/WorkshopMarc811-6e10

Predictive Vegetation Modelling & Mapping for 6e10 D. Puric-Mladenovic Information Management & Spatial Analysis Unit Southern Science and Information March -08-2011

Predictive Vegetation Modelling • How was it developed?

• How can it be used?

• Limitations

How was it developed?

• Sampled – – – –

6e10 and St.L. NP Bruce NP and Peninsula Kawartha PP, Trent-Severn Waterway

• Sampling continues – Eco-districts: 6e15, 6e11, 6e12 – Niagara Escarpment Planning Area

6e10-GPE • 1080 field plots • Over 100 wetland plots • Photo-plots – pitch pine – open rock-barren sites

Field plot distribution in 6e10-GPE

http://www.forestry.ut oronto.ca/imsa/VSP

Plot based sampling

Site-groups mapped by modeling then classification Species

Classification Species-groups mapped by modeling then classification

Modeling

Vegetation Data

Classification

Site-groups mapped by classification then modeling

Site-group Modeling Species-group

Species-groups mapped by classification then modeling

6e10 Modeling Products • It is a digital atlas – ~ 180 vegetation and habitat maps

PVM – 6E10 SLNP How can it be used?

• One map / product or class is not enough • Support multiple uses and applications • Use field data today , but also in the future • Integrate field data with spatial technology (e.g., higher resolution remote sensing data) • Get more, if not for less but for the same amount of $.

Climate change Wildlife and SAR Goods and services

Invasive species

VEGETATION MAPS

Vegetation management

Plant species composition

Significant woodlands and wetlands

NHS

How this mapping differ from the traditional vegetation mapping?

Traditional mapping

PVM mapping

+

How this differ from the traditional vegetation mapping?

PVM Mapping outcomes

Wetlands Classes

Vegetation

Forest

Habitat

Types/ classes

structure

characteristics

Trees and shrubs

ELC classes

Mature forest

Soil moisture

Ground veg.

Silvicultural Cl.

Tree abundance

Species

Species

assemblages

6e10-GPE area has over 180 vegetation and habitat maps

Rock-barren Canopy closure

Some habitat requirements for forest dwellings SAR

?

SAR

• Do we have sufficient spatial information to support SAR protection and recovery planning across landscapes? • Can we target specific areas for SAR sampling?

6e10 -SAR modeling • Typha marsh

– Evaluated wetlands for modeling – Field data for validation

Wetland classes Probability of typha marsh

6e10 -SAR modeling • Canopy closure • Forest maturity • Silvicultural classes

Five-lined Skink (Eumeces fasciatus)

Rock-barren

Sustaining What We Value • Vegetation is used as a surrogate for overall landscape biodiversity • Vegetation mapping enables wildlife and SAR habitat modeling and mapping • Vegetation mapping supports estimates of ecological goods and services • Vegetation mapping provides necessary input for defining and setting various functional targets.

NHS application - Representation

• Vegetation classes are used as conservation features • For each specific targets (in ha) is set

Emerald Ash Borer - Agrilus planipennis

Emerald Ash Borer - Agrilus planipennis

White Ash abundance 0-40%

Soil moisture

Habitat characteristics

Basal area and stand density

Basal area

Predicted biomass and carbon Predicted Biomass & carbon

• Biomass estimates – Productivity, hydrology, wildlife, • Standing carbon estimates • Carbon uptakes estimates* • Carbon credits – Pilot project in eastern Ontario – EOMF, NCC ttp://www.forestry.utoronto.ca/imsa/ BruceBiomass/index.html

Limitations

Modeling issues • Spectral information – Landsat (spring, summer, fall)

• Modeling: species first – Statistically not possible to separate large number of different vegetation classes – Modeling veg. class – yielded more than 65% misclassification error rate

• Classification continues to evolve and change

Sampling confusion • Classifying rock barren, savannah, woodland or forest • Fuzzy boundary (threshold) Open

Savannah

Woodlands

Forest

< 10%

10 to 34%

35 to 59%

60 % to 100%

Solution Collect base information

Not modeled and mapped • • • • •

Meadows Abandoned fields Disturbed vegetation Successional shrub and other communities ……

• Solution - sample this vegetation too – We do not need classification to start sampling

Vegetation mapping perception • • • •

Lines are more accurate pixel Interpretation is more accurate than modeling Pixels are “jaggy” Validation and errors – Not knowing vs. knowing

• Species assemblages ( veg. types) are stable • Looking for wrong vs. right • Scale of application

Running the Numbers: An American Self-Portrait (2006 - 2009)

Running the Numbers: An American Self-Portrait (2006 - 2009)

Running the Numbers: An American Self-Portrait (2006 - 2009)

Running the Numbers: An American Self-Portrait (2006 - 2009)

Running the Numbers: An American Self-Portrait (2006 - 2009)

Cans Seurat, 2007 60x92" Depicts 106,000 aluminum cans, the number used in the US every thirty seconds Running the Numbers: An American Self-Portrait (2006 - 2009)

Validation • Field validation ( and independent data set) • Every field visit is an opportunity to collect field data – For validation – But also to enhance the inventory • “A database of validation efforts over the years which may have helped refine the quality of the product beyond pure stats” (Greg Saunders, SLNP)

Climate change Wildlife and SAR Goods and services

Invasive species

VEGETATION MAPS

Vegetation management

Plant species composition

Significant woodlands and wetlands

NHS

Diverse products

Diverse applications NHS

SAR

Biodiversity

SOR

Invasive sp.

Forest mng.