Temporal kernels for the identification of grassland management using time series of high spatial resolution satellite images

Grasslands, and more precisely agricultural practices associated with grasslands have an important impact on water and soil quality and biodiversity. In many regions, associated with agriculture intensification, a decrease of grasslands and change in their management can be observed during the last half century. Thus, determination of grassland management types represents an important approach for the quality and preservation of the environment. In this context, the objective of this study is to identify agricultural practices on grasslands from a time series of high spatial resolution images. Based on training samples, the classification of the LAI temporal profiles extracted from satellite images was performed using 1-standard classification technique as KNN and 2- an advanced ones using temporal kernels based on Dynamic Time Warping. Results show that use of an advanced classification technique improves of 20% the quality of grassland management identification.