Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie

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Sustainable rangeland stewardship calls for synoptic estimates of rangeland biomass quantity (kg dry matter ha − 1 ) and quality [carbon:nitrogen (C:N) ratio]. These data are needed to support estimates of rangeland crude protein in forage, either by percent (CP c) or by mass (CP m). Biomass derived from remote sensing data is often compromised by the presence of both photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation. Here, we explicitly quantify PV and NPV biomass using HyMap hyperspectral imagery. Biomass quality, defined as plant C:N ratio, was also estimated using a previously published algorithm. These independent algorithms for forage quantity and quality (both PV and NPV) were evaluated in two northern mixed-grass prairie ecoregions, one in the Northwestern Glaciated Plains (NGGP) and one in the Northwestern Great Plains (NGP). Total biomass (kg ha − 1 ) and C:N ratios were mapped with 18% and 8% relative error, respectively. Outputs from both models were combined to quantify crude protein (kg ha − 1 ) on a pasture scale. Results suggest synoptic maps of rangeland vegetation mass (both PV and NPV) and quality may be derived from hyperspectral aerial imagery with greater than 80% accuracy.