Long-term analysis of the urban heat island effect using multisource Landsat images considering inter-class differences in land surface temperature products DOI Creative Commons
Xiong Xu, Haoyang Pei, Chao Wang

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 858, P. 159777 - 159777

Published: Oct. 26, 2022

It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of urban heat island (UHI) effect on cities for applications, such as expansion environmental protection. Owing high resolution availability long time-series data, remote sensing images from Landsat satellites are widely used land surface temperature (LST) retrieval. However, limited by satellite revisit cycle image quality, use multisource in a study UHI inevitable. Nonetheless, owing differences among sensors, Landsat-7 Landsat-8, there may be apparent deviations LST results retrieved different sensor which obtained same area under similar circumstances. Consequently, it necessary build relationship between generated sensors future research effect. In this study, Shenzhen city was studied explore fitting corresponding products Landsat-8 adjacent dates with climatic conditions. Furthermore, factors affecting models, cover types, seasonal inter-annual differences, were analyzed. The constructed model had strong types but relatively weak differences; indicates that pseudo Landsat-8-based product can Landsat-7-based using fitted Landsat-7/8 pair years (or seasons). Finally, considering consistency images, spatiotemporal variations accurately explored data.

Language: Английский

A review of recent developments in the impact of environmental measures on urban heat island DOI

Prashanthini Rajagopal,

Radhakrishnan Shanthi Priya, Ramalingam Senthil

et al.

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 88, P. 104279 - 104279

Published: Oct. 27, 2022

Language: Английский

Citations

88
Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools DOI Open Access
Md. Naimur Rahman, Md. Rakib Hasan Rony, Farhana Akter Jannat

et al.

Climate, Journal Year: 2022, Volume and Issue: 10(1), P. 3 - 3

Published: Jan. 4, 2022

Urbanization is closely associated with land use cover (LULC) changes that correspond to surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base commonly lack the resources manage fast urbanization effects, so any rise in influences both directly indirectly. However, little known about impact rapid on UHI intensity variations during winter dry period major Bangladesh. To this end, we aim quantify spatiotemporal associations between 2000 2019 using remote-sensing geo-spatial tools. Landsat-8 Landsat-5 imageries these from 2020 were used for purpose, overall precision varying 81% 93%. The results LULC classification LST estimation showed existence multiple UHIs all districts, which upward trends, except Rajshahi Rangpur districts. A substantial increase expansion was observed Barisal > 32%, Mymensingh 18%, Dhaka 17%, Chattogram 14%, 13%, while significant decrease built-up areas noticed Sylhet < −1.45% −3.72%. We found greater than small High intensities 10 °C, 9 8 °C compared other due dense unplanned urbanization. identified higher (hotspots) zones be increased bare land. suburbanized strategy should prioritize restraint high UHIs. heterogeneous over seven found, might potential implications regional climate change. Our study findings will enable policymakers reduce change effect concerned

Language: Английский

Citations

73
Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India DOI Open Access
Arijit Das, Sasanka Ghosh, Kalikinkar Das

et al.

Sustainable Cities and Society, Journal Year: 2020, Volume and Issue: 65, P. 102577 - 102577

Published: Oct. 30, 2020

Language: Английский

Citations

120
Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city DOI Open Access
Subhanil Guha, Himanshu Govil

SN Applied Sciences, Journal Year: 2020, Volume and Issue: 2(10)

Published: Sept. 9, 2020

Language: Английский

Citations

85
An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data DOI Creative Commons
Subhanil Guha, Himanshu Govil,

Monika Besoya

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2020, Volume and Issue: 11(1), P. 1319 - 1345

Published: Jan. 1, 2020

The urban landscape is considered the most complex and heterogeneous among different land surface features. It rises temperature (LST) to a large extent compared surrounding rural body. This investigation deals with seasonal variability between LST normalized difference water index (NDWI) on surfaces in Raipur, India by using sixty-four Landsat images from 1991–92 2018–19. results show that post-monsoon season indicates best correlation (0.42) NDWI, followed monsoon (0.34), pre-monsoon (0.25) winter (0.04). bodies reflect moderate negative of LST-NDWI all four seasons (−0.49 pre-monsoon, −0.33 monsoon, −0.31 −0.45 winter). On green vegetation, this strongly positive (0.67) season, (0.43) (0.50) seasons, weak season. built-up area bare lands build (0.24 0.21 0.27 0.15 study can be beneficial for use planning management any city under similar physical environment.

Language: Английский

Citations

80
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data DOI Creative Commons
Ehsan Kamali Maskooni, Seyed Amir Naghibi, Hossein Hashemi

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(17), P. 2742 - 2742

Published: Aug. 24, 2020

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome population growth and industrialization. Bearing mind importance GW potential assessment reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors input advanced machine learning algorithms (MLAs), comprising deep boosting logistic model trees evaluate their efficiency. To do so, results are compared with three benchmark MLAs such boosted regression trees, k-nearest neighbors, random forest. For purpose, we firstly assembled different topographical, hydrological, RS-based, lithological altitude, slope degree, aspect, length, plan curvature, profile relative position, distance rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), lineament, lineament lithology. The spring indicator was divided into two classes training (434 springs) validation (186 a proportion 70:30. dataset springs accompanied by were incorporated outputs validated indices accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, sensitivity. Based upon area under ROC tree (87.813%) generated similar performance (87.807%), followed (87.397%), forest (86.466%), neighbors (76.708%) MLAs. findings confirm great modelling potential. Thus, application can be suggested other areas obtain insight about GW-related barriers toward sustainability. Further, based on algorithm depicts high impact RS-based factor, NDVI 100 influence, well influence river, RSP variables 46.07, 43.47, 37.20 respectively,

Language: Английский

Citations

78
A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data DOI
Subhanil Guha, Himanshu Govil, Neetu Gill

et al.

Quaternary International, Journal Year: 2020, Volume and Issue: 575-576, P. 249 - 258

Published: July 15, 2020

Language: Английский

Citations

77
The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta DOI
Rui Xiao, Wei Cao, Yue Liu

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 821, P. 153381 - 153381

Published: Jan. 24, 2022

Language: Английский

Citations

69
Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020) DOI Creative Commons
Ahmed Derdouri, Ruci Wang, Yuji Murayama

et al.

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(18), P. 3654 - 3654

Published: Sept. 13, 2021

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It projected that UHI intensity will rise record levels in the following decades due rapid expansion, as two-thirds of world population expected live areas by 2050. Nevertheless, last two have seen considerable increase number studies on surface (SUHI)—a form quantified based land temperature (LST) derived from satellite imagery—and its relationship use/cover (LULC) changes. This surge has been facilitated availability freely accessible five-decade archived remotely sensed data, use state-of-art analysis methods, advancements computing capabilities. The authors this systematic review aimed summarize, compare, critically analyze multiple case studies—carried out 2001 2020—in terms various aspects: study area characteristics, data sources, methods for LULC classification SUHI quantification, mechanisms interaction coupled linking techniques between spatial temporal changes, proposed alleviation actions. could support decision-makers pave way scholars conduct future research, especially vulnerable cities not well studied.

Language: Английский

Citations

68
Spatiotemporal mapping of Land Use/Land Cover dynamics using Remote Sensing and GIS approach: a case study of Prayagraj City, India (1988–2018) DOI
Md. Omar Sarif, R. D. Gupta

Environment Development and Sustainability, Journal Year: 2021, Volume and Issue: 24(1), P. 888 - 920

Published: May 9, 2021

Language: Английский

Citations

57