Refining the Idaho GAP Classification Scheme for the Sage Brush Step Shoshone Basin Idaho

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Refining the Idaho GAP Classification Scheme for
the Sage Brush Step Shoshone Basin Idaho
by Wendy Goetz Research Technician College of
Natural Resources Department of Geography and
Earth Resources Utah State University, Logan
UT Todd A. Black Project Leader RS/GIS
Laboratory College of Natural Resources Department
of Geography and Earth Resources Utah State
University, Logan UT
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Partnership

• Partners
• Bureau of Land Management (BLM)
• Burley Field Office
• Idaho Fish and Game (IFG)
• Magic Valley Region
• Utah State University RS/GIS Lab. Landscape
  Ecology Modeling and Analysis Center (LEMA)

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Background

• Pilot project
• GIS vegetation database for Shoshone Basin ID
• Refine 1998 GAP Analysis data layers. Focusing on
  the sagebrush and upland vegetation communities.

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Justification

• Land management practices have caused sagebrush
  to disappear throughout the west
• Sagebrush communities are vital for sage
  grouse--nesting cover, important brood rearing
  areas, and critical food source during winter
  months

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Study Area--Shoshone Basin
Twin Falls County, Idaho 124,000 USGS Quads.

• Norton Canyon

• Magic Hot Springs

• Timber Butte

• Big Creek Ranch

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Purpose/need

• Assist working group in their efforts to
  implement Sage Grouse Management plan for the
  Shoshone Basin area.
• Provide area biologists with a up-dateable
  sagebrush and upland vegetation GIS database.
• Help area managers derive scientific management
  decisions and provide critical information needed
  to manage sage grouse in this area.

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Project Overview

• Preliminary data collection and processes
• Field data collection and evaluation
• LandSat data imagery processing
• digital classification
• Modeling/Final classification
• Accuracy assessments

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Data collection and pre-processing

• 1993 LandSat TM Imagery
• Idaho Western Wyoming GAP Analysis vegetation
  data
• unsupervised classification of 20 classes
• Digital Elevation Models
• Other ancillary data

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Field data collection evaluation

• IDFG tech collected field data summer 1998
• selected at random by classes
• Methods ocular estimations specific data
  collected picture GPS
• digital data entry

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LandSat data imagery processing

• Subset of GAP data to give possible sagebrush
  communities delineation of other habitat types.
• Erdas Imagine analysis of subset imagery

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Digital Classification

• Field data training sites
• Generated field points
• 2x2 pixel analysis 60m buffer
• Imagery training sites

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70 class unsupervised classification
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Modeling/Final Classification

• Recode--Classification

1-low canopy sagebrush 2-medium canopy
sagebrush 3-high canopy sagebrush 4-bitterbrush 5-
grass lands 6-low sage 7-mountain shrub 8-rabbit
brush
Low canopy -- lt15 sagebrush Medium canopy --
15-25 High canopy -- gt25
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Results/Accuracy Assessments
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Conclusions

• LandSat data can provide a means to look at
  vegetation data on large tracks of land
• Importance of preliminary planning
• Consistency in field data collection
• Important ancillary data is needed
• Benefits of having digital data in GIS