Abstract
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
Keywords
Climate variability
Hamrin Lake.
NDVI
Trend analysis
Water quality
Abstract
The assessment of the vegetation changing patterns of the Hamrin Lake area over the period of 2013 to 2023. The analysis is based on the effects of spatially and temporally dynamic changes in surface water quality and climatic factors on the quality and function of the region's water basins over time. NDVI (Normalized Difference Vegetation Index) data provide the basis for seasonal and temporally dynamic analyses of vegetation growth patterns, while Landsat satellite images provide the basis for predicting vegetation growth levels via Supervised Classification. Additionally, the identification of the most dominant variables influencing surface water quality was carried out through Statistical/Redundant Tests. Our results indicate that many of the major variables contributing to surface water quality (in this case, pollutant concentration) were measured near the water quality monitoring stations. Nitrate concentration was found to be at its lowest in the summer months, while total phosphorus concentration was highest under spring weather conditions. NDVI values showed a gradual decline from spring through the summer months, whereas land use values were slightly increasing over the same season. Results from our analysis of Landscape Metrics revealed that connectivity of landscapes was the most influential variable affecting water quality in a seasonal manner, in contrast, water elevation was significant in affecting the majority of stream water quality.
Keywords
تغيرات المناخ، تحليل المسار الزمني، جودة المياه، مؤشر الغطاء النباتي الطبيعي (NDVI)، بحيرة حمرين.