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Natural Resource Ecology Laboratory (NREL)

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https://hdl.handle.net/10217/195540
These digital collections include faculty publications, presentations, reports, and datasets from the Natural Resource Ecology Laboratory (NREL). Included here are individual datasets for the Ethiopia Project, Shortgrass Steppe-Long Term Ecological Research (SGS-LTER), Riparian Habitat and Invasive Species in the Colorado River Basin, and Yellowstone Willows LTREB. Also included is a collection of publications by Eldor A. Paul, a Senior Research Scientist at the Natural Resource Ecology Laboratory and a Professor in the Department of Soil and Crop Sciences.

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Browsing Natural Resource Ecology Laboratory (NREL) by Author "Anderson, Ryan, author"

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    ItemOpen Access
    Application of Landsat 8 imagery and statistical models for mapping critical headwater wetlands of Ethiopia
    (Colorado State Univesity. Libraries, 2014) Chignell, Stephen, author; Anderson, Ryan, author; Wakie, Tewodros, author
    The Bale Mountains of south-central Ethiopia comprise one of Africa's least-studied massifs, and are home to the world-renowned Bale Mountain 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/Thermal Infrared data, topographic variables, and species distribution models to map all perennial alpine lakes and wetlands in the Bale Mountains. Resulting models of wetlands and lakes had classification accuracies of 97% and 100%, respectively. These represent the first comprehensive maps of their kind in Bale, and will facilitate the targeting of conservation and research efforts in the region. Additionally, the methodology is applicable in other remote areas around the world where field data is sparse and regular monitoring is needed.
  • Thumbnail Image
    ItemOpen Access
    Application of Landsat 8 imagery and statistical models for mapping critical headwater wetlands of Ethiopia
    (Colorado State Univesity. Libraries, 2014) Chignell, Stephen, author; Anderson, Ryan, author; Wakie, Tewodros, author
    The Bale Mountains of south-central Ethiopia comprise one of Africa's least-studied massifs, and are home to the world-renowned Bale Mountain 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/Thermal Infrared data, topographic variables, and species distribution models to map all perennial alpine lakes and wetlands in the Bale Mountains. Resulting models of wetlands and lakes had classification accuracies of 97% and 100%, respectively. These represent the first comprehensive maps of their kind in Bale, and will facilitate the targeting of conservation and research efforts in the region. Additionally, the methodology is applicable in other remote areas around the world where field data is sparse and regular monitoring is needed.
  • Thumbnail Image
    ItemOpen Access
    Mapping native and non-native riparian vegetation in the Colorado River Watershed
    (Colorado State University. Libraries, 2018-10-24) Evangelista, Paul, author; Young, Nicholas, author; Vorster, Tony, author; West, Amanda, author; Hatcher, Emma, author; Woodward, Brian, author; Anderson, Ryan, author; Girma, Rebecca, author
    Using remote sensing to map riparian vegetation, particularly single species such as tamarisk and Russian olive, requires georeferenced occurrence locations with estimations of foliar cover to train remote sensing-based models. This report details on the data, resources, methods and results to developing riparian vegetation, tamarisk and Russian olive distribution models along the Colorado River and its’ main tributaries. Change in riparian vegetation for Colorado River Basin was analyzed, finding an overall increase in riparian vegetation between 2006 and 2016. Also, an evaluation of the change map in known regions of tamarisk management showed that our models did identify a substantial decrease in tamarisk. The results of this study are a promising next step for project partners to utilize remote sensing to monitor the efficacy of management efforts throughout the Colorado River Basin and inform future management strategies.