| چکیده انگلیسی مقاله |
Extended abstract
Introduction
In geographical research, one of the key indicators for evaluating climatic changes and energy balance is Land Surface Temperature (LST), which plays a crucial role in analyzing thermal patterns and natural processes (Khosh Akhlagh et al., 2013; Ebrahimi et al., 2021). Remote sensing data due to their high accuracy, wide spatial coverage, and up-to-date availability have become a highly effective tool for generating thermal maps and offer a practical alternative to traditional measurement methods (Darvishi et al., 2019). The Bakhtegan–Maharloo basin, due to the severe decline in water resources, widespread degradation of natural ecosystems, and major land use changes in recent years, has become one of the critical environmental zones in Iran. Accordingly, a precise analysis of the spatial and temporal trends in land surface temperature and identification of the driving factors behind its rise in this region is of significant importance. This study uses Landsat satellite imagery and spatial statistical methods such as Hot Spot Analysis to examine the distribution patterns of land surface temperature in the Bakhtegan–Maharloo basin. The results identify high-temperature zones and clarify the regulatory roles of vegetation cover and surface water in moderating land surface temperature. An increase in LST can lead to higher evaporation rates from water bodies and further intensify the water scarcity crisis. Therefore, accurately identifying these changes is a vital step toward formulating effective strategies for vegetation restoration, sustainable water management, and mitigation of environmental degradation.
Methodology
Landsat 5 and Landsat 8 imagery from the years 1990, 1995, 2001, 2009, and 2020 were extracted via the Google Earth Engine platform. The datasets included Land Surface Temperature (LST), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI). Images with minimal cloud cover were selected, and preprocessing steps such as cloud masking were applied.Due to their advanced sensors, Landsat satellites are ideal for monitoring environmental change. In this study, the TM sensor on Landsat 5 (with 120 m spatial resolution for thermal bands and 30 m for reflective bands) and the TIRS sensor on Landsat 8 (with 100 m thermal and 30 m reflective resolution) were used. NDVI was used to assess vegetation density and health, while NDWI helped delineate surface water extent. These indices were key to identifying environmental variables affecting surface temperature and understanding their interrelation.To identify critical areas, hot spot analysis using the Getis-Ord Gi* statistic was conducted. This method calculates a Z-score to indicate where high or low values are spatially clustered, thus distinguishing hot and cold zones in the data.
Discussion
Thermal maps from 1990 to 2020 reveal significant shifts in temperature class distribution and a general increase in land surface temperature across the region. The findings indicate that this rise is primarily driven by the depletion of water resources, degradation of vegetation cover, and climate-related factors.The results highlight the urgency of implementing environmental management strategies such as rehabilitating water resources, increasing vegetation cover, and improving natural resource governance to prevent further warming. The presence of vegetation has significantly contributed to local temperature reduction, leading to a decrease in high-temperature zones and expansion of moderate-temperature areas. In contrast, barren and built-up lands especially in the southern and eastern parts of the basin remain major contributors to high surface temperatures due to their low NDVI values. Moreover, analysis of NDWI trends shows that, from 2001 onwards, declining water bodies have played a direct role in expanding high-temperature zones. The combined decrease in NDVI and NDWI confirms the central role of vegetation and water loss in driving temperature increases and spatial temperature clustering.
Conclusion
In the early years of the study (1990 and 1995), large portions of the basin experienced moderate temperatures between 15°C and 25°C, largely due to extensive water bodies and their cooling effects. However, from 2001 onward, as NDWI revealed significant water loss and NDVI showed vegetation degradation, the temperature distribution shifted dramatically. Vast areas, particularly in the southern and southeastern regions, transitioned into high-temperature classes ranging from 35°C to 58°C. Hot spot maps reveal that high-temperature clusters have expanded considerably in the southern and central regions, while cold clusters once concentrated in the northeast and near water bodies have gradually diminished. Since 2001, cold zones have nearly disappeared from southern areas and shifted northward.These changes clearly demonstrate the interconnected impacts of water scarcity and vegetation loss on rising surface temperatures and the spatial reorganization of heat intensity. By 2020, the majority of the basin had fallen into the highest temperature categories (45°C to 58°C), a condition directly associated with the decline in NDVI and NDWI. Ultimately, the findings confirm that the degradation of vegetation and reduction in water resources not only intensify surface temperature levels but also risk triggering cascading environmental consequences, including increased evaporation and deepening water shortages. |