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

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 (R 2 )) 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 R 2 = 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.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]. Despite their functions and benefits, mangrove forests have been reduced and degraded worldwide, more seriously in South East Asia, where the decimation rate reached its highest level in the last 50 years [3,4]. The driving factors of mangrove deforestation and degradation are conversion to shrimp aquaculture, agriculture (particularly rice and oil palm in West Africa and Southeast Asia), urban development, poor governance, and overexploitation [3,5]. Unfortunately, the loss of mangrove carbon on large spatial scales is little understood. Without this knowledge, we cannot mitigate the global loss of mangrove habitats [6].Land-cover change is thought to alter the above-ground biomass (AGB) in the tropical areas [7-9]. By mapping the spatial distribution of mangrove AGB and the carbon stocks associated with external factors, we could detect the changes in mangrove ecosystems, better understand the drivers of these changes, and reduce the uncertainty in estimating the loss of mangrove ecosystem services. A precise estimation of mangrove AGB is required for sustainably preserving and protecting mangrove ecosystems from loss and degradation under climate change and accelerated global warming...

Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

“…Although the differences between the AGB and CH prediction accuracies obtained by the two algorithms are small, the XGBoost models outperformed most of the RF models in the prediction scenarios analyzed (Tables 4 and 5). The superior performance of XGBoost when compared with other well-known machine learning algorithms in terms of both accuracy and computational cost has also been observed in other remote sensing studies [30][31][32]61]. Our results highlight the potential of using GLCM-based texture measures (SC3) to achieve enhanced AGB (RMSE = 26.52%; R 2 = 0.65) and CH (RMSE = 20.94%; R 2 = 0.89) prediction accuracies when compared to the use of spectral bands (SC1) or vegetation indices (SC3) as predictor variables ( Table 5).…”

“…Machine learning (ML) algorithms are powerful tools to cope with this kind of high-dimensional and complex data [25,26] and have been increasingly used for a wide range of tasks, including pasture monitoring [15,27,28]. The extreme gradient boosting (XGBoost) algorithm, a novel implementation of gradient boosting decision trees [29], has demonstrated excellent performance in many applications due to its high efficiency and impressive accuracy [30][31][32].…”

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

“…Additionally, the soil-adjusted vegetation index (SAVI) was also computed in this study because soil brightness at low vegetation cover is correlated with forest structure [52,53]. Importantly, some potential vegetation indices derived from the red-edge bands of S-2, such as the normalized difference index using bands 4 & 5 of S-2 (NDI45), inverted red-edge chlorophyll index (IRECl), and modified chlorophyll absorption in reflectance index (MCARI), have been frequently used for forest AGB estimation in recent studies because of their excellent explanations of the relationship between biophysical parameters and forest AGB [54,55]. We chose a total of 26 predictor features consisting of 11 multispectral bands of S-2 and 8 VIs derived from S-2, 5 variables (HH, HV, HH/HV, HV/HH, and HH-HV) derived from the ALOS-2 PALSAR-2 data and two backscattering coefficients (VV and VH) from S-1 (Table 3) to generate inputs for the mangrove AGB estimation model.…”

Recent Advancement in Mangrove Forests Mapping and Monitoring of the World Using Earth Observation Satellite Data

“…Pham et al [11] investigated the usefulness of gradient boosting decision tree classification approach to estimate Above-Ground Biomass (AGB) of mangrove forests. This study was conducted in Can Gio Biosphere research in Vietnam.…”