Investigating the relationship between agronomic change and surface temperature using remote sensing data (Case study: Zabol)

Document Type : Research Paper


1 water & soil faculty, university of Zabol -zabol-iran

2 MA of urban planning, shiraz University, Shiraz. Iran

3 MA of Remotsensing, shahid Rajaee Teacher Training University, Tehran, Iran


Urban unsustainable development and growth of the industry in recent decades, and as a result, the physical expansion of cities, is a pivotal factor in environmental degradation and exerts a heavy pressure on the environment and is changed land cover vegetation and urban land use Managers and authorities of the country's energy resources are currently facing with a serious challenge in identifying the causes of the formation of thermal islands and the way to reduce this destructive phenomenon, and for this purpose, it is necessary to collect accurate and reliable spatial information about the location of the focal points of thermal anomalies. Land surface temperature (LST) is one of the key parameters for controlling and evaluating the physical, chemical and biological processes of the earth's surface and an important factor for studying the climate in urban areas. However, the temperature data recorded by the synoptic stations are not suitable in terms of spatial resolution. On the other hand, access to information in the shortest time and at the lowest cost is one of the important factors influencing decision making. In recent years, the use of modern, low cost and fast methods, such as remote sensing (RS) technology, has always been of interest to experts, managers and decision makers due to the ability to identify phenomena. In this study, LST values have been calculated using LANDSAT images with a single- band algorithm based on normalized difference vegetation index (NDVI). In ZABOL city case study, LST values have been calculated between 1986 - 2016 Period of ten years. Land use maps are also computed using the precise neural network method, and the relationship between surface temperature changes and land use as well as vegetation changes are calculated. The results showed that temperature variations correlate inversely with vegetation changes and the highest temperature corresponds to the built areas and waste land zones.


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