A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography
Iqbal, IA and Musk, RA and Osborn, J and Stone, C and Lucieer, A, A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography, International Journal of Applied Earth Observation and Geoinformation, 76 pp. 231-241. ISSN 0303-2434 (2019) [Refereed Article]
Forest inventory operations have greatly benefitted from remotely sensed data particularly airborne laser scanning (ALS) which has become a popular technology choice for large-area forest inventories. For remote regions, for fragmented estates or for single stand-level inventories ALS may be unsuitable because of the high cost of data acquisition. Point cloud data generated from digital aerial photography (DAP) is emerging as a cost-effective alternative to ALS. In this study we compared area-based forest inventory attributes derived from point cloud datasets sourced from ALS, small-format and medium-format digital aerial photography (SFP and MFP). Non-parametric modelling approach, namely RandomForest, was employed to model forest structural attributes at both plot- and stand-levels. The results were evaluated using field data collected at 105 inventory plots. At plot-level, the maximum difference among relative RMSEs of basal area (BA), top height (Htop), stocking (N) and total stem volume (TSV) of the three datasets was 2.46%, 0.55%, 13.29% and 2.53%, respectively. At stand-level, the maximum difference among relative RMSEs of BA, Htop, N and TSV of the three datasets was 3.86%, 1.25%, 7.85% and 6.04%, respectively. This study demonstrates the robustness of DAP across different sensors, and thus informs forest managers planning data acquisition solutions to best suit their operational needs.
forest inventory, Pinus radiata, airborne laser scanning, digital aerial photography, photogrammetry, small-format photography, medium-format photography, image point cloud, Random Forest, remote sensing, forestry