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

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

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Main Authors: BISPO, P. da C., RODRÍGUEZ-VEIGA, P., ZIMBRES, B., MIRANDA, S. do C. de, CEZARE, C. H. G., FLEMING, S., BALDACCHINO, F., LOUIS, V., RAINS, D., GARCIA, M., ESPIRITO-SANTO, F. D. B., 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., SANO, E. E., OMETTO, J. P., TANSEY, K., BALZTER, H.
Other Authors: EDSON EYJI SANO, CPAC.
Format: Artigo de periódico biblioteca
Language:Portugues
pt_BR
Published: 2020-12-14
Subjects:Biomassa, Cerrado, Sensoriamento Remoto, Carbono,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1128070
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spelling dig-alice-doc-11280702020-12-15T09:04:37Z Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. BISPO, P. da C. RODRÍGUEZ-VEIGA, P. ZIMBRES, B. MIRANDA, S. do C. de CEZARE, C. H. G. FLEMING, S. BALDACCHINO, F. LOUIS, V. RAINS, D. GARCIA, M. ESPIRITO-SANTO, F. D. B. 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. SANO, E. E. OMETTO, J. P. TANSEY, K. BALZTER, H. EDSON EYJI SANO, CPAC. Biomassa Cerrado Sensoriamento Remoto Carbono 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 2020-12-15T09:04:29Z 2020-12-15T09:04:29Z 2020-12-14 2020 Artigo de periódico Remote Sensing, v. 12, n. 17, 2020. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1128070 Portugues pt_BR openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language Portugues
pt_BR
topic Biomassa
Cerrado
Sensoriamento Remoto
Carbono
Biomassa
Cerrado
Sensoriamento Remoto
Carbono
spellingShingle Biomassa
Cerrado
Sensoriamento Remoto
Carbono
Biomassa
Cerrado
Sensoriamento Remoto
Carbono
BISPO, P. da C.
RODRÍGUEZ-VEIGA, P.
ZIMBRES, B.
MIRANDA, S. do C. de
CEZARE, C. H. G.
FLEMING, S.
BALDACCHINO, F.
LOUIS, V.
RAINS, D.
GARCIA, M.
ESPIRITO-SANTO, F. D. B.
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.
SANO, E. E.
OMETTO, J. P.
TANSEY, K.
BALZTER, H.
Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
description 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
author2 EDSON EYJI SANO, CPAC.
author_facet EDSON EYJI SANO, CPAC.
BISPO, P. da C.
RODRÍGUEZ-VEIGA, P.
ZIMBRES, B.
MIRANDA, S. do C. de
CEZARE, C. H. G.
FLEMING, S.
BALDACCHINO, F.
LOUIS, V.
RAINS, D.
GARCIA, M.
ESPIRITO-SANTO, F. D. B.
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.
SANO, E. E.
OMETTO, J. P.
TANSEY, K.
BALZTER, H.
format Artigo de periódico
topic_facet Biomassa
Cerrado
Sensoriamento Remoto
Carbono
author BISPO, P. da C.
RODRÍGUEZ-VEIGA, P.
ZIMBRES, B.
MIRANDA, S. do C. de
CEZARE, C. H. G.
FLEMING, S.
BALDACCHINO, F.
LOUIS, V.
RAINS, D.
GARCIA, M.
ESPIRITO-SANTO, F. D. B.
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.
SANO, E. E.
OMETTO, J. P.
TANSEY, K.
BALZTER, H.
author_sort BISPO, P. da C.
title Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
title_short Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
title_full Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
title_fullStr Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
title_full_unstemmed Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach.
title_sort woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach.
publishDate 2020-12-14
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1128070
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