Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2022

Идентификатор DOI: 10.1016/j.rse.2022.112917

Ключевые слова: agb, carbon cycle, map validation, remote sensing, uncertainty assessment

Аннотация: Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement. © 2022 The Authors

Журнал: Remote Sensing of Environment

Выпуск журнала: Vol. 272

Номера страниц: 112917

ISSN журнала: 00344257

Издатель: Elsevier Inc.

• Araza A. (Laboratory of Geo-information and Remote sensing, Wageningen University and Research, Droevendaalsesteeg 3, Wageningen, 6708 PB, Netherlands, Environmental Systems Analysis, Wageningen University and Research, Droevendaalsesteeg 3a, Wageningen, 6708 PB, Netherlands)
• de Bruin S. (Laboratory of Geo-information and Remote sensing, Wageningen University and Research, Droevendaalsesteeg 3, Wageningen, 6708 PB, Netherlands)
• Herold M. (Laboratory of Geo-information and Remote sensing, Wageningen University and Research, Droevendaalsesteeg 3, Wageningen, 6708 PB, Netherlands, Helmholtz GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Potsdam, 14473, Germany)
• Quegan S. (National Centre for Earth Observation (NCEO) and University of Sheffield, Hicks Building, Sheffield, S3 7RH, United Kingdom)
• Labriere N. (Laboratoire Évolution et Diversité Biologique (EDB), UMR 5174 (CNRS/IRD/UPS), 118 route de Narbonne, Cedex 9, Toulouse, 31062, France, Université Toulouse, 41 Allées Jules Guesde, CS 61321, Cedex 6, Toulouse, 31013, France)
• Rodriguez-Veiga P. (Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of LeicesterLE1 7RH, United Kingdom, National Centre for Earth Observation, University of Leicester, Space Park Leicester, Leicester, LE4 5SP, United Kingdom)
• Avitabile V. (European Commission, Joint Research Centre (JRC), European Commission, Ispra, Italy)
• Santoro M. (Gamma Remote Sensing, Worbstrasse 225, Gümligen, Switzerland)
• Mitchard E.T.A. (School of GeoSciences, University of Edinburgh, Crew Building, The King's BuildingsEH9 3FF, United Kingdom)
• Ryan C.M. (School of GeoSciences, University of Edinburgh, Crew Building, The King's BuildingsEH9 3FF, United Kingdom)
• Phillips O.L. (School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, United Kingdom)
• Willcock S. (School of Natural Sciences, Bangor University, United Kingdom, Rothamsted Research, West Common, Harpenden, AL5 2JQ, United Kingdom)
• Verbeeck H. (Computational and Applied Vegetation Ecology, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium)
• Carreiras J. (National Centre for Earth Observation (NCEO) and University of Sheffield, Hicks Building, Sheffield, S3 7RH, United Kingdom)
• Hein L. (Environmental Systems Analysis, Wageningen University and Research, Droevendaalsesteeg 3a, Wageningen, 6708 PB, Netherlands)
• Schelhaas M.-J. (Wageningen Environmental Research, Wageningen University and Research, Droevendaalsesteeg 3-3 A, Wageningen, 6708 PB, Netherlands)
• Pacheco-Pascagaza A.M. (Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of LeicesterLE1 7RH, United Kingdom, Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester, United Kingdom)
• da Conceição Bispo P. (Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester, United Kingdom)
• Laurin G.V. (Department for Innovation in Biological, Agro-food, and Forestry Systems, Tuscia University, Viterbo, Italy)
• Vieilledent G. (AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France)
• Slik F. (Environmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Brunei Darussalam)
• Wijaya A. (Research, Data and Innovation Department, World Resources Institute Indonesia, Jl. Wijaya I/63, South Jakarta, Indonesia)
• Lewis S.L. (Department of Geography, University College London, London, WC1E 6BT, United Kingdom, School of Geography, University of Leeds, Woodhouse, Leeds, LS2 9JT, United Kingdom)
• Morel A. (Department of Geography and Environmental Sciences, University of Dundee, Nethergate, Dundee, DD1 4HN, United Kingdom)
• Liang J. (Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, United States)
• Sukhdeo H. (Guyana Forestry Commission, 1 Water Street Kingston, Georgetown, Guyana)
• Schepaschenko D. (International Institute for Applied Systems Analysis, Laxenburg, 2361, Austria, Center of Forest Ecology and Productivity of the Russian Academy of Sciences, Moscow, 117997, Russian Federation, Institute of Ecology and Geography, Siberian Federal University, Krasnoyarsk, 660041, Russian Federation)
• Cavlovic J. (University of Zagreb, Faculty of Forestry and Wood Technology, Department of Forest Inventory and Management, Croatia)
• Gilani H. (Institute of Space Technology, 1 Islamabad Highway, Islamabad, 44000, Pakistan)
• Lucas R. (Department of Geography and Earth Sciences, Aberystwyth University, Ceredigion, Aberystwyth, SY23 3DB, United Kingdom)

• Scopus
• Web of Science Core Collection
• Ядро РИНЦ (eLIBRARY.RU)