Imagine the result

Habitat Classification for Ecological Risk 

Assessment Using Aerial Photography and GIS 

Data in a Two-stage Expert System 

January 11, 2011

1
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

2
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

3
Input Data

4
Input Data

5
Input Data

6
Imagery Challenges

• Typical Issues

– High spatial 

resolution 

presents 

challanges

• Particular problems 

with this imagery

– Time of year

– Compression

7
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

8
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

9
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

10
Pre-processing

• Similar band-based 

statistics calculated 

based on parcels

• Object bounds here 

are man-made rather 

than image-driven

11
Pre-processing

• Hydric soils 

differentiated from 

others

12
Pre-processing

• Parcels defined by 

acreage

• Large acreage has 

“hot” colors

• Note that river has 

zero acreage as 

far as tax rolls are 

concerned

13
Bringing Data Together

• Two-stage 

expert system

– Everything 

converted to 

raster

– Classified 

pixel-bypixel (2-foot 

pixel) based 

on set of 

rules

14
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

15
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

16
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

17
Second Stage Expert System

• Raw Imagery

18
Second Stage Expert System

• Stage 1 classes

19
Second Stage Expert System

• Generalized polygons

20
Second Stage Expert System

• Agriculture Pixels

– General polygons 

shown in red

– Pixels classed as 

agriculture shown 

as green, 

everything else 

gray

21
Second Stage Expert System

• Deciduous Pixels

– General polygons 

shown in red

– Pixels classed as 

deciduous trees 

shown as green, 

everything else 

gray

22
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

23
Second Stage Expert System

• Final Map

24
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

25
Classification versus NLCD

Raw 

Imagery

Initial 

Classification

NLCD 

(white is 

undefined 

ag, pale 

yellow is 

row crop)

Final 

Classification

26
Broader Applicability

• Technique would work best on a regional level

– Uses fairly detailed data which may vary from region to 

region

– Can be combined to cover larger areas once complete

– Could also be further generalized spatially, while maintaining 

much 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-optimal imagery

– Spring collection

– Compressed

• Aerial and satellite imagery at least as useful as that used is 

widely available

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