Monitoring Large Wildlife Directly through High Spatial Resolution Remote Sensing
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Monitoring Large Wildlife Directly through High Spatial Resolution Remote Sensing
Eric Sanderson and Scott Bergen
Wildlife Conservation Society
NASA-NIP: NNG04GP73G
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Why count wildlife?
• Wildlife are important components of the Earth system– They provide economic benefits (and costs)– They provide cultural benefits– They provide ecosystem benefits
• To manage wildlife, you need to know where they are and how many
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Usual ways of counting wildlife
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Why use RS to count wildlife?
• Repeatable
• World-wide
• Large scale
• Cost-effective
Not! Yet?
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But can you use RS to count wildlife?
• Maybe!• Pixel resolution of
Quickbird and IKONOS suggests possibilities
• Some early successes– Walruses in Alaska
– Elephants in Amboseli
– Prairie dog colonies in North Dakota
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But can you use RS to count wildlife?
• Confounding factors– Animal size– Animal color– Background– Canopy cover– Aggregation– Shadow
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Experimental Method: Year 1
• Controlled test in the Bronx Zoo– A diversity of different kinds of animals– Held within a limited extent– Know a priori how many there are
• Deploy experimental targets (“faux fur”)– Size (20 x 40 cm, 40 x 80 cm, 60 x 120 cm)– Color (white, black, brown)– Habitat (bare soil, grass, shrub, forest)– Shadow (open, shadow)
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Nov. 10, 200410:52:45 am
35 people involved
21 keepers
15 Volunteers
28 Enclosures mapped for individual animal locations
300 Faux fur targets placed in 4 ‘habitats’
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This data will help us determine the effects of body size, body color, habitat type, social aggregation and image resolution have upon identifying different animals in the wild from high resolution satellite imagery.
Early Results
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Band Combinations for Targets
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Target Size
Small Medium Large
% I
den
tifi
ed
White = yes, Gray = partial, Black = no
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Target Color
Black Brown White
Pre
sen
ce
White = yes, Gray = partial, Black = no
% I
den
tifi
ed
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Target “Habitat”
White = yes, Gray = partial, Black = no
Bare Grass Shrub Forest
% I
den
tifi
ed
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Shadowed Targets
In open In shadow
% I
den
tifi
ed
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Logistic regression results for factors effecting the identification of faux fur
Effect Deg. Freedom Wald Stat.
Intercept 1 26.811 0.0000
Color (8 bit) 1 51.291 0.0000
Size (m2) 1 12.434 0.0004
“Habitat” – Avg Veg Height (m)
1 34.617 0.0000
Contrast 1 0.756 0.3846
Aggregation 1 0.065 0.7988
Shadow 1 4.395 0.0360
Agg. * Shadow 1 0.802 0.3706
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Logistic regression equation results for all factors that were significant or highly significant.
Factor Estimate Standard Error
Wald Stat.
Intercept -3.66578 0.513696 50.92369 0.000000
Color (8 bit) 0.01917 0.002593 54.63177 0.000000
Size (m2) 0.97967 0.259426 14.26036 0.000159
Average Vegetation Ht (m)
-0.23020 0.038275 36.17339 0.000000
Shadow -0.42113 0.206441 4.16145 0.041354
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Animals resolved in imageryMammalsGrizzly Bear Polar Bear** Asian Elephant*Giraffe Guanaco LionNyala Grevy's Zebra Prezwalski's HorseBlesbok Gelada Baboon Nubian Ibex Pere David's Deer* Tiger* Thomson's GazelleArabian Oryx Caribou Sumatran Rhino*California Sea Lion
BirdsMaribou Stork Chilean Flamingo American FlamingoAdjutant Stork Emu Rhea*White Naped Crane Black Necked Crane Ostrich(?)
Animals visible in the imagery*Under tree canopy during acquisition** In shadow during acquisition
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Animal Shadows!
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Next Steps• Field experiments
– Ruaha National Park, Tanzania (fall 2005)– National Elk Refuge, Wyoming (winter 2005/06)– Coastal Patagonia, Argentina (fall 2006)
• Automated Image Processing– Image segmentation with Ecognition– Segment animal signature, shadow signature and
associate together– Contextual clues (habitat type, proximity to water,
etc.)
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All kinds of “wild”life
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Education Component• Postdoctoral Associate• Advanced course in Raster Analysis and Image Integration
– New York – fall 2005
• Basic Remote Sensing course for wildlife conservation– Africa – spring 2006– Latin America – fall 2006
• One-on-one advice / training for field conservation projects– Greater Yellowstone– Eastern Steppe, Mongolia– Tierra del Fuego, Chile
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Thomas Mueller / CRC Smithsonian