Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil, The Scientific World Journal, vol.2, issue.1, pp.1-9, 2014. ,
DOI : 10.1016/j.apgeog.2011.08.007
URL : https://hal.archives-ouvertes.fr/hal-01102177
Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon, Proceedings of the National Academy of Sciences, pp.14637-14641, 2006. ,
Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data, Remote Sensing of Environment, vol.130, pp.39-50, 2013. ,
Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil, International Journal of Remote Sensing, vol.52, issue.22, pp.7847-7871, 2011. ,
DOI : 10.1016/j.rse.2006.11.021
URL : https://hal.archives-ouvertes.fr/halshs-00623706
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification, Tech. Rep, vol.8, issue.6, 2016. ,
DOI : 10.1007/978-3-319-44412-3_2
URL : https://hal.archives-ouvertes.fr/hal-01252726
Decoupling of deforestation and soy production in the southern Amazon during the late 2000s, Proceedings of the National Academy of Sciences, vol.109, issue.4, pp.1341-1346, 2012. ,
DOI : 10.1073/pnas.1111374109
Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices, Applied Geography, vol.32, issue.2, pp.702-713, 2012. ,
DOI : 10.1016/j.apgeog.2011.08.007
URL : https://hal.archives-ouvertes.fr/cirad-00820484
Spatial patterns of rainfall regimes related to levels of double cropping agriculture systems in Mato Grosso (Brazil), International Journal of Climatology, vol.11, issue.4, pp.2622-2633, 2014. ,
DOI : 10.1002/joc.3863
URL : https://hal.archives-ouvertes.fr/halshs-00910825
Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, vol.83, issue.1-2, pp.195-213, 2002. ,
DOI : 10.1016/S0034-4257(02)00096-2
Object Recognition from Local Scale- Invariant Features, International Conference on Computer Vision, pp.1150-1157, 1999. ,
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification, ECML- PKDD Workshop on Advanced Analytics and Learning on Temporal Data, 2015. ,
DOI : 10.1007/978-3-319-44412-3_2
URL : https://hal.archives-ouvertes.fr/hal-01252726
kernlab ? an S4 package for kernel methods in R, Journal of Statistical Software, vol.11, issue.9, pp.1-20, 2004. ,