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Counting Animals from Space:. Chapter Two Transitions from Captivity to Wild Places. Scott Bergen & Eric Sanderson. Why Count Wildlife?. Fundamental to Conservation Foundational to Population Studies Federal Programs Spend Millions of Dollars Annual to Count Animals. Nov. 10, 2004.

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counting animals from space

Counting Animals from Space:

Chapter Two

Transitions from Captivity to

Wild Places

Scott Bergen & Eric Sanderson

why count wildlife
Why Count Wildlife?
  • Fundamental to Conservation
  • Foundational to Population Studies
  • Federal Programs Spend Millions of Dollars Annual to Count Animals
nov 10 2004
Nov. 10, 2004

10:52:45 am

35 people involved

21 keepers

15 Volunteers

28 Enclosures mapped for individual animal locations

300 Faux fur targets placed in 4 ‘habitats’

Digital Globe Inc ©

ground vs sky
Ground –vs- Sky

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information shadow
Information Shadow

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tallying identification by species
Tallying Identification by Species

Logit (identified targets) = -3.666 + 0.019(Color) + 0.970(Size) - 0.230(VegHt) - 0.421(Shade).

counting animals
Counting Animals
  • Most reliable estimate use transect with repeat measures
  • Population estimates w/ standard deviation
  • Findings usually extrapolated from small area to available habitat or other limiting feature
  • Costly to count animals on ground
  • Remote sensing rarely used (aerial imagery)
  • Time and scale rarely match satellite scale & time
  • Mismatch in terms of time and location in reference to identifying- verifying high spatial resolution satellite imagery
why the national elk refuge
Why the National Elk Refuge?
  • Reliable elk & bison congregations during winter
  • Logistic regression equation shows good fit for size, color, vegetation and shadow
  • Annual census of both elk and bison
animal count comparisons
Animal Count Comparisons
  • Refuge level, elk (weekly), bison (annual)
  • Ground census estimate @ time of satellite acquisition
  • Panoramic photo estimate @ time of satellite acquisition
  • Heads up digitizing estimate
  • Object oriented estimate
jackson wyoming
Jackson Wyoming
  • Access limited
  • Freakin’ cold -20f
  • Snow bleaching histogram of sensor

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ground census of elk group
Ground Census of Elk Group
  • High Ground limited
  • Limited by distance
  • 1360 individuals
  • 60/40 female- male ratio
thick in elk
Thick In Elk

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panaramic resesults
Panaramic Resesults
  • Verified over 1,000 elk sex, position, direction position in less than 10 seconds
  • Estimated 1070 individuals
  • 679 females, 299 males, 89 ?
  • Knew there were more but individuals > 1km were not identifiable as well as those totally blocked by other elk
heads up
Heads up
  • 1503 individuals

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object oriented approach
Object Oriented Approach
  • Scale based segmentation
  • > classification
  • > revision
  • >classification
  • Hierarchical strutured
  • Means both smaller and larger

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segmentation
Segmentation
  • Adds new dimensions to data
  • Area, spectra, variability within polygons
  • Adjacency
  • Contextual
  • Generates data Important to distinguish animals and differentiate types of animals
initial classification
Initial Classification
  • Good Results
  • Identified 1540 individuals
  • Misidentification within riparian areas
  • Grouped elk in close proximity
classification
Classification
  • Refined with an area classifier
  • 1482 individuals
  • Further refinement, standing – sitting, elk vs bison, sexes in bison
summary of animal counts
Summary of Animal Counts
  • Park Estimate: 4,900 elk, 951 bison
  • Ground Estimate: 1,360 elk, 60/40 f/m
  • Panoramic: 1,071 elk, 69/31 f/m
  • Heads up: 1,503 elk
  • 1st Object Oriented Class: 1,540
  • Ob. Orient w/ Area: 1,480
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