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Article

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

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Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK
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Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, UK
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NERC National Centre for Earth Observation, University Road, Leicester LE1 7RH, UK
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Amazon Environmental Research Institute (IPAM), Brasília 71503-505, Brazil
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University of Goiás State (UEG), Palmeiras de Goiás 76190-000, Brazil
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Federal University of Goiás (UFG), Goiânia 74690-900, Brazil
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Carbomap Ltd., Edinburgh EH1 1LZ, UK
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Department of Environment, Ghent University, 9000 Ghent, Belgium
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Earth Observation Science, Department of Physics & Astronomy, University of Leicester, Leicester LE1 7RH, UK
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Department of Geology, Geography and Environment, University of Alcalá, 28801 Alcalá de Henares, Madrid, Spain
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Department of Ecology, University of Brasília (UNB) and Brazilian Research Network on Global Climate Change—Rede Clima, Brasília 70910-900, Brazil
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School of Geosciences, University of Edinburgh, Edinburgh EH1 1LZ, UK
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Brazilian Agricultural Research Corporation (Embrapa Cerrados), Brasília 70770-901, Brazil
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Earth System Science Center (CCST), National Institute for Space Research (INPE), Av dos Astronautas 1758, São José dos Campos 12227-010, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(17), 2685; https://doi.org/10.3390/rs12172685
Received: 16 July 2020 / Revised: 13 August 2020 / Accepted: 15 August 2020 / Published: 19 August 2020
(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands)
The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1. View Full-Text
Keywords: aboveground biomass; Cerrado ecosystem; random forest; SAR aboveground biomass; Cerrado ecosystem; random forest; SAR
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MDPI and ACS Style

Bispo, P.d.C.; Rodríguez-Veiga, P.; Zimbres, B.; do Couto de Miranda, S.; Henrique Giusti Cezare, C.; Fleming, S.; Baldacchino, F.; Louis, V.; Rains, D.; Garcia, M.; Del Bon Espírito-Santo, F.; Roitman, I.; Pacheco-Pascagaza, A.M.; Gou, Y.; Roberts, J.; Barrett, K.; Ferreira, L.G.; Shimbo, J.Z.; Alencar, A.; Bustamante, M.; Woodhouse, I.H.; Eyji Sano, E.; Ometto, J.P.; Tansey, K.; Balzter, H. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sens. 2020, 12, 2685. https://doi.org/10.3390/rs12172685

AMA Style

Bispo PdC, Rodríguez-Veiga P, Zimbres B, do Couto de Miranda S, Henrique Giusti Cezare C, Fleming S, Baldacchino F, Louis V, Rains D, Garcia M, Del Bon Espírito-Santo F, Roitman I, Pacheco-Pascagaza AM, Gou Y, Roberts J, Barrett K, Ferreira LG, Shimbo JZ, Alencar A, Bustamante M, Woodhouse IH, Eyji Sano E, Ometto JP, Tansey K, Balzter H. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sensing. 2020; 12(17):2685. https://doi.org/10.3390/rs12172685

Chicago/Turabian Style

Bispo, Polyanna d.C., Pedro Rodríguez-Veiga, Barbara Zimbres, Sabrina do Couto de Miranda, Cassio Henrique Giusti Cezare, Sam Fleming, Francesca Baldacchino, Valentin Louis, Dominik Rains, Mariano Garcia, Fernando Del Bon Espírito-Santo, Iris Roitman, Ana M. Pacheco-Pascagaza, Yaqing Gou, John Roberts, Kirsten Barrett, Laerte G. Ferreira, Julia Z. Shimbo, Ane Alencar, Mercedes Bustamante, Iain H. Woodhouse, Edson Eyji Sano, Jean P. Ometto, Kevin Tansey, and Heiko Balzter. 2020. "Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach" Remote Sensing 12, no. 17: 2685. https://doi.org/10.3390/rs12172685

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