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

Abstract

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 R 2 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.

Relationships between above- and below-ground carbon stocks in mangrove forests facilitate better estimation of total mangrove blue carbon

“…Previous assessments of mangrove carbon stock were mainly limited to a specific location or a certain carbon stock component (biomass carbon or soil carbon). Benefiting from the convenience of large-scale monitoring using the remote sensing technology, more attentions have been paid to monitor the mangrove vegetation, representing the above-ground biomass carbon stock [ 45 , 46 , 79 , 104 ]. However, estimation of below-ground soil (or ecosystem) carbon stock remains insufficient.…”

High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data

“…As the resolution increases, the spatial correlation between adjacent image element values of all feature classes decreases [50], which is consistent with the findings of the image resolution on the classification accuracy of features. In 2019, Penghua Qiu used the UAV LIDAR and WorldView-2 data from the same study area of Qinglan Harbor Mangrove Reserve for species classification of mangroves with an overall accuracy of 86.08% [42], which was 9.81% lower compared to the results of this study. Different resolution optical images have great potential to be explored, and studies that simply introduce different types of remote sensing data without trying to explore the value of the data in depth are blind.…”

“…The average annual precipitation is 1650 mm. The soil is mostly alluvial and chalky clay, the terrain is flat, and the average tidal difference is about 0.89 m [42]. The four dominant mangrove species in the study area are Rhizophora apiculata Blume (RB), Bruguiera sexangula (BS), Hibiscus tiliaceus Linn.…”

Applications of UAS in Crop Biomass Monitoring: A Review

“…Depth data that do not change with brightness and color can provide additional useful information for complex scenes (Shuqin et al, 2016). At present, the depth sensors on UAV platforms are mainly LiDAR (Wallace et al, 2012;Qiu et al, 2019;Tian et al, 2019;Wang D.Z. et al, 2019;Yan et al, 2020).…”