Identifying urban boundaries: application of remote sensing and geographic information system technologies

Abstract

This paper focuses on a new definition of urbanization trends by investigating the concept of a fuzzy urban boundary (UB) that assigns different membership levels to urbanized aggregates based on a proposed composite index. The research work builds on this logic to investigate a new approach in defining urbanized areas by compounding the characteristics of the fuzzy density of an urban agglomeration with land use variation and intensity of economic activity. Spatial overlaying capabilities of geographic information system (GIS) are used to model the urbanization trend in the case study of Greater Beirut. The UB is defined using a multispectral high resolution visible (HRV) Satellite Pour L'observation de la Terre (SPOT) satellite image. The challenges of urban modelling using satellite images are addressed through an investigative approach in cartographic feature extraction and delineation of the urban agglomeration. This entails image treatment of the spot HRV image, defining internal characteristics of the urban agglomeration and constructing spatially continuous socio-economic data sets that can be combined with the digital remotely sensed image.Key words: fuzzy logic, GIS, remote sensing, spatial urban modelling, urban boundary, urbanization trends.

Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

“…Nonetheless, the resolution of the imagery and the heterogeneity characteristic of urban landscapes make it difficult to automatically map detailed urban lands solely using optical remote sensing methods (Cockx, Voorde, & Canters, 2014). The use of ancillary datasets such as census data, road networks, impervious surface coverages, landscape metrics, land parcel attributes, and radar data were recently documented to improve urban classifications (Abed & Kaysi, 2003;Berger et al, 2013;Chaudhry & Mackaness, 2008;Hermosilla, Palomar-V azquez, Balaguer-Beser, Balsa-Barreiro, & Ruiz, 2014;Schneider, Friedl, & Potere, 2014;Soergel, 2010;Wu, Qiu, Usery, & Wang, 2009). Fractal methods have also been documented as a successful component in aiding in locating UBs (Tannier & Thomas, 2013;Tannier, Thomas, Vuidel, & Frankhauser, 2011).…”

Spatio‐temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm

“…Remote sensing data, in conjunction with geographic information systems (GIS), have been recognized as an effective tool in quantitatively measuring urban area and modelling urban growth at a relatively large spatial scale (Yeh and Li 1997, Weng measuring a large spatial area, satellite remote sensing has been effectively applied to better understand and monitor landscape development and processes, as well as estimate biophysical characteristics of land surfaces (Roy andTomar 2001, Stow andChen 2002). GIS technology provides a seamless environment for integrating, visualizing and analysing digital data to facilitate change detection and database development (Abed andKaysi 2003, Stewart et al 2004). Significant progress in the acquisition of remotely sensed data in a finer spatio-temporal resolution, compounded with the development of geographic and environment process models, has greatly extended research capability to examine the chronology, causes and impacts of the urbanization process.…”

Applications and Trends of Remote Sensing in Professional Urban Planning

“…More advanced classification techniques, including artificial neural networks and fuzzy set methods (Amed and Kaysi 2003; Herold et al. 2003b; Jensen et al.…”