Measuring Dhaka’s Urban Transformation Using Nighttime Light Data

Morshed, Md. Manjur; Chakraborty, Tanmoy; Mazumder, Tanmoy

doi:10.1007/s41651-022-00120-2

Cited by 18 publications

(6 citation statements)

References 66 publications

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“…The Luojia1-01 NTL performs better when reflecting social and economic activities because of its high spatial resolution, no saturation, and lower blooming effect. At present, many studies have used Luojia1-01 NTL images to analyze socioeconomic parameters such as the gross domestic product (GDP) [56], urban activities [47], poverty estimation [57], and urban transformation [58]. However, there are still some limitations in Luojia1-01 NTL, such as the existence of noise background, which creates a large difference between the extremely high and low values.…”

Section: A Efficacy and Potential Of The Intlmentioning

confidence: 99%

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Guo

Zhang

Zhao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (R² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.

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Section: A Efficacy and Potential Of The Intlmentioning

confidence: 99%

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Guo

Zhang

Zhao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…On the spatial scale, the UGDE had inverted pyramid cluster growth characteristics in different types of urban agglomeration. Morshed et al [46] analyzed the urban transformation in Dhaka by using nighttime remote sensing data; they also found that although the regional size of Dhaka continues to grow with the growth of the population and the approach of the industrial center, it still dominates the newly generated urban hotspots in the surrounding urban clusters. This is somewhat similar to the conclusions of our study.…”

Section: Comparison With Similar Researchmentioning

confidence: 99%

Urban Vitality Measurement and Influence Mechanism Detection in China

Pan

Zhu

Zhang³

2022

IJERPH

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Urban vitality is the life force of a city. In this paper, starting from three subsystems of population, economy, and function, the comprehensive index system for measuring urban vitality was constructed respectively from three scales: grid, prefecture-level administrative region, and urban agglomeration. GIS spatial analysis methods were used to measure the urban vitality index and analyze the spatial distribution pattern. Then, the MGWR was used to reveal the main factors affecting the difference in urban vitality and analyze the influence mechanism of urban vitality. Accordingly, countermeasures and suggestions for creating vibrancy were put forward. The result shows the following: At the grid scale, urban vitality presents a spatial distribution pattern of “large dispersion, small agglomeration”, which has significant differentiation characteristics of city scale and hierarchy. At the administrative region scale, the overall vitality of cities at the prefecture level and above in China is not high, and the spatial differences are large. The spatial scan identified 28 vigorous cities with high potential, belonging to 6 vigorous clusters. On the scale of urban agglomeration, according to the degree of vitality, there are three gradients. The spatial difference in urban vitality was affected by the internal characteristics and external environment.

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“…Based on a United Nations report, the proportion of the world’s population living in urban areas is expected to increase to 68% by 2050 [ 3 ]. However, along with the rapid development of urban construction and the urban economy, a large number of natural surfaces have been replaced by impervious surfaces [ 4 , 5 ], causing a series of urban thermal environment problems. Among them, the urban heat island effect is one of the apparent problems affecting urban residents’ quality of life [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone

Wang

Ding

et al. 2023

IJERPH

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Relationship exploration between the street-greenery rate (SGR) of different street types and land surface temperature (LST) is of great significance for realizing regional sustainable development goals. Given the lack of consideration of the local climate zone concept (LCZ), Chongqing’s Inner Ring region was selected as a case to assess the relationship between SGR and LST. Firstly, the LST was retrieved based on Landsat 8 imagery, which was calibrated by the atmospheric correction method; next, the street-greenery rates of different streets were calculated based on the semantic segmentation method; finally, street types were classified in detail by introducing LCZ, and the relationship between SGR and LST was investigated. The results showed that: (1) The LST spatial distribution pattern was closely related to human activity, with the high-temperature zones mainly concentrated in the core commercial areas, dense residential areas, and industrial cluster areas; (2) The average SGR values of expressways, main trunk roads, secondary trunk roads, and branch roads were 21.70%, 22.40%, 24.60%, and 26.70%, respectively. The level of SGR will decrease when the street width increases; (3) There is a negative correlation between the SGR and the LST in most streets. Among them, the LST of secondary trunk roads in low-rise and low-density built-up areas with a south-north orientation had a strong negative correlation with the SGR. Moreover, the wider the street, the higher the cooling efficiency of plants. Specifically, the LST of streets in low-rise and low-density built-up areas with south-north orientation may decrease by 1°C when the street-greenery rate is increased by 3.57%.

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Measuring Dhaka’s Urban Transformation Using Nighttime Light Data

Cited by 18 publications

References 66 publications

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Urban Vitality Measurement and Influence Mechanism Detection in China

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone

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