Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study

One of the most important signs of decreasing quality of life in urban environments is the reduction of thermal comfort. Heat discomfort has a negative impact on physical and mental performance of humans. Hence, it is of outmost importance to monitor thermal comfort patterns in cities and study its effect on people. The main objective of this study is to present a spatial multi-criteria decision analysis (MCDA)model for modeling thermal comfort for Tehran as a case study. For doing so, the reflectance and thermal information extracted from Landsat-8 satellite images, ASTER digital elevation model, MOD07 water vapor, and meteorological/climatic datasets were used. Several indicators including the downward shortwave radiation (SWD)and longwave radiation (LWD)to surface, upward longwave radiation (LWU)from the surface, brightness, greenness and wetness of the surface were derived. An Ordered Weighted Averaging (OWA)method was adapted considering different mental circumstances e.g., extremely pessimistic, pessimistic, neutral, optimistic and extremely optimistic. Our findings determine the geographical variation of thermal comfort across our study area e.g., the cold periods of the year are spread in the west and north-west side and the warm periods of the year on the west and north-west, while the central, northern, and eastern regions have a more favorable thermal comfort than other regions. The areal percentage of very suitable thermal comfort category for very pessimistic, pessimistic, neutral, optimistic, and very optimistic during the warm period of the year was 2.7, 5.1, 4.4, 13.4 and 1.18, respectively and in the cold period of the year was 9.1, 13.3, 18.3, 28.9 and 33.9, respectively. In both warm and cold periods with increasing degree of optimism, the area of favorable thermal comfort classes increases, while the area of unfavorable thermal comfort categories decreases. Our results and conclusions drawn from our proposed approach are useful for urban planners and public health researcher for monitoring quality of life in cities.