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

Guha, Subhanil; Govil, Himanshu; Besoya, M.

doi:10.1080/19475705.2020.1789762

Cited by 63 publications

(23 citation statements)

References 71 publications

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“…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. (McFeeters 1996;Ghobadi et al 2014;Guha et al 2020c).…”

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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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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“…Later, accuracy evaluation was applied to obtain the NDWI 's transparent pixel value. In addition, validation of the kappa coefficient, overall precision, and accuracy was carried out (Guha et al, 2020;Khine et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…In Southern Bangladesh, thousands of speople have lost their ability to make a proper living just because of waterlogging, particularly poor and landless farmers, share-holders, daily wage laborers, small traders, and others (Shaibur et al, 2019). Various previous studies have shown that GIS and RS techniques are useful for detecting and mapping waterlogged areas for different study areas (Guha et al, 2020;Hassan & Mahmud-ul-islam, 2014;Khine et al, 2018;Mahmud et al, 2017;Sar et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variability of Waterlogging in Rangpur City Corporation Using GIS and Remote Sensing Techniques

Rahman

Shozib

2021

Geos. Ind.

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Waterlogging hazard is a significant environmental issue closely linked to land use for sustainable urbanization. NDWI is widely and effectively used in identifying and visualizing surface water distribution based on satellite imagery. Landsat 7 ETM+ and Landsat 8 OLI TIRS images of pre and post-monsoon (2002, 2019) have been used. The main objective of this study is to detect the seasonal variation of waterlogging in Rangpur City Corporation (RPCC) in 2002 and 2019. In the present study, we used an integrated procedure by using ArcGIS raster analysis. For pre and post-monsoon, almost 93% accuracy was obtained from image analysis. Results show that in 2002 during the pre and post-monsoon period, waterlogged areas were about 159.58 km2 and 32.32 km2, respectively, wherein in 2019, the changes in waterlogged areas are reversed than 2002. In 2019, during pre-monsoon, waterlogged area areas were 122.79 km2, and during post-monsoon, it increased to 127.05 km2. The research also depicts that the trend of the waterlogging situation largely depends on seasonal rainfall and a flawed drainage system. Keywords : Seasonal variation; Waterlogging; Remote sensing; GIS; Rangpur City Corporation Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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“…The inverse relationship of LST with the NDVI and the NDWI determined in these studies suggests that vegetation has a mitigative effect on the surface urban heat island. However, studies dealing with large spatial and/or temporal scales have reported contradictive results regarding the relationship between LST and land cover characteristics, depending on the year, season, time of the day or geographic location [20][21][22][23][24]. This indicates the complexity of this relationship and the importance of its continuous monitoring at a local level.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Spatiotemporal Change of Green Spaces in Relation to the Land Surface Temperature: A Case Study of Belgrade, Serbia

et al. 2021

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Understanding the relationship between land use and land cover and thermal environment has recently become an emerging issue for urban planners and policy makers. We chose Belgrade, as a case study, to present a cost- and time-effective framework for monitoring spatiotemporal changes of green spaces in relation to the land surface temperature (LST). Time series analysis was performed using Landsat 5 TM and Landsat 8 OLI/TIRS imagery from 1991 to 2019 with an approximate 5-year interval (18 images in total). Spectral vegetation indices and supervised land cover classifications were used to examine changes of green spaces. The results showed a fluctuating trend of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The highest values were recorded in 2019, indicating vegetation recovery in the last decade. A significant positive correlation was determined between the spectral vegetation indices and the amount of precipitation during growing season. The land cover classification showed that the share of vegetated and bare land decreased by 11.74% during the study period. The most intensive conversion of green and bare land into built-up land cover occurred in the first decade (1991–2000). To assess spatiotemporal changes in the LST, Landsat Collection 2 Surface Temperature products were used. We found a negative correlation between change in the spectral vegetation indices and change in the LST. This indicates that the reduction in vegetation was associated with an increase in the LST. The municipalities that were the most affected in each decade were also identified with our framework. The findings of this study are of great relevance for actions targeting an improvement in urban thermal comfort and climate resilience.

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

Cited by 63 publications

References 71 publications

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

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

Seasonal Variability of Waterlogging in Rangpur City Corporation Using GIS and Remote Sensing Techniques

Monitoring of Spatiotemporal Change of Green Spaces in Relation to the Land Surface Temperature: A Case Study of Belgrade, Serbia

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