Analytical study on the relationship between land surface temperature and land use/land cover indices

Guha, Subhanil; Govil, Himanshu; Gill, Neetu; Dey, Anindita

doi:10.1080/19475683.2020.1754291

Cited by 48 publications

(20 citation statements)

References 47 publications

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“…With both SAVI and MNDWI bearing a stronger correlation with LST in both the cities, it can be suggested that the impact of UHI is strong in at least over 50% of the areas. Guha et al [81] reported that vegetation areas have a much better negative correlation with LST, but these relationships gradually weaken with the increase of the heterogeneous surface features. Although Panaji has a smaller urban area compared to Tumkur, the large water body, forest cover, and hilly features render Panaji city as a more heterogeneous surface than Tumkur city, which is located in the plains of India's interior.…”

Section: Discussionmentioning

confidence: 99%

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Ramaiah

Avtar

Rahman

2020

Land

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Elucidating the impact of Land Surface Temperature (LST) is an important aspect of urban studies. The impact of urbanization on LST has been widely studied to monitor the Urban Heat Island (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) Panaji and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat data were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur city, while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development.

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Section: Discussionmentioning

confidence: 99%

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Ramaiah

Avtar

Rahman

2020

Land

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“…Urban heat island and urban hot spots are a very common term in an urban environment and are indicated by the zone of very high LST inside the urban bodies (Guha et al 2017). Normalized difference water index (NDWI) is the most popular index for water surface extraction and it is invariably used in LULC and LST related studies (McFeeters 1996;Chen et al 2006;Essa et al 2012;Yuan et al 2017;Guha et al 2020b). Generally, the nature of LST-NDWI relationship in an urban area is insignificant which is controlled by several factors, such as humidity, vegetation, wetland, bare land, air pollution, rock surface, dry or wet soil, heterogeneous man-made materials, etc.…”

Section: Introductionmentioning

confidence: 99%

Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis

Guha¹

2021

International Journal of Engineering and Geosciences

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The present study examines the seasonal relationship between land surface temperature (LST) and normalized difference water index (NDWI) on various land surfaces in Raipur City of India by using a series of Landsat images for four specific seasons since 1991-92. The LST is retrieved using the mono-window algorithm technique. The results show that the LST of the study area is noticeably affected by surface composition. The best correlation (correlation coefficient r = 0.42) between the LST and NDWI is achieved in the post-monsoon season, followed by the monsoon season (r = 0.33), pre-monsoon season (r = 0.25), and winter season (r = 0.04). There is a moderate negative correlation (r =-0.49,-0.33,-0.31, and-0.25 in the premonsoon, monsoon, post-monsoon, and winter season, respectively) generated between the LST and NDWI on water bodies. On green vegetation, this LST-NDWI correlation is moderate positive (r = 0.67, 0.43, 0.50, and 0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). On human settlement and barren land surface, the correlation is weak positive (r = 0.24, 0.21, 0.27, and 0.15 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). The output of the research work can be used in the town planning section of any urban agglomeration.

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“…Spatial variation of LST is mainly due to the variation of the reflectance and roughness of land surface (Grimm et al 2008). Generally, green vegetation and water bodies present low LST, whereas built-up area, bare rock surface, or dry soil reflects high LST (Li et al 2017;Guha et al 2020b;Guha, Govil, and Diwan 2020). Thus, LST related studies are very important in urban and land use planning and development.…”

Section: Introductionmentioning

confidence: 99%

An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data

Guha

Govil

Besoya

2020

Geomatics, Natural Hazards and Risk

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The urban landscape is considered the most complex and heterogeneous landscape among the different land surface features. It rises the land surface temperature (LST) to a large extent compared to the surrounding rural body. This investigation deals with the seasonal variability between LST and normalized difference water index (NDWI) on the different land surfaces in Raipur, India by using sixty-four Landsat images from 1991-92 to 2018-19. The results show that the post-monsoon season indicates the best correlation (0.42) between LST and NDWI, followed by the monsoon (0.34), pre-monsoon (0.25) and winter (0.04). The water bodies reflect a moderate negative correlation of LST-NDWI in all the four seasons (À0.49 in pre-monsoon, À0.33 in monsoon, À0.31 in post-monsoon and À0.45 in winter). On green vegetation, this LST-NDWI correlation is strongly positive in pre-monsoon (0.67) season, moderate positive in monsoon (0.43) and post-monsoon (0.50) seasons, and weak negative in winter (0.25) season. The built-up area and bare lands build a weak positive correlation of LST-NDWI in all the four seasons (0.24 in pre-monsoon, 0.21 in monsoon, 0.27 in post-monsoon and 0.15 in winter). This study can be beneficial for land use planning and management of any city under a similar physical environment.

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Analytical study on the relationship between land surface temperature and land use/land cover indices

Cited by 48 publications

References 47 publications

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis

An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data

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