UAV hyperspectral and lidar data and their fusion for arid and semi‐arid land vegetation monitoring

Unmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Rapid advances in miniature sensor technology are leading to greater potentials for ecological research. We demonstrate one of the first applications of UAV lidar and hyperspectral imagery and a fusion method for individual plant species identification and 3D characterization at submeter scales in south‐eastern Arizona, USA. The UAV lidar scanner characterized the individual vegetation canopy structure and bare ground elevation, whereas the hyperspectral sensor provided species‐specific spectral signatures for the dominant and target species at our study area in leaf‐on condition. We hypothesized that the fusion of the two different data sources would perform better than either data type alone in the arid and semi‐arid ecosystems with sparse vegetation. The fusion approach provides 84–89% overall accuracy (kappa values of 0.80–0.86) in target species classification at the canopy scale, leveraging a wide range of target spectral responses in the hyperspectral data and a high point density (50 points/m2) in the lidar data. In comparison, the hyperspectral image classification alone produced 72–76% overall accuracies (kappa values of 0.70 and 0.71). The UAV lidar‐derived digital elevation model (DEM) is also strongly correlated with manned airborne lidar‐derived DEM (R2 = 0.98 and 0.96), but was obtained at a lower cost. The lidar and hyperspectral data as well as the fusion method demonstrated here can be widely applied across a gradient of vegetation and topography to monitor and detect ecological changes at a local scale. Unmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Furthermore, UAV hyperspectral and lidar data fusion leverages the newest sensor technology and analysis tools. In this study we demonstrate that the fusion improves classification accuracy of individual plant species at the canopy scale and 3D modeling of earth surface.