气候变化领域集成服务门户(CCPortal): Forest biomass retrieval approaches from earth observation in different biomes

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DOI	10.1016/j.jag.2018.12.008
	Forest biomass retrieval approaches from earth observation in different biomes
	Rodríguez-Veiga P.; Quegan S.; Carreiras J.; Persson H.J.; Fransson J.E.S.; Hoscilo A.; Ziółkowski D.; Stereńczak K.; Lohberger S.; Stängel M.; Berninger A.; Siegert F.; Avitabile V.; Herold M.; Mermoz S.; Bouvet A.; Le Toan T.; Carvalhais N.; Santoro M.; Cartus O.; Rauste Y.; Mathieu R.; Asner G.P.; Thiel C.; Pathe C.; Schmullius C.; Seifert F.M.; Tansey K.; Balzter H.
发表日期	2019
ISSN	15698432
起始页码	53
结束页码	68
卷号	77
英文摘要	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 numerous 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 at pixel level. 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. © 2019 The Authors
英文关键词	Aboveground biomass; Carbon cycle; Forest biomes; Forest plots; LiDAR; Optical; SAR
语种	英语
scopus关键词	aboveground biomass; algorithm; biome; carbon cycle; EOS; forest inventory; lidar; optical method; spatial distribution; synthetic aperture radar; Borneo; Indonesia; Kalimantan; Mexico [North America]; Poland [Central Europe]; South Africa; Sweden; Yucatan Peninsula
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156501
作者单位	University of Leicester, Centre for Landscape and Climate Research, United Kingdom; National Centre for Earth Observation, United Kingdom; University of Sheffield, United Kingdom; Sveriges Lantbruksuniversitet, Sweden; Institute of Geodesy and Cartography, Poland; Forest Research Institute, Poland; Remote Sensing Solutions, Germany; Ludwig-Maximilians-University Munich, Germany; Wageningen University & Research, Netherlands; CESBIO, Université de Toulouse, CNES, CNRS, IRD, UPS, France; Max Planck Institute for Biogeochemistry, Germany; Universidade NOVA de Lisboa, CENSE, Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Portugal; GAMMA Remote Sensing, Switzerland; VTT Technical Research Centre of Finland Ltd, Finland; CSIR, South Africa; University of Pretoria, Department of Geography, Geomatics and Meteorology, South Africa; Carnegie Institution for Science, United States; German Aerospace Agency, Germany; Friedrich-Schiller-University Jena, Germany; European Space Ag...
推荐引用方式 GB/T 7714	Rodríguez-Veiga P.,Quegan S.,Carreiras J.,et al. Forest biomass retrieval approaches from earth observation in different biomes[J],2019,77.
APA	Rodríguez-Veiga P..,Quegan S..,Carreiras J..,Persson H.J..,Fransson J.E.S..,...&Balzter H..(2019).Forest biomass retrieval approaches from earth observation in different biomes.International Journal of Applied Earth Observation and Geoinformation,77.
MLA	Rodríguez-Veiga P.,et al."Forest biomass retrieval approaches from earth observation in different biomes".International Journal of Applied Earth Observation and Geoinformation 77(2019).