Urban heat island explored by co-relationship between land surface temperature vs multiple vegetation indices

• Published in: Spatial information research (Online) Vol. 24; no. 5; pp. 515 - 529
• Main Authors: Kayet, Narayan, Pathak, Khanindra, Chakrabarty, Abhisek, Sahoo, Satiprasad
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.10.2016; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Statistical correlation; Thermal remote sensing; Land surface temperature; Land use and land cover; Vegetation indices; Hot spots
• ISSN: 2366-3286; 2366-3294
• DOI: 10.1007/s41324-016-0049-3

Land surface temperature (LST), land use/land cover (LU/LC) and vegetation parameters are a substantial factor in worldwide climate change studies framework. This study of investigating urban heat islands based on thermal remote sensing data. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. In the present study have relationships among the multiple vegetation indices, land use/land cover and LST using remote sensing techniques in the Saranda forest state of Jharkhand. Normalized difference vegetation index (NDVI), Soil-adjusted vegetation index (SAVI), Ratio vegetation index (RVI) and Normalized difference built-up index (NDBI) are used in this study. The study work has been done on the correlation of the association among the different vegetation indices, land use/land cover, and land surface temperature. The result shows that the external temperature an impact on surfaces of self-heating (hot spots) areas. The relationship between LST and NDVI result shows the negative correlation. The NDVI proposes that the green land can deteriorate the effect on mining, urban heat island while we apparent the positive relationship between LST and NDBI. This study demonstrates that the growth of the active mining, the industrial area significantly decreases the vegetation areas, hence grow the surface temperature. This study also shows that the external temperature has an impact on surfaces of self-heating (hot spots) areas. Finally, the accuracy of proposed multiple indexes is evaluated by using DGPS field survey points over the study area. This analysis demonstrates the potential applicability of the methodology for climate modeling framework.