Monitoring and predicting spatial-temporal changes heat island in Babol city due to urban sprawl and land use changes

Abstract

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 changes. The aim of this study is a spatial-temporal evaluation of heat island intensity in the period of 1985-2015 and prediction of heat island intensity variations for the specific 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, Single channel algorithm were used, and Maximum likelihood algorithm was also utilized to classify Images. Therefore, land use changes and land surface temperatures (LST) were examined, and thereby the relationship between land-use changes was analyzed with the normalized land surface temperature. By using the mean and standard deviation of normalized thermal images, the area was divided into five thermal categories. Then, by applying the heat island intensity index, the heat island changes in the studied period of time was investigated. Land use changes for the future studies was investigated by using Markov model and then, the heat island intensity changes were anticipated. 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 an inverse relation with the trend of FVC and follows the same trend as normalized surface temperature changes. High and very high-temperature categories whose area increases annually, are adjacent to the city core and exit ways of the town. The index ratio of heat island during this period has an increasing trend and the amount of index was altered from 0/5 in 1985 to 0/67 in 2015. Land use changes anticipation and the process of heat island intensity variations for the studied area show alarming results.

Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification

“…MLC and SVM are the most common methods of satellite image classification [53,54]. The MLC is one of the most powerful parametric statistical methods, while the SVM is one of the non-parametric methods that has been applied successfully to image classification in recent years [55][56][57][58][59][60][61]. The SVM classifier was employed using a radial basis function (RBF) kernel.…”

Analysis of urban form typology using urban heat island indicators: Case study of Ferdous neighborhood of Tabriz

“…The undesirable effects of this heating include environmental deterioration, increasing the amount of ozone on the Earth's surface and even increasing the mortality rate (Torkashvand, 2016), increasing the population, increasing pollution caused by industries and increasing the traffic of cars, increasing the temperature of some urban areas compared to other areas (Matkan et al, 2014), increasing energy consumption (Ahmadi Vanhari et al, 2016), increasing water consumption during the hottest times of the year as well as thermal discomfort (Hashemi Darebadami et al, 2019) and the presence of impermeable levels in the city (Alijani et al, 2017). Heat and pollution are caused by large vehicles, industries and factories, air conditioning devices (Karimi Firozjaei et al, 2017), and have adverse effects on surrounding lands, microclimates (rainfall, temperature and wind flow) and changing the pattern of local winds, strengthening the growth of clouds and fog, increasing the number of thunderstorms and affecting the amount of rainfall (Halabian and Soltani, 2020). In order to reduce these adverse effects, several strategies have been proposed which are collectively called urban heat island reduction strategies.…”