These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes.
We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods.
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