Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States

Authors: MCGWIRE, KENNETH - Desert Research Institute; Weltz, Mark; Snyder, Keirith; HUNTINGTON, JUSTIN - Desert Research Institute; MORTON, CHARLES - Desert Research Institute; MCEVOY, DANIEL - Desert Research Institute

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/12/2017
Publication Date: 7/10/2017
Citation: McGwire, K.C., Weltz, M.A., Snyder, K.A., Huntington, J.L., Morton, C.G., McEvoy, D.J. 2017. Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States. Rangeland Ecology and Management. 70(60):730-739. https://doi.org/10.1016/j.rama.2017.06.005.
DOI: https://doi.org/10.1016/j.rama.2017.06.005

Interpretive Summary: Drought is a reoccurring issue in the west and methods of how to quantify impacts of drought is a constant challenge for land managers. Satellite images from the MODIS sensor were used to classify vegetation using fifteen years of enhanced vegetation index data in association with precipitation and the Palmer drought severity index. These dataset were used to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. The relationship between the estimated condition of vegetation from the satellite image and meteorological data is found to improve when measurements are averaged over larger areas. A sixteen kilometer sampling grid was found to provide a good balance between predictive ability of vegetation and spatial precision. The average R2 of regressions between the vegetation index and meteorological variables within each of the 16 km grid cells was 0.69. For most of Nevada, the ability to predict vegetation conditions for the entire growing season generally has peaked by the end of May. However, results vary by region, with the northeast particularly benefitting from late-season data. Regressions were performed with and without very wet years, and the ability to make early predictions is better in wet years than in dry to typical conditions. Using this technique land managers can assess entire grazing allotments and better understand current and potential future conditions of vegetation based on accumulated precipitation and stored soil moisture to adapt to and mitigate impacts of drought.

Technical Abstract: Fifteen years of enhanced vegetation index data from the MODIS sensor are examined in conjunction with precipitation and the Palmer drought severity index to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. The correspondence between image and meteorological datasets is found to improve when measurements are averaged over larger areas. A sixteen kilometer sampling grid is found to provide a good balance between predictive ability and spatial precision. The average R2 of regressions between the vegetation index and meteorological variables within each of the 16 km grid cells was 0.69. For most of Nevada, the ability to predict vegetation conditions for the entire growing season generally has peaked by the end of May. However, results vary by region, with the northeast particularly benefitting from late-season data. Regressions were performed with and without very wet years, and the ability to make early predictions is better in wet years than in dry to typical conditions.