Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI

Forest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local and regional AGB mapping is crucial for understanding global carbon stock dynamics. NASA’s global ecosystem dynamics investigation (GEDI) and combination of multi-source optical and synthetic aper...

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Main Authors: Chu Wang, Wangfei Zhang, Yongjie Ji, Armando Marino, Chunmei Li, Lu Wang, Han Zhao, Mengjin Wang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Forests
Subjects:
Online Access:https://www.mdpi.com/1999-4907/15/1/215
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author Chu Wang
Wangfei Zhang
Yongjie Ji
Armando Marino
Chunmei Li
Lu Wang
Han Zhao
Mengjin Wang
author_facet Chu Wang
Wangfei Zhang
Yongjie Ji
Armando Marino
Chunmei Li
Lu Wang
Han Zhao
Mengjin Wang
author_sort Chu Wang
collection DOAJ
description Forest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local and regional AGB mapping is crucial for understanding global carbon stock dynamics. NASA’s global ecosystem dynamics investigation (GEDI) and combination of multi-source optical and synthetic aperture radar (SAR) datasets have great potential for local and regional AGB estimation and mapping. In this study, GEDI L4A AGB data and ground sample plots worked as true AGB values to explore their difference for estimating forest AGB using Sentinel-1 (S1), Sentinel-2 (S2), and ALOS PALSAR-2 (PALSAR) data, individually and in their different combinations. The effects of forest types and different true AGB values for validation were investigated in this study, as well. The combination of S1 and S2 performed best in forest AGB estimation with <i>R</i><sup>2</sup> ranging from 0.79 to 0.84 and <i>RMSE</i> ranging from 7.97 to 29.42 Mg/ha, with the ground sample plots used as ground truth data. While for GEDI L4A AGB product working as reference, <i>R</i><sup>2</sup> values range from 0.36 to 0.47 and <i>RMSE</i> values range from 31.41 to 37.50 Mg/ha. The difference between using GEDI L4A and ground sample plot as reference shows obvious dependence on forest types. In summary, optical dataset and its combination with SAR performed better in forest AGB estimation when the average AGB is less than 150 Mg/ha. The AGB predictions from GEDI L4A AGB product used as reference underperformed across the different forest types and study sites. However, GEDI can work as ground truth data source for forest AGB estimation in a certain level of estimation accuracy.
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spelling doaj.art-59106eb3a8b6439f8980d9963f5962022024-01-26T16:35:02ZengMDPI AGForests1999-49072024-01-0115121510.3390/f15010215Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDIChu Wang0Wangfei Zhang1Yongjie Ji2Armando Marino3Chunmei Li4Lu Wang5Han Zhao6Mengjin Wang7College of Forestry, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaSchool of Geography and Ecotourism, Southwest Forestry University, Kunming 650224, ChinaBiological and Environmental Sciences, The University of Stirling, Stirling FK9 4LA, UKChina Spacesat Co., Ltd., Beijing 100081, ChinaSchool of Geography and Ecotourism, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaForest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local and regional AGB mapping is crucial for understanding global carbon stock dynamics. NASA’s global ecosystem dynamics investigation (GEDI) and combination of multi-source optical and synthetic aperture radar (SAR) datasets have great potential for local and regional AGB estimation and mapping. In this study, GEDI L4A AGB data and ground sample plots worked as true AGB values to explore their difference for estimating forest AGB using Sentinel-1 (S1), Sentinel-2 (S2), and ALOS PALSAR-2 (PALSAR) data, individually and in their different combinations. The effects of forest types and different true AGB values for validation were investigated in this study, as well. The combination of S1 and S2 performed best in forest AGB estimation with <i>R</i><sup>2</sup> ranging from 0.79 to 0.84 and <i>RMSE</i> ranging from 7.97 to 29.42 Mg/ha, with the ground sample plots used as ground truth data. While for GEDI L4A AGB product working as reference, <i>R</i><sup>2</sup> values range from 0.36 to 0.47 and <i>RMSE</i> values range from 31.41 to 37.50 Mg/ha. The difference between using GEDI L4A and ground sample plot as reference shows obvious dependence on forest types. In summary, optical dataset and its combination with SAR performed better in forest AGB estimation when the average AGB is less than 150 Mg/ha. The AGB predictions from GEDI L4A AGB product used as reference underperformed across the different forest types and study sites. However, GEDI can work as ground truth data source for forest AGB estimation in a certain level of estimation accuracy.https://www.mdpi.com/1999-4907/15/1/215GEDI L4A AGB productoptical datasetsSAR datasetsground sample plotsAGB estimationRF
spellingShingle Chu Wang
Wangfei Zhang
Yongjie Ji
Armando Marino
Chunmei Li
Lu Wang
Han Zhao
Mengjin Wang
Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
Forests
GEDI L4A AGB product
optical datasets
SAR datasets
ground sample plots
AGB estimation
RF
title Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
title_full Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
title_fullStr Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
title_full_unstemmed Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
title_short Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI
title_sort estimation of aboveground biomass for different forest types using data from sentinel 1 sentinel 2 alos palsar 2 and gedi
topic GEDI L4A AGB product
optical datasets
SAR datasets
ground sample plots
AGB estimation
RF
url https://www.mdpi.com/1999-4907/15/1/215
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