Quantifying uncertainty in remote sensing-based urban land-use mapping

Cockx, Kasper; Voorde, Tim Van de; Canters, Frank

doi:10.1016/j.jag.2014.03.016

Cited by 30 publications

(21 citation statements)

References 38 publications

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“…Methods such as artificial neural networks, expert systems, vegetation-impervious surface-soil (VIS) classifications, and support vector machines have been widely applied in urban land-use classifications (Foody, 2000;Pacifici, Chini, & Emery, 2009;Pal & Foody, 2010). 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).…”

Section: Introductionmentioning

confidence: 99%

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

Tong

Frazier

et al. 2015

Habitat International

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a b s t r a c tPeriodic monitoring and multi-scale characterization of urban sprawl is essential for improving urban planning and development. However, historical sprawl analysis is not well suited for the neourbanization occurring in most cities in China due to the limited data available. This paper proposes a concise and cost-effective method for automating the extraction of urban boundaries (UBs). The method uses integrated land-use information entropy (LUIE) model along with ordinary Kriging based on a gridded land-use map derived from Landsat imagery to extract UBs. Results indicate that overall extraction accuracies greater than 90% were obtained using an 800 m-resolution LUIE combined with Kriging. The method was applied to identify UBs in Wuhan, China during 1987e2010, and the UBs were characterized at multiple scales and analyzed using landscape metrics. Results show varied landscape dynamics at local administrative and city scales. The study demonstrates that the method for UB identification and multi-scale analysis has the potential to contribute to sprawl monitoring and measurement at multiple spatial scales. Moreover, the findings from this study can potentially guide policy makers and urban planners tasked with understanding and controlling development occurring under neo-urbanization strategies in China.

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Section: Introductionmentioning

confidence: 99%

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

Tong

Frazier

et al. 2015

Habitat International

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“…First, our error simulation was based on a mathematic method that assumed mapping error occurred randomly for boundary pixels of agricultural patches, with lower confidence level. However, various factors, such as complexity of the spectral response [65], classification algorithm [66], uncertainty of samples [67], and spatial resolution of data [68], may affect the accuracy of thematic maps. As discussed in Section 4.1, error also occurs between crop and non-crop, which may perform differently when upscaling maps.…”

Section: Limitations and Future Workmentioning

confidence: 99%

The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps

et al. 2017

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Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of error using a Monte Carlo simulation for two Agriculture Statistic Districts (ASD) in the U.S.A. The results of the simulations were used as base maps for subsequent upscaling, utilizing the majority rule based aggregation method. The results show that increasing error level resulted in higher proportional errors for each crop in both study areas. As a result of increasing error level, landscape characteristics of the base map also changed greatly resulting in higher proportional error in the upscaled maps. Furthermore, the proportional error is sensitive to the crop area proportion in the base map and decreases as the crop proportion increases. These findings indicate that three factors, the error level of the thematic map, the change in landscape pattern/characteristics of the thematic map, and the objective of the project, should be considered before performing any upscaling. The first two factors can be estimated by using pre-existing land cover maps with relatively high accuracy. The third factor is dependent on the project requirements (e.g., landscape characteristics, proportions of cover types, and use of the upscaled map). Overall, improving our understanding of the impacts of land cover mapping error is necessary to the proper design for upscaling and obtaining the optimal upscaled map.

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“…The capabilities of RS satellites make them a robust and reliable source of data for monitoring the expansion of cities at different spatiotemporal scales [7,19]. In a recent study, Cockx et al [12] reported that landcover and land-use information from remote sensing data is a key component in the calibration of many urban growth models. Van de Voorde et al [81] noted that there is a strong relationship between the change of form in landcover and the functional change in land-use through the analysis of satellite imageries.…”

Section: Urban Growth Sprawl and Changementioning

confidence: 99%

Advances in remote sensing applications for urban sustainability

Kadhim

Mourshed

Bray

2016

Euro-Mediterr J Environ Integr

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It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing-for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications.

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Quantifying uncertainty in remote sensing-based urban land-use mapping

Cited by 30 publications

References 38 publications

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

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

The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps

Advances in remote sensing applications for urban sustainability

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