Arash Karimi Zarchi; Reza Shahhoseini
Abstract
Extended Abstract Introduction Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat ...
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Extended Abstract Introduction Heat island phenomenon occurs when the land surface temperature and the air temperature in urban areas are higher than that of the surrounding areas. This temperature difference is shown as the urban heat islands on thermal maps. Information obtained from the urban heat islands can be a useful source in urban planning applications. The availability of reliable information about the urban heat islands plays an important role in predicting and preventing the occurrence of many heating risks in urban areas. One of the common methods of calculating heat islands intensity in urban areas is the use of two temperature sensors installed in the city and around it. Given the limited temperaturemeasuring stations, there is no accurate estimate of the urban heat islands. With the introduction of Remote Sensing technology into the space arena, and with the help of satellite images processing, a precise map can be produced for the land surface temperature, i.e. a precise estimation of the urban heat islands is obtained by calculating the pixels temperature difference at the urban areas and around them. Therefore, one of the important issues in such studies is to detect the urban and non-urban pixels and to separate them from each other. Materials&Methods The most important reason for the occurrence of the heat island phenomenon is the change in land use from rural to urban, which is well exhibited in the urban cover index maps.In this paper, in order to measurethe intensity of surface urban heat islands, a method based on generating the urban percentage map was proposed by combining the Land Surface Temperature (LST) map, the Normalized Difference Built-up Index (NDBI) map and the Normalized Difference Vegetation Index (NDVI) map.Considering the relationship between the land surface temperature and the land cover type, it can be said that the relationship between the land surface temperature and the urban percentage map follows a linear function which can be fitted to the land surface temperature graph in terms of land cover type. Finally, the Urban Heat Island Intensity (UHII) map was calculatedfrom the slope of the fitted line.In order to evaluate the strengths and weaknesses of the proposed method, a classification-based method was used to separate the urban and non-urban pixels and to calculate the urban heat island intensity. The proposed method was implemented on the Landsat-7 ETM + satellite data in the city of Rasht and on the Landsat-8 OLI / TIR satellite data in the city of Langroud. Results&Discussion The results of the classification-based method indicated a large difference between the maximum and the minimum temperature of the urban areas, which led to a high-temperature changein all land cover typesin the study area. Therefore, the use of the average temperature of each class to calculate the heat island intensity is not a suitable method and the accuracy of the heat islands maps is not high and they cannot be used in applications that require high precision.Although, this problem can be solved by increasing the number of classes, increasing the number of classes requires more training data and a sensor with higher spatial resolution. By contrast, the results indicated that the proposed method (based on the urban percentage map) had a high accuracy for calculating the urban heat island intensity which was similar for both study areas. Also, fitting a linear function to the values of land surface temperature and the urban percentage map led to decreasing the effect of suspicious pixels (noisy pixels) on the overall accuracy of the estimation of the urban heat island intensity. Meanwhile, the results obtained on two datasets indicated that this method did not require any training data or any other background information about the study area and it can be applied for many satellite images having thermal band with any spatial resolution. However, because of the ineffectiveness of urban cover indicators in desert areas, the heat islands intensity in these regions was underestimated. Conclusion In applications that do not require high accuracy in calculating the urban heat island intensity, and there are high spatial resolution satellite imagery and sufficient training data in a region, the use of a classification-based approach seems to be suitable. Since the collection of such data and information is costly, a new method based on the urban percentage map was proposed in this paper by fitting a line to the LST parameter diagram in terms of the NDBI index for measuring the heat island intensity. The results indicated the higher efficiency and accuracy of the proposed method compared to the conventional classification-based methods for calculating the urban heat island intensity.
