Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 3; p. 334
• Main Authors: de Oliveira Santos, Cecília Lira Melo, Lamparelli, Rubens Augusto Camargo, Dantas Araújo Figueiredo, Gleyce Kelly, Dupuy, Stéphane, Boury, Julie, Luciano, Ana Cláudia dos Santos, Torres, Ricardo da Silva, le Maire, Guerric
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2019; MDPI
• Subjects: Accuracy; Agricultural land; Agricultural management; Agricultural sciences; Agriculture; Algorithms; Approximation; Classification; Cloud cover; Computer Science; Crops; decision tree; Food; Image classification; Image Processing; Image quality; Land cover; Landsat 7; Landsat 8; Landsat satellites; Life Sciences; Management systems; Mapping; OBIA; Pasture; Radar data; random forest; Remote sensing; Satellite imagery; Seasons; segmentation; Sensors; Sentinel-1; Soybeans; Sugarcane; Time series; time-series analysis; Brazil; France; Segmentation; Landsat 8; Landsat 7; Sentinel-1; Land-cover; Time-series analysis; Random forest; Decision tree; OBIA
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11030334

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.