This study explores the use of machine learning for crop type detection and vegetation monitoring in arid and semi-arid regions, specifically in Biskra and Khenchela, Algeria. Traditional field-based agricultural monitoring is labor-intensive and limited in spatial coverage, prompting the need for more efficient and scalable methods. The research integrates Synthetic Aperture Radar (SAR) data from Sentinel-1 and optical imagery from Landsat 8 to enhance the classification of different crop types and track vegetation dynamics. Key metrics such as radar backscatter and Normalized Difference Vegetation Index (NDVI) are employed to distinguish between irrigated and rainfed crops. The Random Forest algorithm is utilized for classification, with training data derived from field surveys, high-resolution satellite imagery, and existing land cover maps. The Google Earth Engine (GEE) platform facilitates large-scale data processing, providing real-time agricultural analysis. Over the 12-month study period (January to December 2020), the integrated approach demonstrated high accuracy, achieving over 85% precision in classifying key crops such as date palms, cereals, and vegetables. Results indicate that SAR and NDVI data provide complementary insights into vegetation health, phenology, and agricultural practices in different climatic zones. This methodology offers a promising solution for improving agricultural monitoring and resource management in arid and semi-arid regions, enhancing food security and optimizing water usage. The study’s findings also underscore the value of satellite-based remote sensing for large-scale, real-time agricultural analysis.

This study investigates vegetation ecology and rangeland degradation in the arid and desert regions of northeastern Algeria from 2015 to 2023, with predictions extending to 2031. Utilizing remote sensing techniques, specifically Landsat 8 and Landsat 9 imagery, and the Modified Soil Adjusted Vegetation Index (MSAVI), we mapped and analyzed changes in vegetation cover. The QGIS MOLUSCE plugin facilitated spatiotemporal analysis, revealing significant natural and anthropogenic impacts on a study area. Less degraded rangelands have also seen an increase, from 44,110,116 hectares in 2015 to 48,481,851 hectares in 2023, due to drought, overgrazing, and socioeconomic pressures. With a slight decrease to 48,450,495 hectares projected for 2031. This suggests efforts in land rehabilitation and sustainable rangeland management are yielding positive results, though there remains room for improvement. The study recorded that Agricultural croplands have experienced a slight decrease from 1,404,153 hectares in 2015 to 1,350,693 hectares in 2023. However, a modest increase to 1,587,242 hectares is projected by 2031. The findings indicate that Oases date palm areas have increased from 1,287,342 hectares in 2015 to 1,630,197 hectares in 2023, with further growth to 1,816,296 hectares anticipated by 2031. Our research highlights the critical role of remote sensing in monitoring vegetation dynamics within arid and desert ecosystems. The methodology provides a robust framework for similar studies, emphasizing the necessity for ongoing monitoring using advanced télédétection technologies. To mitigate further degradation and preserve these fragile ecosystems, the implementation of sustainable land management practices is essential.

Background: Accurate monitoring of crop types and vegetation health is vital for sustainable agriculture, particularly in arid and semi-arid regions where environmental constraints challenge productivity. In Algeria, the provinces of Biskra and Khenchela exhibit diverse agro-climatic conditions and cropping systems that require robust, scalable monitoring tools. This study addresses these challenges by proposing an integrated remote sensing approach to classify crop types and assess vegetation dynamics during the 2020 growing season. Methods: The methodology combines Sentinel-1 Synthetic Aperture Radar (SAR) and Landsat 8 optical imagery to differentiate between irrigated and rainfed crops.Key Indicators, Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. Temporal Strategy, monthly composites were generated to mitigate atmospheric interference. Platform,data preprocessing and analysis were conducted within the Google Earth Engine (GEE) environment. Classification Model, A Random Forest classifier was trained using ground truth data and high-resolution satellite imagery to integrate spectral and radar features for accurate crop classification. Result: NDVI effectively captured crop phenological patterns, with consistently higher values observed in irrigated date palm plantations in Biskra and seasonal variability in cereal and fruit orchards in Khenchela. Sentinel-1 SAR backscatter data enhanced classification accuracy, particularly in distinguishing vegetation structures. The integrated approach achieved over 85% classification accuracy, validating the synergy between SAR and optical datasets. The study demonstrates the operational potential of this methodology for large-scale, real-time agricultural monitoring and highlights the utility of GEE as a powerful platform for remote sensing in data-scarce environments. These findings contribute to improved agricultural resource management, food security and water-use efficiency.

This study aims to comprehensively analyze plant diversity in the Northeast Algerian Sahara (El Oued region), quantitatively and qualitatively, focusing on species richness and diversity. The study sampled spontaneous vegetation from seven sites, analyzing the number of individuals, families, density, and richness. The results showed a ratio of generic richness to species richness of 0.94 ± 0.05, with a ratio of family richness to species richness of 0.56 ± 0.13. The dominant family was Chenopodiaceae, with 22.73%. The flora was dominated by perennial plants, with Sahara-Arabian elements being more abundant. Sufficient management strategies are required to protect biodiversity in these ecosystems.

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