Abstract. The department of Sidi Bel Abbes is considered among the primary region producing cereal at the national level in Algeria. The study that we conducted is based on time series analysis using Sentinel2 images to monitor the cultivated cereal crops, combined with the field data including cereal plots for the growing season of 2020–2021.The methodology adopted in this study focused mainly on the processing of a Normalized Difference Vegetation Index (NDVI) time series. The analysis of the NDVI time series from October to July 2021, allowed us to determine the typical profiles of cereal crops, based on thresholding obtained to extract the areas cultivated by cereal crops. The geographical distribution of cereal in terms of area represents a high density of green biomass in the highlands and a low density of vegetation towards the plain and the highlands of the northwest area. The analysis of the profile’s growth for the cereal crops, allowed us to understand the behavior of the cereal crop during its development and to catch the relationship of these behaviors, the meteorological conditions and the agricultural practices followed.
<p><strong>Abstract.</strong> Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.</p>
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