Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
AbstractApplication of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha−1. Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs. View Full-Text
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Kachamba, D.J.; Ørka, H.O.; Gobakken, T.; Eid, T.; Mwase, W. Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens. 2016, 8, 968.
Kachamba DJ, Ørka HO, Gobakken T, Eid T, Mwase W. Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sensing. 2016; 8(11):968.Chicago/Turabian Style
Kachamba, Daud J.; Ørka, Hans O.; Gobakken, Terje; Eid, Tron; Mwase, Weston. 2016. "Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland." Remote Sens. 8, no. 11: 968.
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