Land use and land cover maps for Amazon biome in Brazil for 2001-2019 derived from MODIS time series
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Dataset Open Access |
| Lenguaje: | en |
| Publicado: |
PANGAEA
2020
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| _version_ | 1867171017658466304 |
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| author | Câmara, Gilberto Simoes, Rolf Picoli, Michelle Andrade, Pedro R Rorato, Ana Santos, Lorena Maciel, Adeline Sanches, Ieda Coutinho, Alexandre Esquerdo, Julio Antunes, Joao Arvor, Damien Begotti, Rodrigo Sanchez, Alber Queiroz, Gilberto Ferreira, Karine |
| author_facet | Câmara, Gilberto Simoes, Rolf Picoli, Michelle Andrade, Pedro R Rorato, Ana Santos, Lorena Maciel, Adeline Sanches, Ieda Coutinho, Alexandre Esquerdo, Julio Antunes, Joao Arvor, Damien Begotti, Rodrigo Sanchez, Alber Queiroz, Gilberto Ferreira, Karine |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | This dataset contains the yearly maps of land use and land cover classification for Amazon biome, Brazil, from 2000 to 2019 at 250 meters of spatial resolution. We used image time series from MOD13Q1 product from MODIS (collection 6), with four bands (NDVI, EVI, near-infrared, and mid-infrared) as data input. A deep learning classification MLP network consisting of 4 hidden layers with 512 units was trained using a set of 33,052 time series of 12 known classes from both natural and anthropic land covers. Quality assessment using 5-fold cross-validation of the training samples indicates an overall accuracy of 99.22% and the following user's and producer's accuracy for the land cover classes: ProdAcc UserAcc Forest 99.80% 99.86% Pasture 98.72% 98.04% Soy_Corn 98.92% 99.06% Soy_Cotton 99.23% 99.25% Fallow_Cotton 95.74% 96.43% Millet_Cotton 100.00% 97.98% Soy_Fallow 99.76% 99.09% Savanna2 99.94% 99.47% Savanna1 98.18% 99.06% Wetlands 99.31% 98.19% Soy_Millet 76.67% 84.66% Soy_Sunflower 84.62% 78.57% |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_911560 |
| institution | PANGAEA |
| language | en |
| publishDate | 2020 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Land use and land cover maps for Amazon biome in Brazil for 2001-2019 derived from MODIS time series Câmara, Gilberto Simoes, Rolf Picoli, Michelle Andrade, Pedro R Rorato, Ana Santos, Lorena Maciel, Adeline Sanches, Ieda Coutinho, Alexandre Esquerdo, Julio Antunes, Joao Arvor, Damien Begotti, Rodrigo Sanchez, Alber Queiroz, Gilberto Ferreira, Karine Amazon; Amazon_Brazil; Amazonia_Brazil-Bolivia; Brazilian Amazonia; File content; File format; File name; File size; land use classification; LUCC; MODIS; MULT; Multiple investigations; SAT; Satellite remote sensing; tropical forest; Uniform resource locator/link to file This dataset contains the yearly maps of land use and land cover classification for Amazon biome, Brazil, from 2000 to 2019 at 250 meters of spatial resolution. We used image time series from MOD13Q1 product from MODIS (collection 6), with four bands (NDVI, EVI, near-infrared, and mid-infrared) as data input. A deep learning classification MLP network consisting of 4 hidden layers with 512 units was trained using a set of 33,052 time series of 12 known classes from both natural and anthropic land covers. Quality assessment using 5-fold cross-validation of the training samples indicates an overall accuracy of 99.22% and the following user's and producer's accuracy for the land cover classes: ProdAcc UserAcc Forest 99.80% 99.86% Pasture 98.72% 98.04% Soy_Corn 98.92% 99.06% Soy_Cotton 99.23% 99.25% Fallow_Cotton 95.74% 96.43% Millet_Cotton 100.00% 97.98% Soy_Fallow 99.76% 99.09% Savanna2 99.94% 99.47% Savanna1 98.18% 99.06% Wetlands 99.31% 98.19% Soy_Millet 76.67% 84.66% Soy_Sunflower 84.62% 78.57% |
| title | Land use and land cover maps for Amazon biome in Brazil for 2001-2019 derived from MODIS time series |
| topic | Amazon; Amazon_Brazil; Amazonia_Brazil-Bolivia; Brazilian Amazonia; File content; File format; File name; File size; land use classification; LUCC; MODIS; MULT; Multiple investigations; SAT; Satellite remote sensing; tropical forest; Uniform resource locator/link to file |
| url | https://doi.org/10.1594/PANGAEA.911560 |