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Forest biomass retrieval approaches from earth observation in different biomes

Rodríguez-Veiga, Pedro and Quegan, Shaun and Carreiras, Joao and Persson, Henrik J. and Fransson, Johan E.S. and Hoscilo, Agata and Ziółkowski, Dariusz and Stereńczak, Krzysztof and Lohberger, Sandra and Stängel, Matthias and Berninger, Anna and Siegert, Florian and Avitabile, Valerio and Herold, Martin and Mermoz, Stéphane and Bouvet, Alexandre and Le Toan, Thuy and Carvalhais, Nuno and Santoro, Maurizio and Cartus, Oliver and Rauste, Yrjö and Mathieu, Renaud and Asner, Gregory P. and Thiel, Christian and Pathe, Carsten and Schmullius, Chris and Seifert, Frank Martin and Tansey, Kevin and Balzter, Heiko (2018) Forest biomass retrieval approaches from earth observation in different biomes. International Journal of Applied Earth Observation and Geoinformation, 77, pp. 53-68. Elsevier. DOI: 10.1016/j.jag.2018.12.008 ISSN 0303-2434

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Official URL: http://dx.doi.org/10.1016/j.jag.2018.12.008

Abstract

The amount and spatial distribution of forest aboveground biomass (AGB) were estimated using a range of regionally developed methods using Earth Observation data for Poland, Sweden and regions in Indonesia (Kalimantan), Mexico (Central Mexico and Yucatan peninsula), and South Africa (Eastern provinces) for the year 2010. These regions are representative of several forest biomes and biomass levels globally, from South African woodlands and savannas to the humid tropical forest of Kalimantan. AGB retrieval in each region relied on different sources of reference data, including forest inventory plot data and airborne LiDAR observations, and used a range of retrieval algorithms. This is the widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods. The accuracy assessment of all regional maps using independent field data or LiDAR AGB maps resulted in an overall root mean square error (RMSE) ranging from 10 t ha-1 to 55 t ha-1 (37% to 67% relative RMSE), and an overall bias ranging from -1 t ha-1 to +5 t ha-1. The regional maps showed better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets. The comparison of accuracy assessments showed commonalities in error structures despite the variety of methods, input data, and forest biomes. All regional retrievals resulted in overestimation (up to 63 t ha-1) in the lower AGB classes, and underestimation (up to 85 t ha-1) in the higher AGB classes. Parametric model-based algorithms present advantages due to their low demand on in situ data compared to non-parametric algorithms, but there is a need for datasets and retrieval methods that can overcome the biases at both ends of the AGB range. The outcomes of this study should be considered when developing algorithms to estimate forest biomass at continental to global scale level.

Item URL in elib:https://elib.dlr.de/131153/
Document Type:Article
Title:Forest biomass retrieval approaches from earth observation in different biomes
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rodríguez-Veiga, PedroUNSPECIFIEDUNSPECIFIED
Quegan, ShaunUNSPECIFIEDUNSPECIFIED
Carreiras, JoaoUNSPECIFIEDUNSPECIFIED
Persson, Henrik J.UNSPECIFIEDUNSPECIFIED
Fransson, Johan E.S.UNSPECIFIEDUNSPECIFIED
Hoscilo, AgataUNSPECIFIEDUNSPECIFIED
Ziółkowski, DariuszUNSPECIFIEDUNSPECIFIED
Stereńczak, KrzysztofUNSPECIFIEDUNSPECIFIED
Lohberger, SandraUNSPECIFIEDUNSPECIFIED
Stängel, MatthiasUNSPECIFIEDUNSPECIFIED
Berninger, AnnaUNSPECIFIEDUNSPECIFIED
Siegert, FlorianUNSPECIFIEDUNSPECIFIED
Avitabile, ValerioUNSPECIFIEDUNSPECIFIED
Herold, MartinUNSPECIFIEDUNSPECIFIED
Mermoz, StéphaneUNSPECIFIEDUNSPECIFIED
Bouvet, AlexandreUNSPECIFIEDUNSPECIFIED
Le Toan, ThuyUNSPECIFIEDUNSPECIFIED
Carvalhais, NunoUNSPECIFIEDUNSPECIFIED
Santoro, MaurizioUNSPECIFIEDUNSPECIFIED
Cartus, OliverUNSPECIFIEDUNSPECIFIED
Rauste, YrjöUNSPECIFIEDUNSPECIFIED
Mathieu, RenaudUNSPECIFIEDUNSPECIFIED
Asner, Gregory P.UNSPECIFIEDUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Pathe, CarstenUNSPECIFIEDUNSPECIFIED
Schmullius, ChrisUNSPECIFIEDUNSPECIFIED
Seifert, Frank MartinUNSPECIFIEDUNSPECIFIED
Tansey, KevinUNSPECIFIEDUNSPECIFIED
Balzter, HeikoUNSPECIFIEDUNSPECIFIED
Date:12 December 2018
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:77
DOI :10.1016/j.jag.2018.12.008
Page Range:pp. 53-68
Publisher:Elsevier
ISSN:0303-2434
Status:Published
Keywords:Forest biomass earth observation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Thiel, Christian
Deposited On:25 Nov 2019 08:53
Last Modified:25 Nov 2019 08:56

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