MONITORING SPATIOTEMPORAL CHANGES OF HEAT ISLAND IN BABOL CITY DUE TO LAND USE CHANGES

Urban heat island is one of the most vital environmental risks in urban areas. The advent of remote sensing technology provides better visibility due to the integrated view, low-cost, fast and effective way to study and monitor environmental and humanistic changes. The aim of this study is a spatiotemporal evaluation of land use changes and the heat island in the time period of 1985-2015 for the studied area in the city of Babol. For this purpose, multi-temporal Landsat images were used in this study. For calculating the land surface temperature (LST), single-channel and maximum likelihood algorithms were used, to classify Images. Therefore, land use changes and LST were examined, and thereby the relationship between land-use changes was analyzed with the normalized LST. By using the average and standard deviation of normalized thermal images, the area was divided into five temperature categories, inter alia, very low, low, medium, high and very high and then, the heat island changes in the studied time period were investigated. The results indicate that land use changes for built-up lands increased by 92%, and a noticeable decrease was observed for agricultural lands. The Built-up land changes trend has direct relation with the trend of normalized surface temperature changes. Low and very low-temperature categories which follow a decreasing trend, are related to lands far away from the city. Also, high and very high-temperature categories whose areas increase annually, are adjacent to the city center and exit ways of the town. The results emphasize on the importance of attention of urban planners and managers to the urban heat island as an environmental risk

The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented