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Big earth observation time series analysis for monitoring Brazilian agriculture

Picoli, Michelle; Camara, Gilberto; Sanches, Ieda; Simoes, Rolf; Carvalho, Alexandre; Maciel, Adeline; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Begotti, Rodrigo; Arvor, Damien; Almeida, Claudio


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  <dc:creator>Picoli, Michelle</dc:creator>
  <dc:creator>Camara, Gilberto</dc:creator>
  <dc:creator>Sanches, Ieda</dc:creator>
  <dc:creator>Simoes, Rolf</dc:creator>
  <dc:creator>Carvalho, Alexandre</dc:creator>
  <dc:creator>Maciel, Adeline</dc:creator>
  <dc:creator>Coutinho, Alexandre</dc:creator>
  <dc:creator>Esquerdo, Julio</dc:creator>
  <dc:creator>Antunes, Joao</dc:creator>
  <dc:creator>Begotti, Rodrigo</dc:creator>
  <dc:creator>Arvor, Damien</dc:creator>
  <dc:creator>Almeida, Claudio</dc:creator>
  <dc:date>2018-11-01</dc:date>
  <dc:description>This paper presents innovative methods for using satellite image time series to produce land use and land cover classification over large areas in Brazil from 2001 to 2016. We used Moderate Resolution Imaging Spectroradiometer (MODIS) time series data to classify natural and human-transformed land areas in the state of Mato Grosso, Brazil’s agricultural frontier. Our hypothesis is that building high-dimensional spaces using all values of the time series, coupled with advanced statistical learning methods, is a robust and efficient approach for land cover classification of large data sets. We used the full depth of satellite image time series to create large dimensional spaces for statistical classification. The data consist of MODIS MOD13Q1 time series with 23 samples per year per pixel, and 4 bands (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), near-infrared (nir) and mid-infrared (mir)). By taking a series of labelled time series, we fed a 92-dimensional attribute space into a support vector machine model. Using a 5-fold cross validation, we obtained an overall accuracy of 94% for discriminating among nine land cover classes: forest, cerrado, pasture, soybean-fallow, fallow-cotton, soybean-cotton, soybean-corn, soybean-millet, and soybean-sunflower. Producer and user accuracies for all classes were close to or better than 90%. The results highlight important trends in agricultural intensification in Mato Grosso. Double crop systems are now the most common production system in the state, sparing land from agricultural production. Pasture expansion and intensification has been less studied than crop expansion, although it has a stronger impact on deforestation and greenhouse gas (GHG) emissions. Our results point to a significant increase in the stocking rate in Mato Grosso and to the possible abandonment of pasture areas opened in the state’s frontier. The detailed land cover maps contribute to an assessment of the interplay between production and protection in the Brazilian Amazon and Cerrado biomes.</dc:description>
  <dc:identifier>https://zenodo.org/record/3831203</dc:identifier>
  <dc:identifier>10.1016/j.isprsjprs.2018.08.007</dc:identifier>
  <dc:identifier>oai:zenodo.org:3831203</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>ISPRS Journal of Photogrammetry and Remote Sensing 145, Part B(November 2018) 328-339</dc:source>
  <dc:subject>Big earth observation data, Land use science, Satellite image time series, Crop expansion, Brazilian Amazonia biome, Brazilian Cerrado biome,Tropical deforestation</dc:subject>
  <dc:title>Big earth observation time series analysis for monitoring Brazilian agriculture</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
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