2017
DOI: 10.3390/rs9030249
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Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index

Abstract: Abstract:In recent years, hundreds of Earth observation satellites have been launched to collect massive amounts of remote sensing images. However, the considerable cost and time to process the significant amount of data have become the greatest obstacle between data and knowledge. In order to accelerate the transformation from remote sensing images to urban thematic maps, a strategy to map the bare land automatically from Landsat imagery was developed and assessed in this study. First, a normalized difference… Show more

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Cited by 90 publications
(56 citation statements)
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“…Further investigation is needed to tackle the problem of spectral similarity of urban land covers, especially impervious surfaces and bare soil. More recently, several indices specifically dealing with bare soil surfaces have been published [53][54][55]. These bare soil indices could be coupled with built-up indices for improved ISA mapping.…”
Section: Discussionmentioning
confidence: 99%
“…Further investigation is needed to tackle the problem of spectral similarity of urban land covers, especially impervious surfaces and bare soil. More recently, several indices specifically dealing with bare soil surfaces have been published [53][54][55]. These bare soil indices could be coupled with built-up indices for improved ISA mapping.…”
Section: Discussionmentioning
confidence: 99%
“…As the output of this method showed a higher NDBaI value of built-up areas than bare land, this index appears not to be appropriate for use in cities in semi-arid environments. As-syakur et al [7] proposed an enhanced built-up and bareness index (EBBI) and provided a case study of Denpasar city in Bali, Indonesia, with 90.5% overall accuracy of bare land detection [8]. A normalized difference bare land index (NBLI) and unsupervised classification was used by Li et al [8] to automatically map bare land from Landsat images.…”
Section: Introductionmentioning
confidence: 99%
“…We added these nonlinear indices as additional inputs to the classifier to improve identification of vegetation and bodies of water (NDVI, NDWI and EVI) and other types of land cover (NDBI and UI). Although the latter two indices were originally designed to capture built-up and urban areas, they are also sensitive to the spectral characteristics of bare land [17], which are relatively similar to those of urban areas [18].…”
Section: Classification and Classifier Performancementioning
confidence: 99%