A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices

Abstract

This study analyzes the long-term monthly variation of land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference bareness index (NDBaI) in the Raipur City of India using one hundred and twenty-three Landsat images from 1988-2020. In terms of LST, the warmest month is April (38.49 o C) and the coldest month is January (23.04 o C). The standard deviation in LST is noticed as 1.1022 o C throughout the period. The growth pattern of LST is increasing in the earlier stage while it is steady and decreasing in the later stage. The mean linear regression coefficients for LST-NDVI is -0.42, LST-NDBI are 0.68, LST-NDWI is 0.27, and LST-NDBaI is 0.32. It indicates that the high ratio of green vegetation and water bodies resist the raise of LST, whereas the bare rock surface and built-up land accelerate the LST. The value of the spectral indices and LST varies with the change of month due to the physical change of the land surface materials. Hence, the study will be an effective one for the town and country planners for their future estimation of land conversion.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

“…Since our study area image is from the summer season, which may be the main reason for the negative correlation between LST and NDVI in different Land classes. The relationship of LST with NDVI was still negatively correlated with removing water body data, which is limited to the findings of other researchers [ 61 , 62 ].…”

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

“…Conversely, agricultural areas exhibit NDVI values ranging from 0.65 to 0.7, reflecting varied crop densities and stages, which are essential for precision agriculture practices that optimize water usage in geothermal fields [83,84]. Urban areas identified with higher NDBI values (>0.2) highlight increased impervious surfaces contributing to runoff and reduced groundwater recharge, impacting water management in geothermal settings [88,89]. Additionally, accurately detecting water bodies with NDWI values over 0.5 is crucial for maintaining water availability for geothermal plants [91,92].…”

“…The ability of NDBI to detect urban materials such as concrete and asphalt makes it an essential tool for monitoring urban sprawl and guiding strategic water resource management. By integrating NDBI data into urban development plans, planners can more effectively balance urban growth with environmental conservation, enhancing the sustainability of local and broader ecosystems and ensuring that geothermal operations are supported by adequate water management practices [88,89].…”

Nature-based Solutions for Urban Resilience and Human Health

“…However, these models are inappropriate when variables are cross-correlated, and their use can lead to misleading results (Belgiu and Dragut, 2016;He et al, 2022). For example, Guha and Govil (2021) and Ullah et al (2023) found that LST decreases with an increasing normalized difference vegetation index in green areas. Jaganmohan et al (2016) discovered that small, intricately shaped green spaces have a negative impact on cooling, whereas green space areas larger than 5.6 hectares have a positive effect.…”