Long-term dynamics of remote sensing indicators to monitor the dynamism of ecosystems in arid and semi-arid areas: contributions to sustainable resource management

Abstract

Climate change's impact on wetlands has heightened concerns regarding the growing incidence of drought. It is imperative to continuously monitor long-term changes in wetlands to detect variations. This study evaluates the enduring variability of remote sensing indicators within 25 watershed areas in Algeria, recognized for their substantial biodiversity. We employed two robust statistical techniques (linear regression and the Mann-Kendall test) with data from diverse sources, including MODIS satellite data. From a time-series dataset spanning 22 years, we assembled several pivotal indicators: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), and land surface temperature (LST), selected to evaluate vegetation and water stress within the study areas. Our analysis revealed that NDVI showed more conspicuous temporal response when compared with EVI and NDWI. We uncovered negative correlations between NDVI and LST, underscoring the influence of drought and plant stress on vegetation within the study areas (R² = 0.109 to R² = 0.5701). Our Mann-Kendall trend analysis underscored the significance of NDVI and its robust association with EVI and NDWI. Understanding the dynamics of vegetation and water stress is of paramount importance for projecting changes in ecosystems, particularly within water bodies subject to climate variability. HIGHLIGHTS Study reveals increasing drought events in water bodies due to climate change.; Assessing remote sensing indicators in 25 biodiverse watersheds aids biodiversity conservation efforts.; The research underscores the crucial role of remote sensing technology in identifying fluctuations and aiding biodiversity conservation.; Understanding vegetation and water stress dynamics is vital for accurate ecosystem forecasts.;

Keywords

• algeria
• indicators
• remote sensing
• vegetation
• watersheds