Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Abstract

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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“…Recall of ITD ranged from 0.36 to 0.67 for varying canopy cover conditions and is comparable to other ITD algorithms [ 139 ]. It is assumed ITD recall for this study is likely less than that determined by Sparks and Smith [ 139 ] due to the lower pulse density of the lidar data used. It is possible that suppressed or intermediate trees were occluded or omitted during lidar-processing which would have reduced the number of detected stems, especially in areas of high stand density [ 147 – 150 ].…”

“…First, the accuracy of the individual tree detection (ITD) product used in this study has not been assessed. However, ITD accuracy of the ForestView™ algorithm was quantified in a separate study on the University of Idaho Experimental Forest using lidar data with an average pulse density of 20 pulses m -2 [ 139 ]. Recall of ITD ranged from 0.36 to 0.67 for varying canopy cover conditions and is comparable to other ITD algorithms [ 139 ].…”

“…However, ITD accuracy of the ForestView™ algorithm was quantified in a separate study on the University of Idaho Experimental Forest using lidar data with an average pulse density of 20 pulses m -2 [ 139 ]. Recall of ITD ranged from 0.36 to 0.67 for varying canopy cover conditions and is comparable to other ITD algorithms [ 139 ]. It is assumed ITD recall for this study is likely less than that determined by Sparks and Smith [ 139 ] due to the lower pulse density of the lidar data used.…”

“…Nominal point spacing of 0.33 meters and an average nominal pulse density of 9.11 pulses m -2 was achieved. Individual tree detection and segmentation derived from the lidar point cloud was performed by Northwest Management Incorporated (NMI) using a proprietary software and processing workflow associated with NMI’s ForestView™ software [ 139 ]. This point-based lidar processing resulted in a full coverage ITD stem map for the four main University of Idaho Experimental Forest (UIEF) management units.…”

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“…The precision of a canopy model is affected by many factors, including the density of the point cloud, the interpolation algorithm used, and the resolution of the DTM and DSM models from which the CHM model is created. The accuracy of tree canopy mapping mainly depends on the resolution [16], canopy cover [17], and tree height [18]. The larger the pixel size of the raster, the more general the information about the object.…”