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Open AccessArticle

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

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Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Department of Marine Mechanics and Environment, Institute of Mechanics, Vietnam Academy of Science and Technology (VAST), 264 Doi Can street, Ba Dinh district, Hanoi 100000, Vietnam
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Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 530000, Vietnam
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Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New Zealand
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Remote Sensing Application Department, Space Technology Institute, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet street, Cau Giay district, Hanoi 100000, Vietnam
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Thai Nguyen University of Sciences, Tan Thinh Ward, Thai Nguyen City Thai Nguyen University of Sciences, Tan Thinh Ward, Thai Nguyen City 250000, Vietnam
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Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 777; https://doi.org/10.3390/rs12050777
Received: 22 January 2020 / Revised: 22 February 2020 / Accepted: 25 February 2020 / Published: 29 February 2020
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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. View Full-Text
Keywords: Sentinel-2; ALOS-2 PALSAR-2; mangrove; above-ground biomass; extreme gradient boosting; Can Gio biosphere reserve; Vietnam Sentinel-2; ALOS-2 PALSAR-2; mangrove; above-ground biomass; extreme gradient boosting; Can Gio biosphere reserve; Vietnam
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Pham, T.D.; Le, N.N.; Ha, N.T.; Nguyen, L.V.; Xia, J.; Yokoya, N.; To, T.T.; Trinh, H.X.; Kieu, L.Q.; Takeuchi, W. 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. Remote Sens. 2020, 12, 777.

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