Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position |
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Authors: | Rebecca L. Phillips Moffatt K. Ngugi John Hendrickson Aaron Smith Mark West |
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Affiliation: | 1. United States Department of Agriculture (USDA), Agricultural Research Service (ARS), 1701 10th Avenue nw, Mandan, ND, 58554, USA 2. Ducks Unlimited, 2525 River Road, Bismarck, ND, 58503, USA 3. United States Department of Agriculture (USDA), Agricultural Research Service (ARS), 2150 Centre Avenue, Fort Collins, CO, 80526, USA
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Abstract: | Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1?km2 in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups. |
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