Main Article Content
A Split-Window algorithm has been used in the Ilam Dam watershed to determine the relationship between Land Surface Temperature (LST) and types of land use. Landsat satellite images of TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geometric and radiometric corrections of satellite images, land use maps are extracted by using Fuzzy ARTMAP method. An accuracy assessment showed that the highest value of the Kappa coefficient was 94% with a total accuracy of 0.95 for 2015, and that the lowest Kappa coefficient value was 87% with a total accuracy of 0.9 for 1990. The high values of these coefficients indicate the acceptable accuracy of using Landsat's remote sensing data for land use detection. The most important land use change is related to dense forest and sparse forest land uses, with a decrease of 20.07 and 17.04 percent, respectively. The minimum LST measures in 1990, 2010, and 2018 in dense forest are 21.27, 30.55 and 33.82 °C respectively. The maximum LST for the sparse forest land use in 1990 and 2010 are 52.48, 56.09, and for the dense forest land use in 2018 is 56.10 °C. As a result, the average LST in agricultural lands was lower than in sparse forest and rangeland; this is mainly due to the high moisture content and the greater evapotranspiration rate. Land Use / Land Cover (LULC) variations from 1990 to 2018 show that all land uses have experienced an increase in LST.
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