayPriority” + “1WetlandPriority”; Name output as “Alloc1”. 6. Reclassify “Alloc1”: (2, 3, 12, 13) -> 1, (11, 22, 23, 32, 33) -> 2, (20, 21, 30, 31) -> 3. Convert other values to “0”. Name the output as “Landcover1”. 7. Raster calculator: “Landcover1” + “LandLimit”; Name output as “Allocation_1”. Scenario 2 Rules of combination 1. Raster calculator: “OccupancyRec” + “GreenwayRec”; Name output as “2Greenway_bg”. 2. Raster calculator: “OccupancyRec” + “WetlandRec”; Name output as “2Wetland_bg”. 3. Reclassify “2Greenway_bg”: (23, 24) -> 1, (33, 34) -> 2, (43, 44) -> 3. Convert other values to “0”. Name the output as “2GreenwayPriority”. Higher values represent higher priority of building greenway infrastructure in dense population areas. 4. Reclassify “2Wetland_bg”: (23, 24) -> 10, (33, 34) -> 20, (43, 44) -> 30. Convert other values to “0”. Name the output as “2WetlandPriority”. Higher values represent higher priority of building wetland infrastructure in dense population areas. 5. Raster calculator: “2GreenwayPriority” + “2WetlandPriority”; Name output as “Alloc2”. 6. Reclassify “Alloc2”: (3) -> 1, (33) -> 2, (30) -> 3. Convert other values to “0”. Name the output as “Landcover2”.

7. Raster calculator: “Landcover2” + “LandLimit”; Name output as “Allocation_2”. Scenario 3 Rules of combination 1. Raster calculator: “RoadPollutionRec” + “GreenwayRec”; Name output as “3Greenway_rd”. 2. Raster calculator: “RoadPollutionRec” + “WetlandRec”; Name output as “3Wetland_rd”. 3. Raster calculator: “LandusePollutionRec” + “GreenwayRec”; Name output as “3Greenway_lu”. 4. Raster calculator: “LandusePollutionRec” + “WetlandRec”; Name output as “3Wetland_lu”.

2. Methodology and Result

Specification of Allocation and Scenario Refining (Continuous) Scenario 3 rules of combination (Continuous) 5. Reclassify “3Greenway_rd”: (20, 21, 22) -> 1, (23, 24) -> 2, (30, 31, 32, 40, 41) -> 3, (33, 34, 42, 43, 44) -> 4. Convert other values to “0”. Name the output as “VegePriority_rd”. Higher values represent higher priority of building vegetated infrastructure in areas with high pollution impact from traffic. 6. Reclassify “3Wetland_rd”: (20, 21) -> 10, (22, 30) -> 20, (23, 24, 31, 32, 40, 41, 43) -> 30, (33, 34, 42, 44) -> 40. Convert other values to “0”. Name the output as “WetPriority_rd”. Higher values represent higher priority of building wetland infrastructure in adjacent areas with high pollution impact from traffic. 7. Reclassify “3Greenway_lu”: (23) -> 1, (24) -> 2, (33, 34) -> 3, (43, 44) -> 4. Convert other values to “0”. Name the output as “VegePriority_lu”. Higher values represent higher priority of building vegetated infrastructure in large areas with high pollution impact from adjacent land use. 7. Reclassify “3Wetland_lu”: (24, 30) -> 10, (31, 32) -> 20, (33, 40, 41, 42) -> 30, (34, 43, 44) -> 40. Convert other values to “0”. Name the output as “WetPriority_lu”. Higher values represent higher priority of building vegetated infrastructure in large areas with high pollution impact from adjacent land use. 8. Raster calculator: “VegePriority_rd” + “WetPriority_rd”; Name output as “Alloc3_rd”. 9. Raster calculator: “VegePriority_lu” + “WetPriority_lu”; Name output as “Alloc3_lu”. 10. Reclassify “Alloc3_rd”: (3, 4, 13, 14, 23, 24) -> 1, (33, 34, 43, 44) -> 2, (30, 31, 32, 40, 41, 42) -> 3. Convert other values to “0”. Name the output as “3Landcover_rd”. 11. Reclassify “Alloc3_lu”: (3, 4, 23) -> 10, (33, 34, 43, 44) -> 20, (13, 30, 40) -> 30. Convert other values to “0”. Name the output as “3Landcover_lu”. 12. Raster calculator: “3Landcover_rd” + “3Landcover_lu”; Name output as “Alloc3”. 13. Reclassify “Alloc3”: (1, 11) -> 1, (2, 12, 13, 21, 31) -> 2, (3, 22, 23, 32, 33) -> 3, (10) -> 4, (20, 30) -> 5. Convert other values to “0”. Name the output as “Landcover3”. 14. Raster calculator: “Landcover3” + “LandLimit”; Name output as “Allocation_3”.

2. Methodology and Result

Specification of Allocation and Scenario Refining (Continuous) Symbology of allocation map of three scenarios Label

Allocation_1

Allocation_2

Allocation_3

Greenway

Raingarden

Bioswale

Wetland/Pond

Raingarden

Value

Vegetation Buffer

Wetland/Pond

The values and labels here represent the numbers in text with red colors in each scenario rules of combination.

2. Methodology and Result

Stakeholder Survey and Goals • Stakeholders rank Goals of three categories based on their background. 1 is the highest score, 8 is the lowest. • Red represents concerned ones, while green represents the not concerned ones, yellow indifferent.

Brief summarization of stakeholders survey on green infrastructure goals

•

The most concerned objectives given by stakeholders are “Maintain natural process and eco-service” (Environmental) and “Provide eco-tourism and conventional recreation” (Social)

•

Red blocks indicate the “most important” type of goals chosen form three categories, while the green ones show the least priority concerned by each stakeholders represent. Overall, the social goals gains the most attention, then comes the environmental goals.

•

Environmental Objective Metric: 1. Potential connectivity created by green infrastructure for existing habitat; 2. Effectiveness of new patches for supporting enough eco-service and biodiversity.

•

Social Objective Metric: 1. Potential for increasing property value to financially support future recreation development.

2. Methodology and Result – Evaluation 1: Connectivity to Existing Habitat Scenario 1 Existing habitat * 1mi radius buffer

Landuse allocation * Average connection

• •

Assumption: 1 mile is the shortest distance for most of species gaining enough connectivity to survive. Run zonal tools to show the average connectivity of each new patch to the existing habitats.

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 1: Connectivity to Existing Habitat Scenario 2 Existing habitat * Focal: 1mi

Landuse allocation * Zone input

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 1: Connectivity to Existing Habitat Scenario 3 Existing habitat * Focal: 1mi

Landuse allocation * Large scale as Zone input

â&#x20AC;˘

Assumptions: Select large scale green infrastructure (pond/wetland) as concerned zones, for linear green infrastructures (bioswale, green road, small raingarden) could hardly serve as species habitat.

2. Methodology and Result – Evaluation 1: Connectivity to Existing Habitat Scenario 1

Scenario 2

Scenario 3

High connectivity patch: red Distance < 0.4 mi

Future 1

Future 2

Future 3

Percentage of high connectivity patches

14.5% - (2561/17667)

8.9% - (1242/13929)

4.1% - (519/12640)

Total area of high connectivity patches

192.1 sq. mi

68.9 sq. mi

16.7 sq. mi

Area of the biggest patch with high connectivity

29.6 sq. mi

5.3 sq. mi

1.1 sq. mi

• Table shows statistics of new green infrastructure patches with high connectivity to existing habitats (red). • First scenario has the best result of providing potential connections or corridors for existing habitats, especially the existing forests, stream, wetlands areas.

2. Methodology and Result – Evaluation 1: Connectivity to Existing Habitat

Specification of Evaluation 1 Process 1. Use “Natural vegetated reserves” as input; Run “Feature to raster”, use “FID” as conversion field, set up the resolution as “100ft, 100ft”; Name the output raster as “Reserve1”. Use “Reserve 1” as input, run “Reclassify”, convert “0” to “1”, “Nodata” to “0”; Name the output as “Reserve2”. The same process to “Existing wetland”, name the output as “Reserve3”. 2. Raster calculator: “Reserve2” + “Reserve3”, name the output as “Reserve4”. Use “Reserve4” as input, run “Reclassify” to convert “2” and “1” to “1”, “0” to “0”. Name the output as “Existing habitat”. 3. Use “Existing habitat” as input; Run “focal statistics”; Set “1 mile” as radius and statistic type as “MEAN”; Get “ProximityHabitat” as output. 4. Use “Allocation_1” as input; Run “Raster to polygon”, name the output as “1AlloBoundary”. The same process to “Allocation_2” and “3Landcover_lu”, get “2AlloBoundary”, “3AlloBoundary”. 5. Use “ProximityHabitat” as input raster value data; Use “1AlloBoundary” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Connectivity_1” as output. The same process to “2AlloBoundary” and “3AlloBoundary”, get “Connectivity_2” and “Connectivity_3”. 6. Use “Connectivity_1” as input; Run “Raster to polygon”, set “Value” as conversion field; Get “Connecttivity1” as output shapefile. The same process to “Connectivity_2” and “Connectivity_3”, get “Connectivity2” and “Connectivity3” shapefiles.

2. Methodology and Result – Evaluation 2: Effectiveness of Habitat Scenario 1 Core area

•

* 0.5 mi edge

•

Connectivity

Assumption: Habitat effectiveness is affected by 1) connectivity to existing ones and 2) core areas inside edges. Show simultaneously these two attribute of new green infrastructure patches.

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 2: Effectiveness of Habitat Scenario 2 Core area * >250 ac, 0.5 mi edge

Connectivity

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 2: Effectiveness of Habitat Scenario 3 Core area * >250 ac, 0.5 mi edge

Connectivity

2. Methodology and Result – Evaluation 2: Effectiveness of Habitat Scenario 1

Scenario 2

Scenario 3

High effectiveness patch: red Future 1

Future 2

Future 3

Numbers of high priority patches

Area of the biggest high priority patch

17.5 sq. mi

3.3 sq. mi

0.8 sq. mi

Total area of high priority patches

87.7 sq. mi

29.8 sq. mi

1.6 sq. mi

• High effectiveness: Areas which are bigger than 1500 acres (dark green) and have high connectivity (dark red). • Future 1 allocation map has the most patches with high effectiveness of habitat. It also has larges areas of effective habitat new patches. These patches also serve as high priority for green infrastructures.

2. Methodology and Result – Evaluation 2: Effectiveness of Habitat

Specification of Evaluation 2 Process 1. Add a new field “area” to “1AlloBoundary” attribute table, set “Float” as statistic type; Run “Acre [US]” of “Calculate geometry” to the field “area”; Use “Select by attribute” to select features from “1AlloBoundary” attribute table: “area” >= 250; Export the selected features as “hab250_1” shapefile. Then do the same process to “2AlloBoundary” and “3AlloBoundary”, get “hab250_2” and “hab250_3” as outputs. 2. Use “hab250_1” as input; Run “Buffer”, set “- 500 ft” as distance, do NOT dissolve; Name output as “Core_1”. Then do the same process to “hab250_2” and “hab250_3”, get “Core_2” and “Core_3” as output raster layers. Then divide “Core_1”, “Core_2” and “Core_3” into four categories as legend “Core Area” shows in the map. 3. Run “Zonal Statistic as Table”: Use “Core_1” as “input zone data”, set “FID” as “Zone field”, use “Connectivity_1” as “input value raster”, name the output table as “1core_connect”; Do the same process to “Core_2”, “Connectivity_2” and “Core_3”, “Connectivity_3”, get “2core_connect” and “3core_connect” as output tables. 4. Run “Join” to “Core_1”, use the table “1core_connect”, set the shared field as “FID”; Then run “Select by attribute” from attribute table of “Core_1”: area > 1500 acres AND 1core_connect > 1.3; Export the selected features as “CorePrio1”. Do the same process to “Core_2”, “2core_connect” and “Core_3”, “3core_connect”, get “CorePrio2” and “CorePrio3” as output shapefiles. 5. Use “CorePrio1” as input; Run “Buffer”, set “500 ft” as “distance”; Name output as “Priority1”. Do the same process to “CorePrio2” and “CorePrio3”. Get “Priority2” and “Priority3”.

2. Methodology and Result – Evaluation 3: Increasing Property Value Scenario 1 Block group potential

• •

Land use

Make assumptions of the degree by which different types of green infrastructure could affect on increasing property value in each block group. Filter these potential by residential, commercial and industrial landuse.

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 3: Increasing Property Value Scenario 2 Block group potential

Land use

2. Methodology and Result â&#x20AC;&#x201C; Evaluation 3: Increasing Property Value Scenario 3 Block group potential

Land use

2. Methodology and Result – Evaluation 3: Increasing Property Value Scenario 1

Scenario 2

Scenario 3

Highest potential for value increasing: Dark brown

Future 1

Future 2

Future 3

Overall area

19822.2 ac

23465.9 ac

26783.1 ac

Area of residential landuse

17274.9 ac

21169.7 ac

22065.6 ac

Area of commercial landuse

699.4 ac

529.9 ac

1727.9 ac

Area of industrial landuse

1847.9 ac

1766.2 ac

1909.4 ac

• The table summarizes the HIGH potential of property value increasing (Dark brown areas) of residential, commercial and industrial landuse. • The third scenario owns the highest potential effect on increasing property value in developed urban area.

2. Methodology and Result – Evaluation 3: Increasing Property Value

Specification of Evaluation 3 Process 1. Assumption and analysis of perceived attraction of green infrastructure within block groups: - Allocation 1. Use “Allocation_1” as input; Run “Reclassify”: 1 -> 1, 2 -> 3, 3 -> 2. Name the output as “Perception1”. Then run “Zonal statistics”: Use “Perception1” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Potential_1” as output for scenario 1 evaluation. - Allocation 2. Use “Allocation_2” as input; Run “Reclassify”: 1 -> 1, 2 -> 3, 3 -> 2. Name the output as “Perception2”. Then run “Zonal statistics”: Use “Perception2” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Potential_2” as output for scenario 2 evaluation. - Allocation 3. Use “Allocation_3” as input; Run “Reclassify”: 1 -> 1.8, 2 -> 1.2, 3 -> 3.0, 4 -> 0.6, 5 -> 2.4. Name the output as “Perception3”. Then run “Zonal statistics”: Use “Perception3” as input raster value data; Use “Vacancy” map as input zone data, set “FID” as zone field; Set “MEAN” as statistic type; Get “Potential_3” as output for scenario 3 evaluation. Many researches have pointed out that green infrastructure, especially those which have a better aesthetic value to people such as raingardens and greenway, also has a great potential and effect on increasing property value. This evaluation owns its unique significance in study area of this project for the property value of Detroit Metropolitan Area has been fluctuating and at a low level for decades. The essence here is to help green infrastructure gain as much “cultural sustainability” as possible through some critical features that can be perceived attractive. Two of the most important principles are “Open water” and “Naturalistic vegetation design”. Therefore, raingardens, wetlands and bioswale are assumed to gain a better perceived attraction than pure greenway, thus obtain a higher capacity of increasing property value. 2. “Select by attribute”: “Landuse” = “Commercial” OR “Landuse” = “Industrial” OR “Landuse” = “Multifamily” OR “Landuse” = “Single family”; Export selected features as “EffectedLand” shapefile. 3. Run “Zonal Statistic”: Use “EffectedLand“ as “input zone data”, set “FID” as “Zone field”, use “Potential_1” as “input value raster”, name the output table as “IncreaseProperty_1”; Do the same process to “Potential_2” and “Potential_3”, get “IncreaseProperty_2” and “IncreaseProperty_3” as output tables. 4. Use “IncreaseProperty_1” as input raster data; Run “Raster to polygon”, set “Value” as conversion field; Get “1IncreaseProperty” shapefile. Do the same process to “IncreaseProperty_2” and “IncreaseProperty_3”, get “2IncreaseProperty” and “3IncreaseProperty”.

3. Conclusion

Limitation 1. The evaluation of property value capacity of green infrastructure only shows a potential effect on the relevant land use of industrial, commercial and residential. The effect on agriculture land is not considered. The original property value of each parcel is not considered. There are many vacant parcels in the areas which are already identified as “Residential”, “Commercial” or “Industrial” in Detroit urban district. There might be a priority of urban redevelopment and a request for increasing property value of developed area. 2. Allocation and scenario refining are based on a series assumptions of choosing the areas with both high natural suitability and high social priority. This prediction of future allocation is not precise but only shows a recommended possibility according to different emphasis in each scenario. Even those block groups with the highest vacancy might not have available lands to construct green infrastructure for many other reasons. 3. The suitability analysis of scenario two only consider the population distribution information. In terms of recreational opportunity, there are many other factors not being considered in this project, such as accessibility, capital input, market, as well as infrastructures. Additionally, evaluation selected to achieve this goal is “Property value increase”, which will not has a directly promotion to achieving that goal. The possible significance of this evaluation is that attracting more resident could probably provide more local consumption support and service human resource for recreational market development. Future According to the evaluation. Scenario 1 has the best effect on fixing fragmental existing ecosystem and maintain natural ecological integrity since it provide the most effective habitat hubs and connectivity in necessary land. Scenario 2 has a balanced effect on development and environmental protection. It also has a special improvement on creating recreational opportunity by putting small scale green infrastructures in dense population areas. Scenario 3 has the advantage to increasing property value by its unique linear green infrastructure. Therefore, it might be better to select patches adjacent to existing natural reserves from scenario 1 to increase ecological benefits; small scale patches adjacent to residential areas to provide better recreational resources for people and cultural sustainability for green infrastructure with ecological design; as well as linear green infrastructure from scenario 3 to provide a better solution to contamination treatment and aesthetic value of streets and roads thus create a potential economic increasing point by rising property value. Therefore, recommended method is to select and generalize the three scenarios into one, which combines advantage of all scenarios in order to obtain the maximum benefit and meet different requests.

4. Appendix â&#x20AC;&#x201C; original result sheets of stakeholders survey Landscape Architects