Abstract
This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.
Highlights
Mangrove forests are among the most important components of natural ecosystems
The S-2 and ALOS-2 PALSAR-2 images were processed by the SNAP toolbox, and the modeling process was performed in Python 3.7 environment using the Scikit-learn library [46]
The datasets are input to the XGBR model, which ranks the variables in descending order of their importance based on the root mean squared error (RMSE) and the coefficient of determination (R2)
Summary
Mangrove forests are among the most important components of natural ecosystems They perform a wide range of crucial functions, such as mitigating the effects of tropical typhoons and tsunami, reducing coastal erosion, and storing huge amounts of blue carbon [1,2]. Mangrove AGB can be accurately estimated from field-based measurements or forest inventory data. 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]. Fused data are useful in biosphere reserves comprising multiple mangrove species and rich biodiversity In such systems, the spatial distribution of the mangrove AGB is difficult to estimate with sufficient accuracy. FiguFreig3u.re F3l.oFwlocwhcahratrftofrorssaatteelllliittee--iimmaaggee pprroocecsessisnignganadntdhethgeengeeranteiornatoiofnAGofBAmGodBelms obadseelds obnasMedL on ML tteecchhnniiqquueess
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