monitoring large wildlife directly through high spatial resolution remote sensing

Monitoring Large Wildlife Directly through High Spatial Resolution Remote Sensing

Eric Sanderson and Scott Bergen

Wildlife Conservation Society

NASA-NIP: NNG04GP73G

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

Usual ways of counting wildlife

Why use RS to count wildlife?

• Repeatable

• World-wide

• Large scale

• Cost-effective

Not! Yet?

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

But can you use RS to count wildlife?

• Confounding factors– Animal size– Animal color– Background– Canopy cover– Aggregation– Shadow

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)

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’

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

Band Combinations for Targets

Target Size

Small Medium Large

% I

den

tifi

ed

White = yes, Gray = partial, Black = no

Target Color

Black Brown White

Pre

sen

ce

White = yes, Gray = partial, Black = no

% I

den

tifi

ed

Target “Habitat”

White = yes, Gray = partial, Black = no

Bare Grass Shrub Forest

% I

den

tifi

ed

Shadowed Targets

In open In shadow

% I

den

tifi

ed

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 

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 

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

Animal Shadows!

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.)

All kinds of “wild”life

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

Thomas Mueller / CRC Smithsonian