Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data

Geomatics, Natural Hazards and Risk

2021

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Section: Introductionmentioning

confidence: 99%

COVID-19 lockdown effect on land surface temperature and normalized difference vegetation index

Geomatics, Natural Hazards and Risk

2021

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“…Remote sensing techniques are significantly effective in detecting the land use/land cover (LULC) change and its consequences [10]. Several satellite sensors are capable to identify these change zones by using their visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands [11]. Apart from the conventional LULC classification algorithms, some spectral indices are used in detecting specific land features.…”

Section: Introductionmentioning

confidence: 99%

Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city

2020

SN Appl. Sci.

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Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991-2004 and 1.54 °C during 2004-2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the postmonsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city.

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“…Generally, LST builds an inverse relationship with vegetation [29]. NDVI acts as a determining factor of LST [30], and some studies used the LST-NDVI correlation to evaluate the distributional pattern of LST [31][32][33][34][35][36]. A lot of recent studies assess the LST-NDVI correlation in multidimensional approach [2,32,[37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Monitoring LST-NDVI Relationship Using Premonsoon Landsat Datasets

Advances in Meteorology

Diwan

2020

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