Jason McVay

TL;DR: In this article, high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) were used for vegetation classification and structure measurements.

TL;DR: In this paper, a UAV lidar and hyperspectral images were combined for individual plant species identification and 3D characterization at sub-meter scales in south-eastern Arizona, USA.

TL;DR: Using an ensemble of climate, fire, and erosion models, this paper showed that postfire sedimentation is projected to increase for nearly nine tenths of watersheds by >10% and for more than one third of watershed regions by >100% by the 2041 to 2050 decade in the western USA.

TL;DR: In this article, the authors examined regional-scale temporal trends in snow distribution across central and northern Arizona using two tiles of 2928 daily images of MOD10 snow product, which included the entire MODIS archive time period, October 1, 2003-June 1, 2014, and a 245,041 km2 area of 51 HUC8 watersheds.

TL;DR: Aerial lidar was found to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry, and combining point cloud data and derivatives from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation than any single technique alone.