A case study on the relationship between land surface temperature and land surface indices in Raipur City, India
Bibliographic Data
| ID | 9849391 |
|---|---|
| Authors | Subhanil Guha (0000-0002-2967-7248, National Institute of Technology Raipur, corresponding author), Himanshu Govil (0000-0002-3433-8355, National Institute of Technology Raipur), Anindita Dey (0000-0002-6400-4284, Department of Geography, Nazrul Balika Vidyalaya, Guma, West Bengal, India), Neetu Gill (0000-0001-7392-2178, Chhattisgarh Council of Science and Technology, Raipur, Chhattisgarh, India) |
| Year | 2020 |
| Volume | 120 |
| Issue | 1 |
| Pages | 35-50 |
| Publication date | 2020-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Geografisk Tidsskrift-Danish Journal of Geography (JOURNAL) |
| Journal identifiers | ISSN: 0016-7223 • E-ISSN: 1903-2471 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00167223.2020.1752272 |
| OpenAlex | W3024525611 |
| Language | EN |
| Citations received | 7 |
| References cited | 71 |
Land surface temperature (LST) depends primarily on the land surface material and climatic conditions. The present study focuses on deriving the LST of Raipur City and generating the relationship between LST and some land surface indices, like NDVI, NDWI, NDBI, NMDI, and NDBaI for better land-use planning and environmental management inside the city. These land surface indices respond in different ways with the changes of LST in an urban landscape. There are only a few numbers of research works available on the relationship of LST and land surface indices in a tropical city for pre-monsoon season. The present study has been performed on a total of fifteen multi-date Landsat data sets of the pre-monsoon season from 2002, 2006, 2010, 2014, and 2018. The mono-window algorithm has been applied in retrieving the LST. Results show that LST builds a positive relation with NDBI, NDBaI, and NDWI and a negative relation with NDVI and NMDI. These relationships are stronger in the area below mean LST (low LST zones) and weaker in the area above mean LST (high LST zones). It indicates that the values of LST are largely influenced by the different land surfaces, like vegetation, water, soil, and built-up area
Climate change · Climatology · Geography · Hydrology (agriculture · Land use · Meteorology · Monsoon · Normalized Difference Vegetation Index · Physical geography · Vegetation (pathology · Ecology · Environmental Science · Geology · Land Use and Ecosystem Services · Urban Green Space and Health · Urban Heat Island Mitigation
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| Citations per year | 1.4 |
|---|---|
| Citation span | 2021 - 2025 (5) |
| Highly cited | No |