A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Sresto, Mizbah Ahmed; Siddika, Sharmin; Fattah, Md. Abdul; Morshed, Syed Riad; Morshed, Md. Manjur

doi:10.1016/j.heliyon.2022.e10309

Cited by 19 publications

(7 citation statements)

References 56 publications

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“…Another water resource management model is presented in the paper [57] using the GIS database and graph theory by modifying the GIS graph data and equipment management system. Seasonal variations of land use land cover and land surface temperature(LST) and the association of LULC with LST are explored and presented in [58]. The major LULC indices used for analysis include NDVI, NDBI, NDBAI, and MNDWI.…”

Section: Remote Sensing and Gis Applications In Agriculturementioning

confidence: 99%

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

Sumangala

Kini²

2022

IJAEML

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Background/Purpose: The objective of this literature review is to explore different land use and land cover methods using machine learning techniques and also their applications in change detection. Reviewing various methods adopted in this domain opens up a new path for taking up further research by extending the current approaches. Design/Methodology/Approach: The research findings presented in various scholarly articles are collected from secondary resources including scholarly journal publications. These articles are analyzed, and the interpretations are highlighted in this review paper. Findings/Result: This research provides insight into various techniques used to classify remote sensing imagery. The gaps identified during the analysis with different approaches have helped to get a clear picture when formulating research questions in the remote sensing geographic information systems domain. Research limitations/implications: This study has surveyed various applications of remote sensing in GIS. This study is limited to a review of the various machine-learning approaches used for implementing change detection. The various deep learning architectures for image classification could be further explored. Originality/Value: The articles selected for review in this study are from scholarly research journals and are cited by other authors in their publications. The papers selected for review are relevant to the research work and research proposal presented in this paper. Paper Type: Literature review paper.

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Section: Remote Sensing and Gis Applications In Agriculturementioning

confidence: 99%

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

Sumangala

Kini²

2022

IJAEML

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“…The imperative for urbanization and the demand for increased human settlements frequently give rise to an inescapable consequence: the irreversible loss of functional and natural land cover. This phenomenon, in turn, leads to localized alterations in temperature, weather patterns, and precipitation, thus sparking intricate environmental dynamics with potential far-reaching implications (Morshed et al, 2022;Sresto et al, 2022a). When these changes are concentrated over large areas, they have the potential to affect the Earth's climate by altering local, regional, and global circulation patterns, altering the reflectance of the Earth's surface, and raising the atmospheric CO2 concentration (Fattah et al, 2021;Sresto et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon, in turn, leads to localized alterations in temperature, weather patterns, and precipitation, thus sparking intricate environmental dynamics with potential far-reaching implications (Morshed et al, 2022;Sresto et al, 2022a). When these changes are concentrated over large areas, they have the potential to affect the Earth's climate by altering local, regional, and global circulation patterns, altering the reflectance of the Earth's surface, and raising the atmospheric CO2 concentration (Fattah et al, 2021;Sresto et al, 2022a). As a result, growing urbanisation reduces environmentally friendly land cover types like forests, vegetation, scrubland, and water bodies, which has an impact on the ecosystem and, consequently, carbon sequestration (Fattah et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, growing urbanisation reduces environmentally friendly land cover types like forests, vegetation, scrubland, and water bodies, which has an impact on the ecosystem and, consequently, carbon sequestration (Fattah et al, 2021). This effect ultimately contributes to climate change and global warming, as well as a rise in LST (Morshed et al, 2022;Sresto et al, 2022b;Thakur et al, 2020). Considering the method of classification, the supervised classification method was used in the area of interest to determine the LULCC Classification and accuracy assessment of each classification algorithm (Verma et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Examining the spatio-temporal relationship between LST, NDVI, NDBI and LULC change of Pachhua dun, Dehradun, Uttarakhand (India)

Thapa,

Bahuguna,

Negi

et al. 2023

JGISE

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Recent climate change has had a negative impact on a wide range of human and natural systems, and it is clear that humans influence the climate. Because, as anthropogenic influence increases, the heat output from the land surface increases, speeding up the rate of climate change. In this regard, the use of RS and GIS techniques has provided various opportunities for research to examine these changes. The current analysis is based on the Landsat 1989, and 2020. Over the study period of 31 years, the built-up regions increased in size from 44.23 km2 to 154.56 km2. Whereas, the area covered by scrubland, water bodies, and vegetation cover has significantly decreased. The LST study further supports the outcome, showing that the mean and standard deviation increased from 14.81°C±1.32(1989) to 18.82°C±1.57(2020). The study also made an effort to examine how LULC affected LST; while vegetation cover has consistently helped to lower mean LST, built-up areas and scrubland are the main drivers of mean LST rise. The LST and NDBI revealed a positive correlation, while the NDVI/SLOPE and LST showed a negative correlation. Subsequently, the multiple linear regression (MLR) models concluded that the BUAs has evolved into a serious threat to the increase in LST, but increase in vegetation cover and SLOPE would result in slight decrease in LST. the study recommended that the government create policies that restrict future land encroachment and conversion, notably of forested area and water bodies, and make an immediate effort to increase the quantity and quality of urban green cover in the study area. So that we may, respectively, minimize the potential hazard posed by future LST rise and LULC change.

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“…During the previous decade, the global urbanization has appeared as the primary driver of environmental, social, and demographic change [6][7]. Because of urbanization, categories of land use and land cover like vegetation, waterbody, buildup, and cropland are rapidly changing [8,5]. Normalized Difference Vegetation Index which describes the status of vegetation using a combination of the red and the near-infrared band of satellite imagery, is the most extensively used catalog [9].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Heat Effect through Satellite Image Analysis: Focusing on Narayanganj Upazila, Bangladesh

Islam

Haque²

2022

J. Appl. Sci. Process Eng.

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The rapid growth of population and land use cover change are closely connected. Narayanganj Sadar Upazila is the first Growing City in Bangladesh. Land use and land cover change are very first. Global warming, increased greenhouse gas emissions, and other environmental issues have become critical issues to address in recent times. Because of the Unplanned Expansion of urban areas, the LULC pattern is changing, and this kind of adverse (LST increasing, Heat Island Growth) impact is increasing. In this area, the amount of vegetation is decreasing day by day. The objectives of this study are to identify land use land cover (LULC) dynamics for the year 2001 to 2021, identify Urban Heat Islands from the value of land surface temperature (LST) and identify hotspots based on normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and land surface temperature (LST) doing weighted overlay among them. To determine the land cover pattern, and change at Narayanganj Upazila in the years 2001, 2011, and 2021, the land cover type was divided into four categories (build-up, vegetation, water bodies, and barren soil) in geographic information system (GIS) and Earth Resources Data Analysis System (ERDAS) Imagine 2014. The overall accuracy of LULC in 2001, 2011, and 2021 was 90.08, 91.34, and 92.02, respectively. And the value of the kappa coefficient for the years 2001, 2011, and 2021 was 0.91, 0.89, and 0.90, respectively. This study demonstrates an increase of 14.22% in built-up area and destruction of 15.5% of vegetation, 3.26% of barren soil, and 1.05% of the waterbody in the previous 21 years in Narayanganj Upazila. This study will help the administration, agricultural directorates, Pourashava office, and city corporation authority to take necessary measures to mitigate the adverse impacts of land cover change. They can make new rules and regulations on the construction of buildings, restrictions on filling water bodies and measures to conserve vegetation.

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A GIS and remote sensing approach for measuring summer-winter variation of land use and land cover indices and surface temperature in Dhaka district, Bangladesh

Cited by 19 publications

References 56 publications

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

A Systematic Review of Machine Learning Applications in Land Use Land Cover Change Detection using Remote Sensing

Examining the spatio-temporal relationship between LST, NDVI, NDBI and LULC change of Pachhua dun, Dehradun, Uttarakhand (India)

Identifying Urban Heat Effect through Satellite Image Analysis: Focusing on Narayanganj Upazila, Bangladesh

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