Project Team: Ethiopia Water Resources
Team Location: U.S. Geological Survey, Colorado State University, Fort Collins, Colorado
Tasseled Cap ÛÏWetnessÛ layer of study site. Served as predictor variable in Maximum Entropy modeling and derived from underlying NASA Landsat 8 OLI imagery. Image Credit: Ethiopia Water Resources Team.
Authors:
Stephen Chignell, Project Lead (Colorado State University)
Ryan Anderson (Colorado State University)
Tewodros Wakie (Colorado State University)
Mentors/Advisors:
Paul Evangelista (Colorado State University)
Melinda Laituri (Colorado State University)
Abstract:
The Bale Mountains of south-central Ethiopia comprise one of Africa’s least-studied massifs, and are home to the world-renowned Bale Mountains National Park. A designated Biodiversity Hotspot, the area also serves as the headwaters for five major rivers that flow out of the mountains, supporting 12 million people in the arid lowlands to the east. In recent years, development in the surrounding area has forced many agro-pastoralists into the highlands, and approximately 40,000 people now live within the park boundaries. Mapping the location and extent of the region’s water resources has been identified as a key research need for local park officials and conservation groups as they work to sustainably accommodate this massive influx of people and livestock. Of particular concern are the region’s numerous alpine lakes and wetlands, as they are essential for wildlife habitat, water quality, and discharge timing for both upstream and downstream users throughout the dry season. This study used environmental indices derived from Landsat 8 Operational Land Imager (OLI) data, topographic variables, and Maximum Entropy (MaxEnt) modeling to map all perennial lakes and wetlands in the Bale Mountains headwaters. The models were assessed using a cross-validation method, and produced high classification accuracies. These results represent the first comprehensive maps of these critical wetlands, and will facilitate the targeting of conservation and research efforts. Additionally, the methodology is applicable in other remote regions around the world where field data is sparse and regular monitoring is needed.