Classification of cities in Bangladesh based on remote sensing derived spatial characteristics

Rahman, Md. Shahinoor; Mohiuddin, Hossain; Kafy, Abdulla Al; Sheel, Pintu Kumar; Di, Liping

doi:10.1016/j.jum.2018.12.001

Cited by 64 publications

(41 citation statements)

References 31 publications

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“…However, to do this, it is first necessary to understand spatial expansion patterns at the municipality level. As capital cities are the engines of country growth and attention, developed built-up area maps can comprehensively provide support on spatial urban characteristic inheritance and spatial city planning system to release essential funds required for infrastructure development [70].…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu

et al. 2019

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During the last few decades, a large number of people have migrated to Kathmandu city from all parts of Nepal, resulting in rapid expansion of the city. The unplanned and accelerated growth is causing many environmental and population management issues. To manage urban growth efficiently, the city authorities need a means to be able to monitor urban expansion regularly. In this study, we introduced a novel approach to automatically detect urban expansion by leveraging state-of-the-art cloud computing technologies using the Google Earth Engine (GEE) platform. We proposed a new index named Normalized Difference and Distance Built-up Index (NDDBI) for identifying built-up areas by combining the LandSat-derived vegetation index with distances from the nearest roads and buildings analysed from OpenStreetMap (OSM). We also focused on logical consistencies of land-cover change to remove unreasonable transitions supported by the repeat photography. Our analysis of the historical urban growth patterns between 2000 and 2018 shows that the settlement areas were increased from 63.68 sq km in 2000 to 148.53 sq km in 2018. The overall accuracy of mapping the newly-built areas of urban expansion was 94.33%. We have demonstrated that the methodology and data generated in the study can be replicated to easily map built-up areas and support quicker and more efficient land management and land-use planning in rapidly growing cities worldwide.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu

et al. 2019

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“…The accuracy of the classified maps was evaluated through 100 field level and 300 Google Earth image random sampling ground truth data. For accuracy assessment, the overall accuracy ( equation 1 ), user accuracy ( equation 2 ), producer accuracy ( equation 3 ), and kappa statistics ( equation 4 ) were calculated, and these are one of the best quantitative procedures for image classification accuracy [ 2 ].

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Section: Methodsmentioning

confidence: 99%

“…Urbanization is a challenge for any country. Developed countries face fewer daunting challenges than developing countries because of rapid and unplanned urbanization [ 1 , 2 ]. According to United Nations reports, the urban population will upsurge by more than 2.5 billion people by 2050, with Asia and Africa accounting for 90 % of the increase [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Kafy

Faisal

Rakib

et al. 2021

Heliyon

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“…Serious complications such as social and environmental degradation, biodiversity loss, destruction of ecosystems, and health disasters emerge from uncontrolled urbanization ( Anbalagan, 1993 ; Dewan and Yamaguchi, 2009 ; Gazi et al., 2020 ). Bangladesh is rapidly urbanizing compared to other South Asian countries, despite being predominately rural ( Muzzini and Aparicio, 2013 ; Rahman et al., 2019 ). Many city-scale studies suggest that major cities in Bangladesh are going through uncontrolled urban growth with negative environmental consequences ( Ahmed et al., 2013 ; Fattah et al., 2021a , b ; Gazi et al., 2020 ; Kafy et al., 2020 , 2021 ; Naim and Kafy, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Many city-scale studies suggest that major cities in Bangladesh are going through uncontrolled urban growth with negative environmental consequences ( Ahmed et al., 2013 ; Fattah et al., 2021a , b ; Gazi et al., 2020 ; Kafy et al., 2020 , 2021 ; Naim and Kafy, 2021 ). ( Rahman et al., 2019 ) classified 331 cities of Bangladesh using night light intensity and placed Bogura in the same cluster with seven major cities. Therefore, the impact of urban development on the thermal and ecological environment of Bogura needs to be assessed to help the authorities for better environmentally-conscious urban planning.…”

Section: Introductionmentioning

confidence: 99%

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

Abir

Ahmmed

Sarker

et al. 2021

Heliyon

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In recent years, the world has shown considerable concerns about environmental degradation accompanied by urban expansion. In terms of size, Bogura is equivalent to most of the major cities in Bangladesh, yet no thermal and ecological assessment has ever been conducted here. This study uses multitemporal Landsat satellite images between 2001 and 2020 to investigate the thermal and ecological conditions of Bogura Sadar (sub-district). Land surface temperature (LST) is obtained from Landsat images using the widely used radiative transfer equation. The thermal and ecological conditions are evaluated by computing urban heat island (UHI) and urban thermal field variance index (UTFVI) from LST data. The influence of vegetation, built-area, water-body, and bare soil on LST are examined using land cover indices through pixel-level multivariate linear regression analysis. According to the findings of this sub-district-scale (urban and rural areas) study, the mean LST has increased by 0.62 °C in the last 20 years. As per local administrative-wise findings, LST has increased in most areas, regardless of their urban or rural function. The difference between the urban area and the rest of the surroundings was significant (1.74 °C) in 2020. In 2001, UHI affected area was 5.65 km 2 , which expanded to 8.84 km 2 in 2020. Thermal and ecological conditions are worse in urban areas than its surrounding areas. The regression models of the LST and land cover indices could explain more than half (R 2 : 0.66 to 0.73) of LST variation over the years. Land cover could explain the LST in 2020 to the least extent implying that anthropogenic activities have greater influence than earlier. Land cover could explain less than half of the LST variation in the urban area.

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Classification of cities in Bangladesh based on remote sensing derived spatial characteristics

Cited by 64 publications

References 31 publications

Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu

Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Thermal and ecological assessment based on land surface temperature and quantifying multivariate controlling factors in Bogura, Bangladesh

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