Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

Abstract

Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.

Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam

“…The variable importance results in Figure 5 show that the SWIR bands 11 and 12 of the S-2 sensor play an important role in mangrove AGB retrieval in the study area. This finding is consistent with recent studies reported by Wang, et al [89]. In addition, two vegetation red-edge bands (bands 5 and 6) were found to be sensitive to mangrove AGB in the biosphere reserve.…”

Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam

“…However, these approaches are disadvantaged by high cost and site-selection biases [15]. Cost-effective and accurate retrieval techniques for mangrove AGB in tropical and semi-tropical areas would provide baseline data for the monitoring, reporting, and verification schemes adopted in climate-change mitigation strategies, such as Blue Carbon projects and the United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD+) program in the tropics [16].In recent years, mangrove AGBs have been increasingly mapped using earth observation (EO) data collected by optical sensors [17][18][19], synthetic aperture radar (SAR) data [13,20,21], airborne LiDAR [22,23], and LiDAR data acquired form unmanned aerial vehicles (UAV) [24,25]. A few attempts combined the data of multispectral and SAR sensors for mangrove AGB retrieval in tropical regions.…”

“…Meanwhile, numerous EO datasets have been compiled from optical, SAR, and LiDAR data. These data are commonly retrieved from non-parametric regression techniques such as the random forest regression (RFR) algorithm [17,25,27], artificial neuron networks (ANN) [26], and support vector regression (SVR) [28,29]. Recently, gradient boosting decision trees (GBDT) effectively solved regression problems such as evaporation prediction [30] and oil price estimation [31].…”

“…In recent years, mangrove AGBs have been increasingly mapped using earth observation (EO) data collected by optical sensors [17][18][19], synthetic aperture radar (SAR) data [13,20,21], airborne LiDAR [22,23], and LiDAR data acquired form unmanned aerial vehicles (UAV) [24,25]. A few attempts combined the data of multispectral and SAR sensors for mangrove AGB retrieval in tropical regions.…”

Remotely piloted aircraft systems and forests: a global state of the art and future challenges

“…Methodologies based on high-resolution light detection and ranging (LiDAR) have also been used in this context (Sankey et al 2017;Guo et al 2017;Cao et al 2019;Almeida et al 2019), enabling high-density point clouds to be generated from the highresolution RPAS images and showing advantages over those heretofore commonly collected by larger, manned aircraft as LiDAR platforms (Sačkov et al 2016). Moreover, this type of study has enabled advances regarding the detection of individual trees, which is another widely reported application, capable of providing highly accurate and spatially detailed information about the forest attributes across a forested landscape (Mohan et al 2017;Balsi et al 2018;Wu et al 2019;Wang et al 2019). Liu et al (2018), studying a ginkgo (Ginkgo biloba L.) plantation, evaluated estimates of parameters such as diameter at breast height, Lorey's mean height, stem density, basal area, volume, and aboveground biomass.…”