No abstract

The area burned annually by wildfires is expected to increase worldwide due to climate change. Burned areas increase soil erosion rates within watersheds, which can increase sedimentation in downstream rivers and reservoirs. However, which watersheds will be impacted by future wildfires is largely unknown. Using an ensemble of climate, fire, and erosion models, we show that postfire sedimentation is projected to increase for nearly nine tenths of watersheds by >10% and for more than one third of watersheds by >100% by the 2041 to 2050 decade in the western USA. The projected increases are statistically significant for more than eight tenths of the watersheds. In the western USA, many human communities rely on water from rivers and reservoirs that originates in watersheds where sedimentation is projected to increase. Increased sedimentation could negatively impact water supply and quality for some communities, in addition to affecting stream channel stability and aquatic ecosystems.

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 semiarid 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/m 2 ) 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 (R 2 = 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.

No abstract

Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R 2 ) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R 2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R 2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel.