ALBERT

Description: This data sets include yearly maps of land cover classification for the state of Mato Grosso, Brasil, from 2001 to 2016, based on MODIS image time series at 250 meter spatial resolution. Ground samples consisting of 2,115 time series with known labels are used as training data for a support vector machine classifier. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of Mato Grosso, Brazil's agricultural frontier. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 93% and the following user's and producer's accuracy for the land cover classes: Cerrado: UA - 99% PA - 98% Fallow_Cotton UA - 100% PA - 100% Forest UA - 99% PA - 98% Pasture UA - 95% PA - 96% Soy-Corn UA- 87% PA - 97% Soy-Cotton UA - 99% PA - 94% Soy-Fallow UA - 100% PA - 100% Soy-Millet UA- 84% PA - 84% Soy-Sunflower UA - 85% PA - 85% --- The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2005 to 2016, were equal to 0.98. At state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.98, 0.73, 0.96 and 0.80. --- 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 RDS file (R compressed format) with the training data set (2,115 ground samples). --- The software used to produce the analysis is available as open source on https://github.com/e-sensing. --- 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)"

Description: This data set includes yearly maps of land cover classification for the state of Mato Grosso, Brasil, from 2001 to 2017, 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 BrazilMato Grosso, Brazil's agricultural frontier. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 96% and the following user's and producer's accuracy for the land cover classes: Cerrado: UA - 98% PA - 99% Fallow_Cotton UA - 96% PA - 93% Forest UA - 99% PA - 98% Pasture UA - 97% PA - 98% Soy-Corn UA- 91% PA - 93% Soy-Cotton UA - 97% PA - 97% Soy-Fallow UA - 98% PA - 98% Soy-Millet UA- 90% PA - 89% Soy-Sunflower UA - 77% PA - 65% --- 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 state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.97, 0.85, 0.98 and 0.80. The areas classified as forest were compared with the Hansen et al. (2013) 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 99% 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, 84% of the pixels match the pixels by Hansen et al. (2013, doi:10.1126/science.1244693) as having more than 25% tree cover. The pixels labelled 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 pasture match the pixels indicated by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003) for the state of Mato Grosso. In the data set "Land cover change maps for Mato Grosso State in Brazil version 2", we analysed the samples from the clustering process using self-organizing maps. The samples with high level of confusion were removed from the dataset. In addition, we used a Bayesian smoothing method to reclassify the pixels based on machine learning probabilities associated to each class and each pixel. The main rationale is to change those pixels classes with low certainty (high entropy) to the neighborhood classes with high certainty (low entropy) using a Bayesian inference. To reclassify pixels we used a 3x3 window from which we computed the neighborhood entropy. --- 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). --- The software used to produce the analysis is available as open source on https://github.com/e-sensing. --- 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)"

Description: 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. --- 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. --- 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. --- 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. --- 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. --- 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). --- The software used to produce the analysis is available as open source on https://github.com/e-sensing. --- 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)"

Description: 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%

Description: Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.