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Prediction of senescent rangeland canopy structural attributes with airborne hyperstectral imagery

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Canopy structural and chemical data are needed for senescent, mixed-grass prairie landscapes in autumn, yet models driven by image data are lacking for rangelands dominated by non-photosynthetically active vegetation (NPV). Here, we report how aerial hyperspectral imagery might be modeled to predict canopy attributes post growing-season using two approaches: 1) Application of narrow spectral regions with Vegetation Indices (VIs) and 2) Application of the full spectrum with Partial Least Squares Regression (PLSR). We collected Airborne Visible Infrared Spectrometer (AVIRIS) imagery and field data at 24 random herbaceous plots divided into summit, midslope and toeslope positions (72 sites total). Field data included dry mass for photosynthetically active vegetation (PV), NPV, total standing crop (TSC) and canopy nitrogen (N). We also estimated percent bare ground cover (%BG). We tested established VIs derived from selected regions of the spectrum with a novel re-sampling model selection procedure for each variable. We evaluated all regions of the spectrum with Partial Least Squares Regression (PLSR) for the same dataset. The randomly selected validation dataset (24 of the 72 sites) in the PLSR analyses indicated R2 values for NPV, TSC, %BG, Canopy N, and PV were 0.56, 0.62, 0.58, 0.67, and 0.73, respectively, with prediction errors that were lower than VI models. Analyses of VIs in a re-sampling model selection procedure indicated the short-wave infrared (SWIR) simple ratio ('2128 / '1642) was a key predictor for TSC, NPV and %BG. Overall, the SWIR (from 1260 to 1880 nm) was the spectral region most important for prediction of senescent rangeland canopy attributes. Analyses of the full spectrum using PLSR resulted in slightly lower root-mean-square error of prediction, as compared to VIs, which represent reflectance ratios for specific spectral bands. We conclude that prediction of canopy mass, N content and %BG can be achieved for senescent rangeland landscapes, given hyperspectral imagery and field data.
Rebecca Phillips , Mark West , Nicanor Saliendra , Brad Rundquist , Duane Poole
autumn , canopy , data collection , growing season , hyperspectral imagery , image analysis , landscapes , least squares , models , nitrogen , nitrogen content , prediction , rangelands , reflectance , vegetation cover
GIScience & Remote Sensing 2013 v.50 no.(2)
Journal Articles, USDA Authors, Peer-Reviewed
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