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Article

Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

1
Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, UCLouvain, 1348 Louvain-la-Neuve, Belgium
2
Department of Environment, Ghent University, 9000 Ghent, Belgium
3
Department of Green Chemistry and Technology, Ghent University, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Mauro Maesano and Federico Valerio Moresi
Remote Sens. 2021, 13(18), 3777; https://doi.org/10.3390/rs13183777
Received: 22 August 2021 / Revised: 14 September 2021 / Accepted: 17 September 2021 / Published: 20 September 2021
(This article belongs to the Special Issue UAV Applications for Forest Management: Wood Volume, Biomass, Mapping)
Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests. View Full-Text
Keywords: UAV; DAP; SfM; LiDAR; digital terrain model; tropical forest; biomass UAV; DAP; SfM; LiDAR; digital terrain model; tropical forest; biomass
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MDPI and ACS Style

Zhang, H.; Bauters, M.; Boeckx, P.; Van Oost, K. Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches. Remote Sens. 2021, 13, 3777. https://doi.org/10.3390/rs13183777

AMA Style

Zhang H, Bauters M, Boeckx P, Van Oost K. Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches. Remote Sensing. 2021; 13(18):3777. https://doi.org/10.3390/rs13183777

Chicago/Turabian Style

Zhang, He, Marijn Bauters, Pascal Boeckx, and Kristof Van Oost. 2021. "Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches" Remote Sensing 13, no. 18: 3777. https://doi.org/10.3390/rs13183777

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