Mapping Urban Bare Land Automatically  from Landsat Imagery with a Simple Index

Li, Hui; Wang, Cuizhen; Zhong, Cheng; Su, Aijun; Xiong, Chengren; Wang, Jinge; Liu, Junqi

doi:10.3390/rs9030249

Cited by 87 publications

(56 citation statements)

References 19 publications

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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%

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

et al. 2017

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Impervious surface area (ISA) is a key factor for monitoring urban environment and land development. Automatic mapping of impervious surfaces has attracted growing attention in recent years. Spectral built-up indices are considered promising to map ISA distributions due to their easy, parameter-free implementations. This study explores the potentials of impervious surface indices for ISA mapping from Landsat imagery using a case study area in Boston, USA. A modified normalized difference impervious surface index (MNDISI) is proposed, and a Gaussian-based automatic threshold selection method is used to identify the optimal MNDISI threshold for delineating impervious surfaces from background features. To evaluate its effectiveness, comparison analysis is conducted between MNDISI and the original NDISI using Landsat images from three sensors (TM/ETM+/OLI-TIRS) acquired in four seasons. Our results suggest that built-up indices are sensitive to image seasonality, and summer is the best time phase for ISA mapping. With reduced uncertainties from automatic threshold selection, the MNDISI extracts impervious surfaces from all Landsat images in summer with an overall accuracy higher than 87% and an overall Kappa coefficient higher than 0.74. The proposed method is superior to previous index-based ISA mapping from the enhanced thermal integration and automatic threshold selection. The ISA maps from the TM, ETM+ and OLI-TIRS images are not significantly different. With enlarged data pool when all Landsat sensors are considered and automation of threshold selection proposed in this study, the MNDISI could be an effective built-up index for rapid and automatic ISA mapping at regional and global scales.

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Section: Discussionmentioning

confidence: 99%

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

et al. 2017

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“…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%

Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates

Rasul

Balzter

Ibrahim

et al. 2018

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Arid and semi-arid regions have different spectral characteristics from other climatic regions. Therefore, appropriate remotely sensed indicators of land use and land cover types need to be defined for arid and semi-arid lands, as indices developed for other climatic regions may not give plausible results in arid and semi-arid regions. For instance, the normalized difference built-up index (NDBI) and normalized difference bareness index (NDBaI) are unable to distinguish between built-up areas and bare and dry soil that surrounds many cities in dry climates. This paper proposes the application of two newly developed indices, the dry built-up index (DBI) and dry bare-soil index (DBSI) to map built-up and bare areas in a dry climate from Landsat 8. The developed DBI and DBSI were applied to map urban areas and bare soil in the city of Erbil, Iraq. The results show an overall classification accuracy of 93% (κ = 0.86) and 92% (κ = 0.84) for DBI and DBSI, respectively. The results indicate the suitability of the proposed indices to discriminate between urban areas and bare soil in arid and semi-arid climates.

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“…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%

High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

2017

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Semi-arid ecosystems play a key role in global agricultural production, seasonal carbon cycle dynamics, and longer-run climate change. Because semi-arid landscapes are heterogeneous and often sparsely vegetated, repeated and large-scale ecosystem assessments of these regions have to date been impossible. Here, we assess the potential of high-spatial resolution visible band imagery for semi-arid ecosystem mapping. We use WorldView satellite imagery at 0.3-0.5 m resolution to develop a reference data set of nearly 10,000 labeled examples of three classes-trees, shrubs/grasses, and bare land-across 1000 km 2 of the semi-arid Sertão region of northeast Brazil. Using Google Earth Engine, we show that classification with low-spectral but high-spatial resolution input (WorldView) outperforms classification with the full spectral information available from Landsat 30 m resolution imagery as input. Classification with high spatial resolution input improves detection of sparse vegetation and distinction between trees and seasonal shrubs and grasses, two features which are lost at coarser spatial (but higher spectral) resolution input. Our total tree cover estimates for the study area disagree with recent estimates using other methods that may underestimate treecover because they confuse trees with seasonal vegetation (shrubs and grasses). This distinction is important for monitoring seasonal and long-run carbon cycle and ecosystem health. Our results suggest that newer remote sensing products that promise high frequency global coverage at high spatial but lower spectral resolution may offer new possibilities for direct monitoring of the world's semi-arid ecosystems, and we provide methods that could be scaled to do so.

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Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index

Cited by 87 publications

References 19 publications

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery

Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates

High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

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