Assessment of impervious surface growth in urban environment through remote sensing estimates

Chaudhuri, Anindita Sarkar; Singh, Pratika; C., S.

doi:10.1007/s12665-017-6877-1

Cited by 23 publications

(6 citation statements)

References 24 publications

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“…As for the satellites used in the studies, most use Landsat [159,160], and there are considerations pointed out in some of the analyzed papers. MODIS is pointed out as interesting for research, but some studies concluded that temperature data are overestimated, especially during the daytime, and underestimated at the nighttime when compared to in situ data [161,162], or showed higher LST results in winter when compared to in situ data [163] and variability in measured temperature [164], especially in very dry or very wet weather conditions [165].…”

Section: Social Economic and Healthmentioning

confidence: 99%

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

2021

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Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus and Web of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.

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Section: Social Economic and Healthmentioning

confidence: 99%

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

2021

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“…Much research shows that an impervious surface is one of the most important factors for urban rainstorm waterlogging. As impervious surface information is very easily obtained through remote sensing images for urban area [117][118][119][120][121][122], we used impervious surface percentage (ISP) as one of the explanatory variables in our regression model (Figure 3g).…”

Section: Explanatory Variablesmentioning

confidence: 99%

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Tran

Dang

et al. 2020

Water

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In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

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“…The LSMA method assumes that the spectral brightness values of mixed pixels are a linear combination of the spectral brightness values of the basic components of the mixed pixels, which are also called endmembers. By calculating the composition ratio of each endmember in the mixed pixel, the spectrum of the mixed pixels can be unmixed into a linear combination of various endmember spectra [23,24]. The LSMA with full abundance constraints can be expressed as:…”

Section: Delsma Modelmentioning

confidence: 99%

“…The spectral mixture analysis method has been used to decompose the proportion of endmembers of various features in each pixel, which can significantly improve the interpretation accuracy of medium resolution remote sensing images [23]. In spectral mixture analysis, the linear spectral mixture analysis (LSMA) is the most widely used method [24][25][26][27]. This method is based on the vegetation-impervious surface-soil (V-I-S) conceptual model, combined with the fully constrained least squares (FCLS) method for mixed pixel decomposition.…”

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confidence: 99%

Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

et al. 2019

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Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.

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Assessment of impervious surface growth in urban environment through remote sensing estimates

Cited by 23 publications

References 24 publications

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites

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