Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA
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abstract
The principal study objective was to explore the feasibility of using small-footprint lidar data and multispectral imagery to estimate forest volume and biomass on small (0.017-ha) plots. In addition, the spatial dependency of residuals between ground-measured and lidar-estimated variables was investigated. The lidar data set was acquired over deciduous and pine stands in the southeastern United States. Individual trees were identified on the lidar-derived canopy height model by local maximum focal filtering with both square and circular windows of variable size. The size of the dynamically varying window was based on the height of the canopy and the taxonomic group as derived from coregistered multispectral optical data. Lidar-measured parameters at an individual tree level (height, crown diameter) were used with regression models and cross validation to estimate plot-level field inventory data, including volume, biomass, basal area, and diameter at breast height (dbh). Maximum R2 values for estimating biomass were 0.32 for deciduous trees (RMSE 44 Mg/ha) and 0.82 for pines (RMSE 29 Mg/ha). When estimating volume, maximum R2 values for deciduous trees were 0.39 (RMSE 52.84 m3/ha) and 0.83 for pines (RMSE 47.9 m3/ha). Calculation of Moran's / coefficient for each regression model revealed a lack of significant autocorellation of the residuals at 0.05 level. Both model fit and prediction for volume and biomass models for deciduous and pine plots indicated that the circular window shape is more appropriate to locate individual trees with lidar. Using the fused data set, lidar and optical imagery, as opposed to using lidar data alone, always improved biomass and volume estimates for pines and in some cases for deciduous plots as well.
