Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning
Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability...
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/11/2510 |
| _version_ | 1797491991057006592 |
|---|---|
| author | Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Dhemerson E. Conciani Diego Pereira Costa Natalia Crusco Soltan Galano Duverger Nilson Clementino Ferreira Washington Franca-Rocha Heinrich Hasenack Luiz Felipe Morais Martenexen Valderli J. Piontekowski Noely Vicente Ribeiro Eduardo Reis Rosa Marcos Reis Rosa Sarah Moura B. dos Santos Julia Z. Shimbo Eduardo Vélez-Martin |
| author_facet | Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Dhemerson E. Conciani Diego Pereira Costa Natalia Crusco Soltan Galano Duverger Nilson Clementino Ferreira Washington Franca-Rocha Heinrich Hasenack Luiz Felipe Morais Martenexen Valderli J. Piontekowski Noely Vicente Ribeiro Eduardo Reis Rosa Marcos Reis Rosa Sarah Moura B. dos Santos Julia Z. Shimbo Eduardo Vélez-Martin |
| author_sort | Ane A. C. Alencar |
| collection | DOAJ |
| description | Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km<sup>2</sup>) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil. |
| first_indexed | 2024-03-10T00:56:12Z |
| format | Article |
| id | doaj.art-df17425c20a84877be11a1ccdbff4ee7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:56:12Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-df17425c20a84877be11a1ccdbff4ee72023-11-23T14:42:54ZengMDPI AGRemote Sensing2072-42922022-05-011411251010.3390/rs14112510Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep LearningAne A. C. Alencar0Vera L. S. Arruda1Wallace Vieira da Silva2Dhemerson E. Conciani3Diego Pereira Costa4Natalia Crusco5Soltan Galano Duverger6Nilson Clementino Ferreira7Washington Franca-Rocha8Heinrich Hasenack9Luiz Felipe Morais Martenexen10Valderli J. Piontekowski11Noely Vicente Ribeiro12Eduardo Reis Rosa13Marcos Reis Rosa14Sarah Moura B. dos Santos15Julia Z. Shimbo16Eduardo Vélez-Martin17Instituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilPrograma de Pós-Graduação em Modelagem em Ciência da Terra e do Ambiente (PPGM), Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BrazilArcPlan Sigga, São Paulo 04140-060, BrazilPrograma de Pós-Graduação em Modelagem em Ciência da Terra e do Ambiente (PPGM), Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BrazilLaboratório de Processamento de Imagens e Geoprocessamento (LAPIG), Instituto de Estudos Socioambientais, Universidade Federal de Goiás (UFG), Av. Esperança, s/n—Chácaras de Recreio Samambaia, Goiânia 74690-900, BrazilPrograma de Pós-Graduação em Modelagem em Ciência da Terra e do Ambiente (PPGM), Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BrazilCentro de Ecologia, Instituto de Biociências, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilLaboratório de Processamento de Imagens e Geoprocessamento (LAPIG), Instituto de Estudos Socioambientais, Universidade Federal de Goiás (UFG), Av. Esperança, s/n—Chácaras de Recreio Samambaia, Goiânia 74690-900, BrazilArcPlan Sigga, São Paulo 04140-060, BrazilPrograma de Pós-Graduação em Modelagem em Ciência da Terra e do Ambiente (PPGM), Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BrazilPrograma de Pós-Graduação em Modelagem em Ciência da Terra e do Ambiente (PPGM), Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BrazilInstituto de Pesquisa Ambiental da Amazônia (IPAM), SCN 211, Bloco B, Sala 201, Brasília 70836-520, BrazilGeoKarten Consultoria em Tecnologia da Informação, Roca Sales 95735-000, BrazilFire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km<sup>2</sup>) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.https://www.mdpi.com/2072-4292/14/11/2510fireburned areamachine learningBrazilLandsatfire regime |
| spellingShingle | Ane A. C. Alencar Vera L. S. Arruda Wallace Vieira da Silva Dhemerson E. Conciani Diego Pereira Costa Natalia Crusco Soltan Galano Duverger Nilson Clementino Ferreira Washington Franca-Rocha Heinrich Hasenack Luiz Felipe Morais Martenexen Valderli J. Piontekowski Noely Vicente Ribeiro Eduardo Reis Rosa Marcos Reis Rosa Sarah Moura B. dos Santos Julia Z. Shimbo Eduardo Vélez-Martin Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning Remote Sensing fire burned area machine learning Brazil Landsat fire regime |
| title | Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning |
| title_full | Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning |
| title_fullStr | Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning |
| title_full_unstemmed | Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning |
| title_short | Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning |
| title_sort | long term landsat based monthly burned area dataset for the brazilian biomes using deep learning |
| topic | fire burned area machine learning Brazil Landsat fire regime |
| url | https://www.mdpi.com/2072-4292/14/11/2510 |
| work_keys_str_mv | AT aneacalencar longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT veralsarruda longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT wallacevieiradasilva longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT dhemersoneconciani longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT diegopereiracosta longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT nataliacrusco longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT soltangalanoduverger longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT nilsonclementinoferreira longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT washingtonfrancarocha longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT heinrichhasenack longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT luizfelipemoraismartenexen longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT valderlijpiontekowski longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT noelyvicenteribeiro longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT eduardoreisrosa longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT marcosreisrosa longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT sarahmourabdossantos longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT juliazshimbo longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning AT eduardovelezmartin longtermlandsatbasedmonthlyburnedareadatasetforthebrazilianbiomesusingdeeplearning |