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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, USA. Mapped explanatory data included tasseled-cap indices and disturbance history metrics (year, magnitude, and duration of disturbance) from LTS imagery, lidar-derived vegetation metrics, climate, topography, and soil parent material. Vegetation data from USDA Forest Service forest inventory plots was summarized at two plot sizes (plot and subplot) and geographically located with two levels of accuracy (standard and improved). Maps of vegetation composition and structure were developed with the Gradient Nearest Neighbor (GNN) method of NN imputation using different combinations of explanatory variables, plot spatial resolution, and plot positional accuracy. Lidar vegetation indices greatly improved predictions of live tree structure, moderately improved predictions of snag density and down wood volume, but did not consistently improve species predictions. LTS disturbance metrics improved predictions of forest structure, but not to the degree of lidar indices, while also improving predictions of many species. Absence of disturbance attribution (i.e. disturbance type such as fire or timber harvest) in LTS disturbance metrics may have limited our ability to predict forest structure. Absence of corrected lidar intensity values may also have lowered accuracy of snag and species predictions. However, LTS disturbance attribution and lidar corrected intensity values may not be able to overcome fundamental limitations of remote sensing for predicting snags and down wood that are obscured by the forest canopy. Improved GPS plot locations had little influence on map accuracy, and we suggest under what conditions improved GPS plot locations may or may not improve the accuracy of predictive maps that link remote sensing with forest inventory plots. Subplot NN imputation maps had much lower accuracy compared to maps generated using response variables from larger whole plots. No single map had optimal results for every mapped variable, suggesting map users and developers need to prioritize what forest vegetation attributes are most important for any given map application. |
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