## Imagine the result

Habitat Classification for Ecological Risk Assessment Using Aerial Photography and GIS Data in a Two-stage Expert System January 11, 2011

## Background

• Ecological Habitat Classification
– Used to drive modeling of exposure of different animals to hazardous chemicals • Input data
– New York digital orthophotography
– Soils data
– Tax parcel data • Output
– Land cover map
– 15 land cover classes
– Minimum mapping unit of ~ 1/20 acre – 150 square miles / 173 MB

## Exposure Modeling

• How much exposure to a chemical or agent is an animal likely to experience?
– Requires knowing levels of chemical and where they are located
– Also requires knowing where a given animal is likely to spend its time • Many methods of modeling animal range
– Generally require locating useful habitat

## Input Data

## Input Data

## Input Data

## Imagery Challenges • Typical Issues
– High spatial resolution presents challanges • Particular problems with this imagery
– Time of year
– Compression

## Pre- processing • Imagery segmented into spectrally similar clusters • Similarity is determined by both intensity of reflectance and spatial proximity • First step in thinking in object- based rather than pixel - based terms

## Pre- processing • Min, max, mean, and standard deviation calculated for each band and segment • Image to left is mean for near- infrared
– Water is very low (note that two segments have similar mean)
– Vegetation higher

## Pre- processing • Min, max, mean, and standard deviation calculated for each band and segment • Standard deviation in near- infrared shown to left • Objects with low variability in spectral response are dark

## Pre- processing • Similar band - based statistics calculated based on parcels • Object bounds here are man - made rather than image -driven

## Pre- processing • Hydric soils differentiated from others

## Pre- processing • Parcels defined by acreage • Large acreage has “hot” colors • Note that river has zero acreage as far as tax rolls are concerned

## Bringing Data Together • Two-stage expert system – Everything converted to raster – Classified pixel - bypixel (2 -foot pixel) based on set of rules

## Post- processing • Initial classification generalized to remove clusters smaller than 1/20 acre • This fixes boundaries of final map polygons, but classes are still subject to change

## Post- processing • Initial classification generalized to remove clusters smaller than 1/20 acre • This fixes boundaries of final map polygons, but classes are still subject to change

## Second Stage Expert System• Final land cover determined by second set of rules, determined by proportions of pixel classes within each larger polygon • More detailed classes, such as pasture versus row crop, determined at this point

## Second Stage Expert System • Raw Imagery

## Second Stage Expert System • Stage 1 classes

## Second Stage Expert System • Generalized polygons

## Second Stage Expert System • Agriculture Pixels
– General polygons shown in red
– Pixels classed as agriculture shown as green, everything else gray

## Second Stage Expert System • Deciduous Pixels
– General polygons shown in red
– Pixels classed as deciduous trees shown as green, everything else gray

## Second Stage Expert System • Evergreen Pixels
– General polygons shown in red
– Pixels classed as Evergreen trees shown as green, everything else gray – Note intermixing of pixels called deciduous vs. evergreen

## Second Stage Expert System • Final Map

## Results

• Broad classes (agriculture vs. wetland vs. evergreen, etc) – 81% overall accuracy • For detailed classes (row crop vs. pasture or open evergreen vs. closed evergreen etc.) – 73% overall accuracy by area
– Only 47% accuracy by polygon
– Small polygons tended to be problematic (shadows, etc .) • Comparable to NLCD accuracy for more general classes
– NLCD Anderson level 2 accuracy is ~70% for patch and 58% for pixel
– NLCD agreement is defined as matching either “primary or alternate reference class” however

## Classification versus NLCD Raw Imagery Initial Classification NLCD (white is undefined ag, pale yellow is row crop) Final Classification

## Broader Applicability

• Technique would work best on a regional level
– Uses fairly detailed data which m ay vary from region to region
– Can be combined to cover larger areas once complete
– Could also be further generalized spatially, while m aintaining mu ch of the detailed information • One key benefit over other methods is the preservation of hedgerows and streams – This information can be folded into classifications with lower spatial resolution • Pilot study was conducted with sub- optim al im agery – Spring collection
– Compressed • Aerial and satellite im agery at least as useful as that used is widely available