A Comparative Study of Built-up Index Approaches for Automated Extraction of Built-up Regions From Remote Sensing Data

Varshney, Avnish; Rajesh, Edida

doi:10.1007/s12524-013-0333-9

Cited by 34 publications

(19 citation statements)

References 14 publications

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“…Subpixel land-cover heterogeneity influences Landsat urban area overestimation which must be considered in order to reduce the bias and improve Landsat-derived urban area estimation (Herold et al 2002;Lu, Moran, and Hetrick 2011;Varshney and Rajesh 2014;Schneider 2012). The complexity and diversity of urban areas identified here from high spatial resolution data, with biases ranging from −2.50% to −34.67%, highlights the inappropriateness of applying a single model to adjust the moderate spatial resolution urban area estimates in a metropolitan region (e.g.…”

Section: Refining Landsat Estimations Using High Spatial Resolution Datamentioning

confidence: 97%

“…Urban areas are complex, heterogeneous environments that are challenging to classify even when using high spatial resolution multispectral imagery (Varshney and Rajesh 2014;Lu, Moran, and Hetrick 2011). Within urban areas, traditional moderate and coarse spatial resolution pixel-based classification methods present multiple challenges due to the land surface spatial heterogeneity and the spectral similarity between urban and non-urban materials (Myint et al 2011).…”

Section: Aerial Image Classificationmentioning

confidence: 99%

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Subpixel land-cover classification for improved urban area estimates using Landsat

MacLachlan

Roberts

Biggs

et al. 2017

International Journal of Remote Sensing

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Urban areas are Earth's fastest growing land use that impact hydrological and ecological systems and the surface energy balance. The identification and extraction of accurate spatial information relating to urban areas is essential for future sustainable city planning owing to its importance within global environmental change and human-environment interactions. However, monitoring urban expansion using medium resolution (30-250 m) imagery remains challenging due to the variety of surface materials that contribute to measured reflectance resulting in spectrally mixed pixels. This research integrates high spatial resolution orthophotos and Landsat imagery to identify differences across a range of diverse urban subsets within the rapidly expanding Perth Metropolitan Region (PMR), Western Australia. Results indicate that calibrating Landsat-derived subpixel land-cover estimates with correction values (calculated from spatially explicit comparisons of subpixel Landsat values to classified high-resolution data which accounts for over [under] estimations of Landsat) reduces moderate resolution urban area over (under) estimates by on an average 55.08% for the PMR. This approach can be applied to other urban areas globally through use of frequently available and/or low-cost high spatial resolution imagery (e.g. using Google Earth). This will improve urban growth estimations to help monitor and measure change whilst providing metrics to facilitate sustainable urban development targets within cities around the world. ARTICLE HISTORY

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Section: Refining Landsat Estimations Using High Spatial Resolution Datamentioning

confidence: 97%

Section: Aerial Image Classificationmentioning

confidence: 99%

Subpixel land-cover classification for improved urban area estimates using Landsat

MacLachlan

Roberts

Biggs

et al. 2017

International Journal of Remote Sensing

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“…Analysis of imagery acquired from WA winter season coincided with peak green-up which provided the greatest contrast between spectrally similar surfaces (e.g., bare earth and urban) [30][31][32]. Imagery date selection was founded upon the strong positive relationship between Australian soil moisture (related to rainfall) and the Normalised Difference Vegetation Index (NDVI) [33], which exhibits an approximate one month lag between peak soil moisture and peak NDVI [33].…”

Section: Data Preprocesingmentioning

confidence: 99%

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

MacLachlan

Biggs

Roberts

et al. 2017

Land

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Earth observation data can provide valuable assessments for monitoring the spatial extent of (un)sustainable urban growth of the world's cities to better inform planning policy in reducing associated economic, social and environmental costs. Western Australia has witnessed rapid economic expansion since the turn of the century founded upon extensive natural resource extraction. Thus, Perth, the state capital of Western Australia, has encountered significant population and urban growth in response to the booming state economy. However, the recent economic slowdown resulted in the largest decrease in natural resource values that Western Australia has ever experienced. Here, we present multi-temporal urban expansion statistics from 1990 to 2015 for Perth, derived from Landsat imagery. Current urban estimates used for future development plans and progress monitoring of infill and density targets are based upon aggregated census data and metrics unrepresentative of actual land cover change, underestimating overall urban area. Earth observation provides a temporally consistent methodology, identifying areal urban area at higher spatial and temporal resolution than current estimates. Our results indicate that the spatial extent of the Perth Metropolitan Region has increased 45% between 1990 and 2015, over 320 km 2 . We highlight the applicability of earth observation data in accurately quantifying urban area for sustainable targeted planning practices.

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“…Information about spatial distribution is essential for city studies and also helpful to address some environmental problems. Built up area extraction becomes a fascinating subject to study (Hidayati et al, 2017;Li & Chen, 2018;Luo et al, 2017;Varshney & Rajesh, 2014; index (MNDWI. Previous studies have applied various approaches, but limitations still exist, an issue which is addressed in this paper.…”

Section: Discussionmentioning

confidence: 99%

Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index

Hidayati

Suharyadi²,

Danoedoro³

2018

For. Geo.

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Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.

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A Comparative Study of Built-up Index Approaches for Automated Extraction of Built-up Regions From Remote Sensing Data

Cited by 34 publications

References 14 publications

Subpixel land-cover classification for improved urban area estimates using Landsat

Subpixel land-cover classification for improved urban area estimates using Landsat

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index

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