Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

Abstract

Abstract. Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature-emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R 2 ) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 • C and R 2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 • C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands in Yazd caused a rise in surface temperature during the 11-year period.

Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality

“…This study builds upon previous work that were conducted in different areas in the world such as Indianapolis in the United States (Weng, Lu, and Schubring 2004), Shanghai in China (Yue et al 2007;Wang et al 2017), Santiago in Chile (Pena 2008), Ho Chi Minh in Vietnam (Van and Bao 2010), Hong Kong (Liu and Zhang 2011), and Yazd in Iran (Zareie et al 2016). It extends previous work and focus on evaluating the usability of Landsat Thematic Mapper (TM) and Operational Land Imager/Thermal Infrared Scanner (OLI/TIRS) sensors for characterizing the spatial and temporal characteristics of vegetation abundance and surface temperature throughout different seasons in different years in Greater Amman Municipality in the Hashemite Kingdom of Jordan.…”

Perabiakan Algoritma Suhu Permukaan Laut Landsat 8 Untuk Perairan Ponelo

“…Pengujian kualitas algoritma dilakukan untuk mendapatkan algoritma yang baik dalam merepresentasikan SPL menggunakan R-squared (R 2 ) dan Root Mean Square Error (RMSE) (Zareie et al 2016;Syariz et al 2015). Xesti adalah estimasi nilai SPL berdasarkan algoritma dan Xmeas merupaka SPL data insitu.…”

Analysis of Land Surface Temperature due to Dynamics of Green Spaces and Water Bodies Using Geospatial Techniques in Gida Kiremu, Limu and Amuru Districts, Western Ethiopia

“…Relationships of land surface temperatures with Normalized Difference Index, Normalized Barren Index and Modified Normalized Difference Water Index were examined (Qinqin, 2012). Analysis of spatial changeability of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) the nexus between the factors is essential in natural and environmental studies (Zareie et al, 2016) as well as for spatial decision making and for monitoring of the natural resources. Furthermore, the investigation of Land surface temperature with NDVI, NDBaI and MNDWI of different times can be used to identify land use/land cover changes, which were made because of the forest fires, deforestation, built up expansion, expansion of agricultural land and grazing land restoration (Zhou and Wang, 2011;Mimbrero et al, 2014).…”