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  1. Abstract

    The purpose of this study is to develop an unmanned aerial vehicle (UAV)‐based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to quantify six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass–shrub ecosystem. The drone‐based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line‐point intercept methods) in the spring and summer of 2017. The drone‐based estimates showed strong agreements with in situ measurements in cases where deciduous vegetation (mesquite) had leaves withR2for bare soil gap size and vegetation height of 0.97 and 0.89 in the summer, respectively. The RMSE of bare soil gap size and vegetation height are 0.2 m and 6.72 cm in the summer, respectively. Based on these results, we found that drone‐based remote sensing proved to be an efficient and highly accurate method that serves as a complement to field measurements for rangeland indicator estimation. We discussed the possible applications of drone‐based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non‐stationarity, and to detect landscape anisotropy.

     
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  2. Abstract

    Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjustedR2of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similarwithinbut differentamong,plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.

     
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