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SILVIS LAB
Spatial Analysis For Conservation and Sustainability
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Remote Sensing

Satellite images provide a wonderful record of the last fifty years of global change. We have pioneered new methods to map wildlife habitat and proxies for biodiversity and habitat, as well as agricultural abandonment and other types of land use change for large areas. We analyze MODIS/VIIRS data across the globe, Landsat and Sentinel-2 across continents, and high-resolution CORONA spy satellite imagery across countries.

Land degradation in the Caucasus Mountains

Feb 2019 - Land Use - Remote Sensing

What are the underlying drivers of land degradation in the Caucasus Mountains? Katarzyna Ewa Lewinska hopes to sort out part of this complex problem through remote sensing-based analysis.

After the fall: Land cover mapping in the Caucasus

Jan 2019 - Biodiversity - Land Use - Remote Sensing

What happens to croplands/forests during political instability? Johanna Buchner’s work in the Caucasus shows that change is dependent on local conditions, and not similar across broad scales.

A Cooper’s hawk (Accipiter cooperi) in an urban nature conservancy. Prey abundance seems to be the driving factor in their colonization and persistence in urban areas. Photo: Ashley Olah 2014.

Hawks, Urbanization, and Citizen Science

Dec 2018 - Birds - Conservation - Remote Sensing

Hawks are colonizing and persisting in Chicago. Sofia Kozidis is studying the pattern of hawk occupancy in many other urban areas.

Map of habitat heterogeneity across the conterminous U.S., based on 30-m resolution standard deviation texture (21x21 moving window) of NDVI (index of vegetation greenness) from Landsat 8 imagery. Darker green areas indicate regions with higher habitat heterogeneity.

Characterizing habitat heterogeneity using image texture

Dec 2018 - Biodiversity - Birds - Conservation - Remote Sensing

Laura Farwell is using image texture measures as powerful predictors of regional and global biodiversity patterns.

Moose abundance in the former Soviet Union can be predicted from the DHI Photo source: pexels.com

Predicting Moose Abundance from Space

Dec 2018 - Biodiversity - Conservation - Remote Sensing

Elena Razenkova found that remote measures of productivity like DHI can help explain moose abundance over the last 3 decades.

Figure 2 A field abandoned due to soil salinization (Khorezm, Uzbekistan)

Agricultural land abandonment mapping

Dec 2018 - Land Use - Remote Sensing

He Yin wants to increase precision in quantifying land abandonment across a diversity of biomes and complex crop systems.

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Contact Us

  • Dept of Forest & Wildlife Ecology
    Russell Laboratories
    1630 Linden Drive
    Madison, WI 53706
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  • Email: helmers@wisc.edu
  • Phone: (608) 265-9219
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