| Autor: |
Subhanil Guha, Anindita Dey, Himanshu Govil, Neetu Gill, Prabhat Diwan |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Data Management, Analytics and Innovation ISBN: 9789813299481 |
| DOI: |
10.1007/978-981-32-9949-8_13 |
| Popis: |
The present study used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Impervious Surface Index (MNDISI) to determine the linear relationship between Land Surface Temperature (LST) distribution and these remote sensing indices under various spatial resolutions. Four multi-date Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images of parts of Chhattisgarh State of India were used from four different seasons (spring, summer, autumn and winter). The results indicate that LST established moderate to strong negative correlations with NDVI and weak negative to moderate positive correlations with MNDISI at various spatial resolutions (30–960 m). Generally, the coarser resolutions (840–960 m) possess stronger correlation coefficient values due to more homogeneity. The autumn or post-monsoon image represents the strongest correlation for LST–NDVI and LST–MNDISI at any resolution levels. The image of winter season reveals the best predictability of LST distribution with the known NDVI and MNDISI values. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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