This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
To understand causes and consequences of landscape change, it is often not enough to simply detect change. Rather, the agent causing the change must also be determined. Here, we describe and test a method of change agent attribution built on four tenets: agents operate on patches rather than pixels; temporal...
This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS)
imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor
imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were
developed for 539,000 ha in the central Oregon Cascades,...
Understanding fine-grain patterns of forest disturbance and regrowth at the landscape scale is critical for effective management, particularly in forests in western Washington, Oregon, and California, U.S., where the policy known as the Northwest Forest Plan (NWFP) was imposed in 1994 over > 8 million ha of forest in an...