Monitoring rapid urban expansion using a multi-temporal RGB-impervious surface model

Shahtahmassebi, AmirReza; Yu, Zhou; Wang, Ke; Hong, Xu; Deng, Jin Song; Li, Jia Dan; Luo, Ran; Ji, Wu; Moore, Nathan

doi:10.1631/jzus.a1100034

Cited by 17 publications

(15 citation statements)

References 41 publications

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“…Therefore, MESMA can effectively address mixed pixel problems in complicated urban environments. Many previous studies have demonstrated the applicability of MESMA to the study of coastal salt marshes [41], Amazonia [40], and urban regions [16,18,20,42,43]. However, few studies have performed sub-pixel change detection using MESMA in the context of CSGT [44].…”

Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

“…These three optimization techniques included count-bsed selection (CoB), endmember average root mean square error (EAR), and minimum average spectral angle (MASA). The criteria used to select the optimum spectrum for each endmember were as follows: the highest CoB, lowest EAR, and lowest MASA [20,43]. Third, the MESMA method was executed using a range of spectral mixture models after construction of the optimum library.…”

Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

“…Finally, the best single model for each pixel was selected based on the minimum value of the root mean square error (RMSE), which was 0.025. The RMSE value was based on research on Cixi County [20,43]. Three fractional images were generated by the optimal models for each Landsat image: an impervious surface image, a soil image, and a vegetation image.…”

Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

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Assessing the Impacts of Chinese Sustainable Ground Transportation on the Dynamics of Urban Growth: A Case Study of the Hangzhou Bay Bridge

Zheng

Huang

et al. 2016

Sustainability

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Abstract:Although China has promoted the construction of Chinese Sustainable Ground Transportation (CSGT) to guide sustainable development, it may create substantial challenges, such as rapid urban growth and land limitations. This research assessed the effects of the Hangzhou Bay Bridge on impervious surface growth in Cixi County, Ningbo, Zhejiang Province, China. Changes in impervious surfaces were mapped based on Landsat images from 1995, 2002, and 2009 using a combination of multiple endmember spectral mixture analysis (MESMA) and landscape metrics. The results indicated that the area and density of impervious surfaces increased significantly during construction of the Hangzhou Bay Bridge (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009). Additionally, the bridge and connected road networks promoted urban development along major roads, resulting in compact growth patterns of impervious surfaces in urbanized regions. Moreover, the Hangzhou Bay Bridge promoted the expansion and densification of impervious surfaces in Hangzhou Bay District, which surrounds the bridge. The bridge also accelerated socioeconomic growth in the area, promoting rapid urban growth in Cixi County between 2002 and 2009. Overall, the Hangzhou Bay Bridge is an important driver of urban growth in Cixi County, and policy suggestions for sustainable urban growth should be adopted in the future.

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Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

Section: Estimating the Amount Of Impervious Surface Within A Pixelmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the Impacts of Chinese Sustainable Ground Transportation on the Dynamics of Urban Growth: A Case Study of the Hangzhou Bay Bridge

Zheng

Huang

et al. 2016

Sustainability

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“…classification and urban metrics) from ISFs (e.g. Van de Voorde et al, 2011;Shahtahmassebi et al, 2012) but such products may be inappropriate because some information about the characteristics of land cover transitions could be lost in this step.…”

Section: More Than Two Imagesmentioning

confidence: 99%

Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

Shahtahmassebi

Song

Zheng

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hotspot regions in the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re-densification mechanisms.Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms.

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“…When Turkey is considered, it is estimated that this ratio is 73% and will become 84% in 2050 within the frame of the researches performed (URL-1,2014). It is seen that residential, occupational and transportation areas and settlements developed by rapid expansion in urban areas have become a constantly growing fact subject to population increase (Shahtahmassebi et al, 2012). The constructions built in line with increasing requirements are signs that the natural environment and climate are adversely affected in contrast to being positive signs of growth and wealth.…”

Section: Introductionmentioning

confidence: 99%

The Usage of Rusboost Boosting Method for Classification of Impervious Surfaces

Kesikoglu

Atasever

Özkan

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4 KEY WORDS: RUSboost, Classification tree, Impervious surface, Image classification, Remote sensing ABSTRACT:Impervious surface areas are artificial structures covered by materials such as asphalt, stone, brick, rooftops and concrete. Buildings, parking lots, roads, driveways and sidewalks are shown as impervious surfaces. They increase depending on the population growth. The spatial development of impervious surface expansion is necessary for better understanding of the urbanization status and its effect on environment. There are different impervious surface determining approaches met in literature. In this paper, it is aimed to extract the impervious surface areas of Kayseri city, Turkey by using remote sensing techniques. It is possible to group these techniques under a few main topics as V-I-S (vegetation-impervious surface-soil) model, based on spectral mixture analysis or decision tree algorithms or impervious surface indices. According to these techniques, we proposed a new technique by using RUSBoost algorithm based on decision tree in this study. In this scope, Landsat 8 LDCM image belonging to July, 2013 was used. Determining of impervious surface areas accurately depends on accuracy of image classification methods. Therefore, satellite image was classified separately by using Classification Tree and RUSBoost boosting method which increases accuracy of the classification method based on decision tree. Classification accuracies of these supervised classification methods were compared and it was observed that the best overall accuracy was obtained with RUSBoost method. For this reason, RUSBoost method was preferred to determine impervious surface areas. The overall accuracies were obtained 95% with Classification Tree and 97% with RUSBoost boosting method.

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Monitoring rapid urban expansion using a multi-temporal RGB-impervious surface model

Cited by 17 publications

References 41 publications

Assessing the Impacts of Chinese Sustainable Ground Transportation on the Dynamics of Urban Growth: A Case Study of the Hangzhou Bay Bridge

Assessing the Impacts of Chinese Sustainable Ground Transportation on the Dynamics of Urban Growth: A Case Study of the Hangzhou Bay Bridge

Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

The Usage of Rusboost Boosting Method for Classification of Impervious Surfaces

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