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Remote Sens. 2019, 11(3), 334; https://doi.org/10.3390/rs11030334

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

1
School of Agricultural Engineering, FEAGRI, University of Campinas, UNICAMP, Campinas 13083-875, Sao Paulo, Brazil
2
Interdisciplinary Center on Energy Planning, NIPE, University of Campinas, UNICAMP, Campinas 13083-896, Sao Paulo, Brazil
3
CIRAD, UMR TETIS, F-34398 Montpellier, France
4
Paris Institute of Technology for Life, Food and Environmental Sciences, AgroParisTech, 75231 Paris, France
5
Brazilian Bioethanol Science and Technology Laboratory, CTBE, Brazilian Center for Research in Energy and Materials, CNPEM, Campinas 13083-970, Sao Paulo, Brazil
6
Institute of Computing, University of Campinas, UNICAMP, Campinas 13083-852, Sao Paulo, Brazil
7
CIRAD, UMR Eco&Sols, Campinas 13083-896, Brazil
8
Eco&Sols, University of Montpellier, CIRAD, INRA, IRD, Montpellier SupAgro, 34000 Montpellier, France
*
Author to whom correspondence should be addressed.
Received: 19 January 2019 / Revised: 30 January 2019 / Accepted: 3 February 2019 / Published: 8 February 2019
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
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Abstract

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. View Full-Text
Keywords: land-cover; time-series analysis; random forest; OBIA; segmentation; decision tree; Landsat 7; Landsat 8; Sentinel-1 land-cover; time-series analysis; random forest; OBIA; segmentation; decision tree; Landsat 7; Landsat 8; Sentinel-1
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lira Melo de Oliveira Santos, C.; Augusto Camargo Lamparelli, R.; Kelly Dantas Araújo Figueiredo, G.; Dupuy, S.; Boury, J.; Luciano, A.C.S.; Torres, R.S.; le Maire, G. Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region. Remote Sens. 2019, 11, 334.

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