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Open AccessArticle

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

1
College of Geography and Environmental Science, Hainan Normal University, Haikou 571158, China
2
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
4
School of Geography, South China Normal University, Guangzhou 510631, China
*
Author to whom correspondence should be addressed.
Forests 2019, 10(10), 871; https://doi.org/10.3390/f10100871
Received: 31 July 2019 / Revised: 24 September 2019 / Accepted: 26 September 2019 / Published: 4 October 2019
To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha−1 vs. 38.95 Mg ha−1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests. View Full-Text
Keywords: AGB estimation and mapping; mangroves; UAV LiDAR; WorldView-2 AGB estimation and mapping; mangroves; UAV LiDAR; WorldView-2
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Qiu, P.; Wang, D.; Zou, X.; Yang, X.; Xie, G.; Xu, S.; Zhong, Z. Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data. Forests 2019, 10, 871.

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