Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size

In recent years, the use of airborne lidar technology to measure forest biophysical characteristics has been rapidly increasing. This paper discusses processing algorithms for deriving the terrain model and estimating tree heights by using a multiple return, high � / density, small-footprint lidar data set. The lidar data were acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern US. The specific objectives were: (1) to develop and test algorithms to estimate plot level tree height using lidar data, and (2) to investigate how ground measurements can help in the processing phase of lidar data for tree height assessment. The study area is located in the Piedmont physiographic province of Virginia, USA and includes a portion of the Appomattox-Buckingham State Forest (37825?N, 78841?W). Two lidar processing algorithms are discussed */the first based on single tree crown identification using a variable window size for local filtering, and the second based on the height of all laser pulses within the area covered by the ground truth data. Height estimates resulted from processing lidar data with both algorithms were compared to field measurements obtained with a plot design following the USDA Forest Service Forest Inventory and Analysis (FIA) field data layout. Linear regression was used to develop equations relating lidar-estimated parameters with field inventories for each of the FIA plots. As expected, the maximum height on each plot was predicted with the highest accuracy (R 2 values of 85 and 90%, for the first and the second algorithm, respectively). The variable window size algorithm performed better for predicting heights of dominant and co-dominant trees (R 2 values 84 � /85%), with a diameter at breast height (dbh) larger than 12.7 cm (5 in), when compared with the algorithm based on all laser

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