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

Abstract

This study proposes a hybrid intelligence approach based on an extreme gradient boosting regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 data to estimate the mangrove above-ground biomass (AGB), including small and shrub mangrove patches in the Red River Delta biosphere reserve across the northern coast of Vietnam. We used the novel extreme gradient boosting decision tree (XGBR) technique together with genetic algorithm (GA) optimization for feature selection to construct and verify a mangrove AGB model using data from a field survey of 105 sampling plots conducted in November and December of 2018 and incorporated the dual polarimetric (HH and HV) data of the ALOS-2 PALSAR-2 L-band and the Sentinel-2 multispectral data combined with Sentinel-1 (C-band VV and VH) data. We employed the root-mean-square error (RMSE) and coefficient of determination (R 2 ) to evaluate the performance of the proposed model. The capability of the XGBR-GA model was assessed via a comparison with other machine-learning (ML) techniques, i.e., the CatBoost regression (CBR), gradient boosted regression tree (GBRT), support vector regression (SVR), and random forest regression (RFR) models. The XGBR-GA model yielded a promising result (R 2 = 0.683, RMSE = 25.08 Mg·ha −1 ) and outperformed the four other ML models. The XGBR-GA model retrieved a mangrove AGB ranging from 17 Mg·ha −1 to 142 Mg·ha −1 (with an average of 72.47 Mg·ha −1 ). Therefore, multisource optical and synthetic aperture radar (SAR) combined with the XGBR-GA model can be used to estimate the mangrove AGB in North Vietnam. The effectiveness of the proposed method needs to be further tested and compared to other mangrove ecosystems in the tropics.

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).…”

“…This algorithm creates a number of decision trees sequentially based on the idea of "boosting", which combines all the predictions of a set of weak learners for developing a strong learner through additive training strategies. XGBoost has showed superiority over other machine learning algorithms and achieved outstanding performances in many research areas [32,[61][62][63].…”

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

“…For a GBM, the base learners are decision trees. Despite being popular in the machine learning community, the GBM and GBRT approaches are rarely applied in forest AGB estimation studies [23,30]. SGB is another implementation of the tree-based gradient boosting approach that builds a base learner from a random subsample drawn from the entire training dataset without replacement at each iteration, and it can thus reduce the risk of overfitting [54,55].…”

“…The results suggested that the linear models performed poorly and that no regression model outperformed the others. Pham et al [30] evaluated the capabilities of the GBRT, SVR, RF, categorical boosting (CatBoost) regression, and their proposed extreme gradient boosting decision tree technique together with a genetic algorithm for feature selection (XGBR-GA) for estimating mangrove AGB from multiple data sources and found that XGBR-GA outperformed the other four machine learning models.…”

Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

“…Besides the conventional satellite sensors, such as Worldview [14] and Pleiades [15], unmanned aerial vehicles (UAVs) [16] have been employed for mangrove mapping, especially for individual mangrove analysis, in which the fuzzy-based and objected-based approaches are often adopted [8,12]. Since SAR images can penetrate the canopy and sensitive to the surface and vertical structure, thus, they are useful for mapping and monitoring mangrove structure and biomass [17,18]. Hence, various SAR datasets, such as ALOS, Radarsat, Sentinel-1, are used to investigate mangroves communities distribution [19][20][21].…”