Câmara, Gilberto; Picoli, Michelle; Maciel, Adeline; Simoes, Rolf; Santos, Lorena; Andrade, Pedro R; Ferreira, Karine; Begotti, Rodrigo; Sanches, Ieda; Carvalho, Alexandre X Y; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Arvor, Damien (2019): Land cover change maps for Mato Grosso State in Brazil: 2001-2017 (version 3) [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.899706
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Abstract:
These data sets include yearly maps of land cover classification for the state of Mato Grosso, Brazil, from 2001 (2000-09-01 to 2001-08-31) to 2017 (2016-09-01 to 2017-08-31), based on MODIS image time series (collection 6) at 250-meter spatial resolution (product MOD13Q1). Ground samples consisting of 1,892 time series with known labels are used as training data for a support vector machine classifier. We used the radial basis function kernel, with cost C=1 and gamma = 0.01086957. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of land cover change maps for Mato Grosso State in Brazil. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion.
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The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2001 to 2017, were equal to 0.98. At the state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.97, 0.85, 0.98 and 0.80.
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The areas classified as forest were compared with the Hansen et al. (2013, doi:10.1126/science.1244693) mapping for the year 2000. In order to separate the forest areas, we examined the areas with more than 25% tree cover on the Hansen et al. (2013, doi:10.1126/science.1244693) map. We found that 98% of the pixels classified as forest match the pixels indicated by Hansen et al. (2013) as having more than 25% tree cover. When we joined the cerrado and forest classes, 83% of the pixels match the pixels by Hansen et al. (2013) as having more than 25% tree cover.
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The pixels labeled as pasture were compared to the pasture mapping done by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003). We found that 80% of the pixels classified as forest match the pixels indicated by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003) for the state of Mato Grosso.
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In the land cover change maps for Mato Grosso State in Brazil version 3, we applied a methodology to deal with trajectories in classified maps. This methodology for reasoning about land-use change trajectories, called LUC Calculus, has been discussed in previous work (Maciel et al., 2018, doi:10.1080/13658816.2018.1520235). For reducing the temporal variability, we use the entire history of the study area considered as a set of land-use trajectories (from 2001 to 2017). For reasoning about this, we adopt the reference date 2001 and we used two-step post-processing, first applying masks and rules on the initial classified map (2001) and then land-use rules using LUC Calculus for the all years (2001-2017). The first-step post-processing was performed on the initial classified map (2001). We applied the forest mask to the classified map of the year 2001. This forest mask comes from the PRODES Project (http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes). In the non-forest, the appearance of secondary vegetation is not mapped. An additional set of rules was applied on the initial map using two sets of maps: PRODES map of the year 2001 and Cerrado map of 2000 (http://www.obt.inpe.br/cerrado). This mask of the Cerrado biome depicts the Cerrado within two classes: Anthropized Cerrado and Non-Anthropized Cerrado. The second-step post-processing was carried on the entire years from the classified map (2001-2017) using the LUC Calculus method. First, we elaborate a set of rules defined by experts in Amazon and Cerrado biomes. These rules express information about different trajectories of land-use change in MT that represent an irregular transition between classes. The rules used was: Forest (F), Cerrado (C), Pasture (P) and Soybean (S)
1. C -> F* to C -> C*
2. C -> C -> P* -> C to C -> C -> C* -> C
3. C -> C -> S* -> C to C -> C -> C* -> C
4. P -> P -> C* -> C* -> P to P -> P -> P* -> P* -> P
5. F -> C* -> F -> F to F -> F* -> F -> F
6. F -> F -> C* -> F to F -> F -> F* -> F
7. F -> C* -> F to F -> F* -> F
8. F -> C* to F -> F*
9. F -> F -> P -> F* to F -> F -> P -> SV*
10. P -> P -> F* -> P to P -> P -> SV* -> P
The sequential application of the rules is able to ensure the temporal consistency among classes over the years. The class changed is highlighted with "*". From rule 1 to 8 we assume the reference date, 2001, as the starting point to find the class to will be changed. Rules 9 and 10 exemplify scenery where new class secondary vegetation (SV) occurs. The trajectory methodology enables us to include a new class called 'secondary vegetation'. This class represents a significant portion of the deforestation areas that have fallen into disuse or abandoned and have regrown as secondary forest.
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The following data sets are provided: (a) The classified maps in compressed TIFF format (one per year) at MODIS resolution. (b) A QGIS style file for displaying the data in the QGIS software (c) An csv file with the training data set (1,892 ground samples).
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The software used to produce the analysis is available as open source on https://github.com/e-sensing.
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Note: The TIFF raster files use the Sinusoidal Projection, which is the same cartographical projection used by the input MODIS images. When opening the TIFF raster maps in QGIS, to ensure correct navigation please use the Sinusoidal Projection, by selecting in QGIS projection menu, the following option: "Generated CRS (+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs)"
Related to:
Maciel, Adeline; Câmara, Gilberto; Vinhas, Lubia; Picoli, Michelle; Begotti, Rodrigo; Assis, Luiz Fernando (2019): A spatiotemporal calculus for reasoning about land-use trajectories. International Journal of Geographical Information Science, 33(1), 176-192, https://doi.org/10.1080/13658816.2018.1520235
Picoli, Michelle; Câmara, Gilberto; Sanches, Ieda; Simoes, Rolf; Carvalho, Alexandre X Y; Maciel, Adeline; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Begotti, Rodrigo; Arvor, Damien; Almeida, Claudio (2018): Big earth observation time series analysis for monitoring Brazilian agriculture. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 328-339, https://doi.org/10.1016/j.isprsjprs.2018.08.007
Previous version:
Câmara, Gilberto; Picoli, Michelle; Simoes, Rolf; Maciel, Adeline; Carvalho, Alexandre X Y; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Begotti, Rodrigo; Arvor, Damien (2017): Land cover change maps for Mato Grosso State in Brazil: 2001-2016, links to files. PANGAEA, https://doi.org/10.1594/PANGAEA.881291
Câmara, Gilberto; Picoli, Michelle; Simoes, Rolf; Maciel, Adeline; Carvalho, Alexandre X Y; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Begotti, Rodrigo; Arvor, Damien; Santos, Lorena (2018): Land cover change maps for Mato Grosso State in Brazil: 2001-2017 (Version 2), links to files. PANGAEA, https://doi.org/10.1594/PANGAEA.895495
Coverage:
Latitude: -12.700000 * Longitude: -56.000000
Event(s):
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File content | Content | Câmara, Gilberto | |||
| 2 | File format | File format | Câmara, Gilberto | |||
| 3 | File size | File size | kByte | Câmara, Gilberto | ||
| 4 | File name | File name | Câmara, Gilberto | |||
| 5 | Uniform resource locator/link to file | URL file | Câmara, Gilberto |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
95 data points