Assessment of Land Surface Temperature Trend and Its Spatial Correlation with Landscape Structural Elements in Rasht Watershed, Guilan Province

Abstract

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.

Keywords

• land surface temperature
• hot spots
• moran statistical test
• thermal remote sensing
• rasht watershed