Edge-Tree Correction for Predicting Forest Inventory Attributes Using Area-Based Approach With Airborne Laser Scanning

We describe a novel method to improve the correspondence between field and airborne laser scanning (ALS) measurements in an area-based approach (ABA) forest inventory framework. An established practice in forest inventory is that trees with boles falling within a fixed border field measurement plot are considered “in” trees; yet their crowns may extend beyond the plot border. Likewise, a tree bole may fall outside of a plot, but its crown may extend into a plot. Typical ABA approaches do not recognize these discrepancies between the ALS data extracted for a given plot and the corresponding field measurements. In the proposed solution, enhanced ABA (EABA), predicted tree positions, and crown shapes are used to adjust plot and grid cell boundaries and how ALS metrics are computed. The idea is to append crowns of “in” trees to a plot and cut down “out” trees, then EABA continues in the traditional fashion as ABA. The EABA method requires higher density ALS data than ABA because improvement is obtained by means of detecting individual trees. When compared to typical ABA, the proposed EABA method decreased the error rate (RMSE) of stem volume prediction from 23.16% to 19.11% with 127 m² plots and from 19.08% to 16.95% with 254 m² plots. The greatest improvements were obtained for plots with the largest residuals.