Assessing Urban Environmental Quality Change of Indianapolis, United States, by the Remote Sensing and GIS Integration

Liang, Bo; Weng, Qihao

doi:10.1109/jstars.2010.2060316

Cited by 87 publications

(53 citation statements)

References 37 publications

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“…Hardisky et al [43] found that NDWI is able to track changes in vegetation biomass and water stress more than NDVI. NDWI can also be used to measure and assess the turbidity of water bodies from remote sensing data [44], and therefore, Liang and Weng [11] used NDWI as a parameter to assess the UEQ where the higher NDWI represents the higher urban quality (i.e., close to lake shore). The NDWI (ranging from −1 to 1) can be are calculated using Equation (7) [14]:…”

Section: Normalized Difference Vegetation Index (Ndwi)mentioning

confidence: 99%

An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

Faisal

Shaker

2017

Sustainability

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Abstract:The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

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Section: Normalized Difference Vegetation Index (Ndwi)mentioning

confidence: 99%

An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

Faisal

Shaker

2017

Sustainability

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“…Remotely sensed derived variables, GIS thematic layers, and census data are three essential data sources for urban analyses, and their integration is thus a central theme in urban analysis. Since census data collected within spatial units can be stored as GIS attributes, the combination of census and remote sensing data combined with a GIS can be envisaged in three main ways [62] that relate to urban analyses: (i) remote sensing imagery have been used in extracting and updating transportation networks [63][64][65][66] and buildings [67][68][69][70], providing land use/cover data and biophysical attributes [17,58,59,[71][72][73], and detecting urban expansion [61,74,75]; (ii) Census data have been used to improve image classification in urban areas [60,76,77]; (iii) The integration of remote sensing and census data has been applied to estimate population and residential density [78][79][80][81][82][83][84][85][86][87][88], to assess socioeconomic conditions [89,90], and to evaluate the quality of life [91][92][93][94]. We note that census data are available at a number of different scales, as determined by independent (not remote sensing-based) spatial areas, typically down to census block levels.…”

Section: Integrating Remote Sensing and Gis For Urban Analysismentioning

confidence: 99%

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

et al. 2011

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Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

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“…Remotely sensed imagery is an effective data source for urban environment analysis that is inherently suited to provide information on urban land cover characteristics and their changes over time at various spatial and temporal scales [2][3][4][5][6]. In the past decades, remote sensing has been widely used in various applications, such as urban structure extraction, urbanization monitoring, change detection, and so on [5,[7][8][9][10][11][12][13]. With the development and innovations in data, technologies, and theories in the wider arena of earth observation, urban remote sensing has rapidly gained popularity among a wide variety of communities from many aspects such as Land Use/Land Cover (LULC) mapping, Urban Heat Islands (UHIs) analysis, impervious surface area estimation and urban ecological security assessment [12].…”

Section: Introductionmentioning

confidence: 99%

“…Many algorithms and models have been developed to analyze urban environment [5][6][7][8][9][10][11][12][13]. However, they are usually implemented with independent functions in separate modules.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples

Liu

Xia

et al. 2014

Remote Sensing

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Remote sensing imagery has been widely used in urban growth and environment analysis with many effective and advanced strategies being developed. However, most of these approaches are separated from each other. There is an urgent need to combine different modules into some practical processing chains. Firstly, we present a comprehensive analysis of key processing chains in applying remote sensing images to urban environment analysis from such aspects as Land Use/Land Cover (LULC), urban landscape ecology, Urban Heat Islands (UHIs), vegetation and water monitoring, change detection, urban ecological security assessment and urban environmental mapping. Secondly, an integrated system, namely Urban Environment Analysis System (UEAS), is implemented based on the 9459 aforementioned processing chains to analyze urban environment using multi-temporal and multi-source remotely sensed data. Several case studies are demonstrated to confirm the effectiveness of the integrated system and the combined processing chains. The contributions of this paper lie in introducing ensemble learning to urban environment remote sensing, combining remote sensing derived information with thematic models for urban environment assessment, and developing an integrated system for urban environment analysis.

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Assessing Urban Environmental Quality Change of Indianapolis, United States, by the Remote Sensing and GIS Integration

Cited by 87 publications

References 37 publications

An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System and Examples

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