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Airborne LiDAR and Terrestrial Laser Scanning Derived Vegetation Obstruction Factors for Visibility Models., , , and . Trans. GIS, 18 (1): 147-160 (2014)Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales., , , , , , , and . Remote Sensing, 9 (9): 903 (2017)Spatial pattern of soil organic carbon acquired from hyperspectral imagery at reynolds creek critical zone observatory (RC-CZO)., , , , and . WHISPERS, page 1-5. IEEE, (2016)Empirical Methods for Remote Sensing of Nitrogen in Drylands May Lead to Unreliable Interpretation of Ecosystem Function., , , , , , , , , and 1 other author(s). IEEE Trans. Geosci. Remote. Sens., 57 (6): 3993-4004 (2019)Nasa Snowex'17 in SITU Measurements and Ground-Based Remote Sensing., , , , , , , , , and 33 other author(s). IGARSS, page 6266-6268. IEEE, (2018)Improved visibility calculations with tree trunk obstruction modeling from aerial LiDAR., , , and . Int. J. Geogr. Inf. Sci., 27 (10): 1865-1883 (2013)Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach., , , , , , , , , and . Remote. Sens., 11 (18): 2141 (2019)Resolving the Influence of Forest-Canopy Structure on Snow Depth Distributions with Terrestrial Laser Scanning., , , , , and . IGARSS, page 6284-6286. IEEE, (2018)A first overview of SnowEx ground-based remote sensing activities during the winter 2016-2017., , , , , , , , , and 34 other author(s). IGARSS, page 1391-1394. IEEE, (2017)