Modeling the impact of urban land cover features and changes on the land surface temperature (LST): The case of Jordan

Abstract

Higher land surface temperature (LST) in cities than their surrounding areas presents a major sustainability challenge for cities. Decision-makers and planners use the LST measurements to monitor the urban environment to reduce the urban climate’s main challenges. Therefore, there is an urgent need to examine the impacts of urban features and changes on the LST. This study focused on the relationship between LST and urban land changes and the impact of these changes on LST during different periods. Although a set of studies explored the relationship between landscape features and urban LST, several aspects still need further discussion. Here, the study aims to explore the influence of landscape features and land cover patterns on land surface temperature LST in two cities (Amman and Zarqa) in Jordan, and identify which of these features (vegetation cover, built-up and population density) has the most effective and influence on LST values. Therefore, this paper is the first study about land surface temperature LST and its relations with these aspects in Jordan. This study used a mixed method approach using quantitative method (GIS) and qualitative method (comparative case studies). This study revealed that the most important features affecting the LST values were: (1) Population density; (2) Built-up; and (3) Vegetation, and in descending order from the strongest to the least effective. It is also concluded that in the city in which the population density is high, the effect of built-up areas on the values of LST is as high as possible and positive more than the cities with medium and low population density. As for the city in which the population density is medium to low, the effect of vegetation cover on the values of LST is greater, and this can be more positive than in cities with a high population density. Therefore, the study contributes to improving the planners' and policymakers’ suitable future decisions for making sustainable cities.