Geology topics

Jonathon Donager

Publications and source records attributed to Jonathon Donager.

2 recordsLinked to original sources

USGS2021

Integrating airborne and mobile lidar data with UAV photogrammetry for rapid assessment of changing forest snow depth and cover

Forest structure and topography can influence the ecohydrologic function and resiliency to drought and changing climate. It is, therefore, important to understand how forest restoration treatments alter snowpack distribution and design the treatments accordingly. We use a combination of aerial lidar, multi-temporal terrestrial mobile lidar, and UAV photogrammetry to estimate rapidly changing snow depth and cover in northern Arizona, USA. We then examine the impact of forest structure and topography on snow depth and snow cover persistence to inform forest restoration treatments. Our results show that mobile lidar data can be used to estimate snow depth with standard errors of 8 cm when differenced with snow-off airborne lidar data. UAV-based Structure-from-Motion data can be used to estimate snow cover persistence with 92–97% overall accuracies in forested ecosystems. Random forest models indicate spatially varying importance of forest structural and topographic variables in predicting snow depth and cover persistence, when summarized at different spatial scales (from 5 m to 250 m) and with variable directional location offsets. Forest snow depth was best explained (R 2 ≈ 0.46) by canopy height metrics at summary scales of >75 m, while canopy cover was most important at summary scales of <40 m (R 2 ≈ 0.3). Snow cover persistence was best explained at very local scales by canopy cover (R 2 ≈ 0.38) and less so at larger scales (>75 m) by topographic and forest patch characteristics (R 2 ≈ 0.34). Our results demonstrate that 3-dimensional datasets are critical in rapidly characterizing changing snowpack to better understand the impacts of forest structure and topography to inform forest restoration treatment designs. The relationships observed in our study can inform currently ongoing regional-scale forest restoration in the southwest to improve forest health and resiliency.

Science of Remote Sensing↗

USGS2017

UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height ( R 2 = 0.90; RMSE = 2.3 m) and crown diameter ( R 2 = 0.72; RMSE = 0.71 m) as well as total tree canopy cover ( R 2 = 0.87; RMSE = 9.5%) and tree density ( R 2 = 0.77; RMSE = 0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) ( R 2 = 0.92; RMSE = 0.75 m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.

Arizona↗