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.

本文介绍了2001年至2016年使用卫星影像时间序列进行巴西大面积土地利用和土地覆被分类的创新方法。我们使用中分辨率成像光谱仪（MODIS）时间序列数据对自然和人为转化的土地区域进行分类位于巴西的农业前沿马托格罗索州。我们的假设是，使用时间序列的所有值来构建高维空间，再加上高级的统计学习方法，是对大型数据集进行土地覆盖分类的一种强大而有效的方法。我们使用了卫星图像时间序列的全部深度来创建用于统计分类的大尺寸空间。数据包含MODIS MOD13Q1时间序列，每个像素每年有23个样本，以及4个波段（归一化植被指数（NDVI），增强植被指数（EVI），近红外（nir）和中红外（mir）。通过采用一系列标记的时间序列，我们将92维属性空间输入到支持向量机模型中。使用5倍交叉验证，我们在区分以下9种土地覆被类别时获得了94％的总体准确度：森林，塞拉多，牧场，黄豆，休耕棉，黄豆棉，黄豆玉米，黄豆粟，和大豆向日葵。所有类别的生产者和用户准确性均接近或优于90％。结果突出显示了马托格罗索州农业集约化的重要趋势。现在，双季作物制是该州最常见的生产系统，使土地免于农业生产。牧场扩张和集约化的研究少于作物扩张，尽管它对森林砍伐和温室气体（GHG）排放有更大的影响。我们的结果表明，马托格罗索州的放养率显着提高，并且可能放弃该州边境开放的牧场。详细的土地覆盖图有助于评估巴西亚马逊和塞拉多生物群落生产与保护之间的相互作用。