Monitoring urbanization in mega cities from space

Taubenböck, Hannes; Esch, Thomas; Felbier, Andreas; Wiesner, Michael; Roth, Achim; Dech, Stefan

doi:10.1016/j.rse.2011.09.015

Cited by 418 publications

(245 citation statements)

References 30 publications

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“…The assumption that urban land development is irreversible is commonly adopted for land change studies (CarrionFlores & Irwin, 2004;Seto et al, 2011;Schneider, 2012;Taubenböck et al, 2012) and in practice holds true especially at the temporal scale of interest (e.g. decade).…”

Section: Definitionsmentioning

confidence: 99%

“…Spatial information has been incorporated through object-oriented approaches like segmentation Bhaskaran et. al., 2011;Taubenböck et al, 2012), as well as by adding window-based texture features (Shaban & Dikshit, 2001) or spatial statistics (Ghimore et. al., 2010) into pixel-based change detection processing streams.…”

Section: Introductionmentioning

confidence: 99%

“…During the last two decades, eight different teams have developed global maps that depict the spatial extent of urbanization ca 2000 (Gamba & Herold, 2009), while others have proposed methods that could be applied in the future (Zhang & Seto, 2011;Taubenböck et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

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Abstract. Urbanization is one of the most important components of global environmentalchange, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a new methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify ca 2010 urban extent; and (3) mapping newly built areas (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250 m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socicoeconomic/political and ecological/climatic conditions with good accuracy (70-91% overall accuracy by country, 69-89% by biome). The 250 m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of ii the error in the change detection is related to human decision making or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as provide a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.

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Section: Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

View full text Add to dashboard Cite

show abstract

“…A maximum likelihood classification, based on stable spectral features such as the normalized difference vegetation index (NDVI), provided urban footprints of the investigated area. An urban footprint is here defined as the physical representation of the urban area, which can be discriminated by satellite imagery (Taubenböck et al, 2012). Three change maps are shown in Fig.…”

Section: Group 2 (Urban Earth Observation German Aerospace Center (Dmentioning

confidence: 99%

Evaluating the use of uncertainty visualization for exploratory analysis of land cover change: A qualitative expert user study

Kinkeldey

Schiewe

Gerstmann

et al. 2015

Computers & Geosciences

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a b s t r a c tExtensive research on geodata uncertainty has been conducted in the past decades, mostly related to modeling, quantifying, and communicating uncertainty. But findings on if and how users can incorporate this information into spatial analyses are still rare. In this paper we address these questions with a focus on land cover change analysis. We conducted semi-structured interviews with three expert groups dealing with change analysis in the fields of climate research, urban development, and vegetation monitoring. During the interviews we used a software prototype to show change scenarios that the experts had analyzed before, extended by visual depiction of uncertainty related to land cover change. This paper describes the study, summarizes results, and discusses findings as well as the study method. Participants came up with several ideas for applications that could be supported by uncertainty, for example, identification of erroneous change, description of change detection algorithm characteristics, or optimization of change detection parameters. Regarding the aspect of reasoning with uncertainty in land cover change data the interviewees saw potential in better-informed hypotheses and insights about change. Communication of uncertainty information to users was seen as critical, depending on the users' role and expertize. We judge semi-structured interviews to be suitable for the purpose of this study and emphasize the potential of qualitative methods (workshops, focus groups etc.) for future uncertainty visualization studies.

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“…De acordo com Kochhan e Gleyzes (2012) e Taubenböck et al (2012), devido à sua complexidade geográfica, as megacidades são muito peculiares quanto aos aspectos culturais, ambientais, socioeconômicos e demográficos, bem como quanto aos serviços básicos de infraestrutura, tais como energia, abastecimento de água, telefonia, sistemas de transportes, etc. Os problemas gerados nas megacidades possuem a mesma magnitude "mega", e são mais evidentes nos mercados emergentes, onde a maioria das megacidades está localizada.…”

Section: Classificação De Uso E Ocupação Do Solo Urbanounclassified

Identificação de padrões de uso do solo urbano em São Paulo/SP utilizando parâmetros de variogramas.

Barros¹

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Este exemplar foi revisado e alterado em relação à versão original, sob responsabilidade única do autor e com a anuência de seu orientador. que é uma classe com padrões e características singulares, já a identificação das classes "Ocupação Densa Regular" e "Ocupação Densa Irregular" não obteve uma precisão boa, sendo que essas classes são similares em diversos aspectos.Palavras chave: Sensoriamento Remoto, geoestatística, variograma, padrões urbanos. ABSTRACTThe images of high spatial resolution studies of leveraged Remote Sensing in urban environments, as they enable better distinction of the elements that make up this very heterogeneous environment.Geostatistical techniques are increasingly used in studies of Remote Sensing, the variogram is an important tool geostatiscal analysis, because it allows to understand the spatial behavior of a regionalized variable, in this case, the gray levels of a satellite image.This study aims to assess the methodological proposal is to identify urban residential

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Monitoring urbanization in mega cities from space

Cited by 418 publications

References 30 publications

Detecting change in urban areas at continental scales with MODIS data

Detecting change in urban areas at continental scales with MODIS data

Evaluating the use of uncertainty visualization for exploratory analysis of land cover change: A qualitative expert user study

Identificação de padrões de uso do solo urbano em São Paulo/SP utilizando parâmetros de variogramas.

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