Abstract
Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.
Highlights
The emergence of three-dimensional remote sensing techniques such as aerial laser scanning (ALS)and digital aerial photogrammetry (DAP) have the ability to provide detailed structural information in the form of point clouds
Proportion of valid DAP-digital elevation model (DEM) generated during the sensitivity analysis were 99.9%, 94.9% and 91.8% for the PTD, hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF) methods respectively
This study demonstrates the current capacity of typical low-cost unmanned aerial systems (UAS)-DAP ground modeling results within the interior forests of central British Columbia
Summary
The emergence of three-dimensional remote sensing techniques such as aerial laser scanning (ALS). Digital aerial photogrammetry (DAP) have the ability to provide detailed structural information in the form of point clouds. Provided an adequate digital elevation model (DEM) can be extracted from point clouds, analysis techniques such as individual tree crown detection (ITCD) [1,2,3,4] and area-based analysis (ABA) [5,6,7] can be applied to estimate metrics related to forest structure. Concurrent with the development of DAP analysis techniques has been the increased adoption of camera-equipped unmanned aerial systems (UAS). The ease of deployment of UAS allow for frequent flights and the ability to monitor highly dynamic vegetation compared to conventional remote sensing platforms such as aircraft and satellites [15,16]
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