Identifying Most Influential Land Use Parameters Contributing Reduction of Surface Water Bodies in Rajshahi City, Bangladesh: A Remote Sensing Approach

Kafy, Abdulla Al; Islam, Muhaiminul; Khan, Abdur Rouf; Ferdous, Lamia; Hossain, Mainul

doi:10.21523/gcj1.18020202

Cited by 19 publications

(4 citation statements)

References 29 publications

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“…Therefore, it is clear that areas with vegetation and water bodies attenuate the accumulated heat. In contrast, areas with exposed soil and certain construction materials tend to increase the temperature [22,[37][38][39][40][41][42], so the hypotheses were developed to explain the results obtained in the northeastern capitals. In addition, Land Use Land Cover (LULC) images from MapBiomas [43] were used to get the percentages of each class present in each adjacent area (Table A1 and Figure 6).…”

Section: Surface Urban Heat Islandmentioning

confidence: 99%

Urban Heat Island Assessment in the Northeastern State Capitals in Brazil Using Sentinel-3 SLSTR Satellite Data

Fernandes,

Ferreira,

Nascimento

et al. 2024

Sustainability

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The lack of a solid methodology defining urban and non-urban areas has hindered accurately estimating the Surface Urban Heat Island (SUHI). This study addresses this issue by using the official national urban areas limit together with a surrounding areas classification to define three different reference classes: the urban adjacent (Ua), the future urban adjacent (FUa), and the peri-urban (PUa), consequently providing a more accurate SUHI estimation on the nine northeastern Brazilian capitals. The land surface temperature was obtained in this study using the Sentinel-3 satellite data for 2019 and 2020. Subsequently, the maximum and average SUHI and the complementary indexes, specifically the Urban Thermal Field Variation Index (UTFVI) and the Thermal Discomfort Index (TDI), were calculated. The UTFVI expresses how harmful the eco-environmental spaces are, with a very strong SUHI for three capitals. In addition, the TDI, with values between 24.6–28.8 °C, expresses the population’s thermal comfort, with six capitals showing a very hot TDI. These findings highlight the need for strategies to mitigate the effects of the SUHI and ensure the population’s thermal comfort. Therefore, this study provides a better SUHI understanding and comparison for the Brazilian northeastern region, which has diverse areas, populations, and demographic variations.

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Section: Surface Urban Heat Islandmentioning

confidence: 99%

Urban Heat Island Assessment in the Northeastern State Capitals in Brazil Using Sentinel-3 SLSTR Satellite Data

Fernandes,

Ferreira,

Nascimento

et al. 2024

Sustainability

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“…The Landsat images were obtained during the same month to avoid seasonal volatility. To create LULC and LST distribution maps, illustrations in cloud-free environments were taken in the late fall months of September, October, and November [12]. ArcMap was used to process images, classify images, and identify picture changes.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques

Islam,

Shahriar

2023

Preprint

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Urbanization has had a significant impact on global environmental change, with more than half of the population now living in cities. Rapid population growth and changes in land use and land cover (LULC) are closely linked, leading to significant impacts on ecosystems, habitats, and regional and local climates. The drastic changes in LULC resulting from rapid urban growth have played a major role in these impacts. The rise in urban surface temperature is a result of inadequate planning and uncontrolled management of land use and land cover changes. This study utilizes multi-temporal and multi-spectral Landsat 7 ETM and Landsat 8 OLI satellite data to analyze the impact of different land use and land cover changes on surface temperature in the Jessore region of Bangladesh for the years 2000, 2010, and 2020. The analysis of land use and land cover changes indicates a significant increase in populated areas, accompanied by a noticeable decrease in vegetation, aquatic land, and agricultural land. Over the past two decades, the built-up area of the study region has almost doubled. An analysis of changes in Land Surface Temperature (LST) reveals that the built-up parts experienced the most significant increase in temperature, followed by empty space, vegetation, farmland, and water bodies. The research LULC-LST profiles also showed that the degree of heat was highest in populated regions and lowest in water bodies. The LST has risen by about\(3^\circ\)C over the past 20 years. The research shows that the amount of vegetation in the study area is declining, while the amount of non-evaporating surfaces is increasing, and the surface temperature is significantly rising. Remote sensing is an effective method for quickly studying urban growth and determining how urbanization affects LST. The findings contributed to the establishment of a policy that promoted inclusive climate resilience and made Jessore more resilient to the effects of climate change by increasing knowledge about urban LST's relationship to LULC.

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“…Human migration to Cities causes urban areas to grow every year and creates rapid changes to their ecosystems, biodiversity, natural landscapes, and the environment (McKinney, 2002). More than 70% of the world's population is anticipated to live in urban areas in the next 30 years (Celik, Kaya, Alganci, & Seker, 2019;Grimmond, 2007;Handayanto, Kim, & Tripathi, 2017;Kafy, Islam, Ferdous, Khan, & Hossain, 2019;Ullah et al, 2019). While this development is a sign of economic growth and economic stability in the region, it has several short and long-term consequences.…”

Section: Introductionmentioning

confidence: 99%

“…While this development is a sign of economic growth and economic stability in the region, it has several short and long-term consequences. Over the last decade, geographers, urban planners, and climate scientists have been paying considerable attention to elevated LST over urban areas (Al-sharif & Pradhan, 2014;Kafy et al, 2019;Maduako, Yun, & Patrick, 2016;M. Rahman, 2016;Zheng, Shen, Wang, & Hong, 2015;Zine El Abidine, Mohieldeen, Mohamed, Modawi, & Al-Sulaiti, 2014).…”

Section: Introductionmentioning

confidence: 99%

Use of Cellular Automata-Based Artificial Neural Networks For Detection and Prediction of Land Use Changes in North Eastern Dhaka City

Rahman

2021

Preprint

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The purpose of this study was to examine the pattern of change in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in Mirpur and its surrounding area (north-eastern part of Dhaka) over the last 30 years using Landsat Satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, five-year intervals of Landsat 4-5 TM and Landsat 8 OLI pictures were utilized to track the relationship between LULC changes and LST. Cellular Automata-based Artificial Neural Network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. Two environmental indices were analyzed to determine their link with LST: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). The link between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature has been increasing steadily for the previous 30 years. For the year 2019, it was determined that roughly 86 percent of total land area has been converted to built-up area and that 89 percent of land area has an LST greater than 28°C. According to the study, if the current trend continues, 72 percent of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a Kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

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Identifying Most Influential Land Use Parameters Contributing Reduction of Surface Water Bodies in Rajshahi City, Bangladesh: A Remote Sensing Approach

Cited by 19 publications

References 29 publications

Urban Heat Island Assessment in the Northeastern State Capitals in Brazil Using Sentinel-3 SLSTR Satellite Data

Urban Heat Island Assessment in the Northeastern State Capitals in Brazil Using Sentinel-3 SLSTR Satellite Data

Assessing the Effect of Land Use and Land Cover Changes on Land Surface Temperature in Jessore District, Bangladesh using Remote Sensing Techniques

Use of Cellular Automata-Based Artificial Neural Networks For Detection and Prediction of Land Use Changes in North Eastern Dhaka City

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