Thermal Analysis of Climate Regions using Remote Sensing and Grid Computing

TL;DR

This paper presents a method for analyzing climate regions and heat island effects using satellite data and grid computing, enabling efficient processing without specialized resources.

Contribution

It introduces a web-based grid computing service for satellite image analysis, facilitating thermal environment studies with accessible tools.

Findings

Identified LST differences in Dobrogea, Romania.

Demonstrated the use of grid computing for large satellite data processing.

Applied supervised classification and indices like NDVI and LSE.

Abstract

The analysis of climate regions is very important for designers and architects, because the increase in density and built up spaces and reduction in open spaces and green lands induce the increase of heat, especially in an urban area, deteriorating the environment and causing health problems. This study analyzes the Land Surface Temperature (LST) differences in the region of Dobrogea, Romania, and compares with the land use and land cover types using TM and ETM+ data of 1989 and 2000. As the analysis is performed on large data sets, we used Grid Computing to implement a service for using on Computational Grids with a Web-based client interface, which will be greatly useful and convenient for those who are studying the ground thermal environment and heat island effects by using Landsat TM/ETM+ bands, and have typical workstations, with no special computing and storing resources for computationally intensive satellite image processing and no license for a commercial image processing tool. Based on the satellite imagery, the paper also addresses a Supervised Classification algorithm and the computation of two indices of great value in water resources management, Normalized Difference Vegetation Index (NDVI), respectively Land Surface Emissivity (LSE).