A new index for mapping built-up and bare land areas from Landsat-8 OLI data

Zhou, Yi; Yang, Guang; Wang, Shixin; Wang, Litao; Wang, Futao; Liu, Xiongfei

doi:10.1080/2150704x.2014.973996

Cited by 71 publications

(35 citation statements)

References 10 publications

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“…In contrast, NDBI has performed poorly for mapping built-up areas in the semi-arid cities of Urumqi and Shihezi in western China [35,36]. Zhou et al [21] also reported a low accuracy (57.4%) of NDBI for Zhengzhou city in China from Landsat 8 OLI data.…”

Section: Discussionmentioning

confidence: 99%

“…To minimize atmospheric absorption features, the width of several OLI bands in Landsat 8 was enhanced [19]. In 2014, Bhatti and Tripathi proposed a built-up area extraction method (BAEM), and similarly, Zhou et al used a built-up and bare-land index (BBI) to extract built-up and bare soil from Landsat 8 [20,21]. Piyoosh and Ghosh [22] developed a normalized ratio urban index (NRUI) and modified normalized difference soil index (MNDSI) to distinguish between urban areas and soil from Landsat 8.…”

Section: Introductionmentioning

confidence: 99%

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

Rasul

Balzter

Ibrahim

et al. 2018

Land

169

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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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Section: Discussionmentioning

confidence: 99%

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

Land

169

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show abstract

“…Nevertheless, classical pre-processing tasks of low-level EO products (e.g. radar terrain correction or optical atmospheric correction) are normally Zhou et al (2014) performed using largely employed software (i.e. ATCOR, SNAP, etc.).…”

Section: Data Pre-processing and Preparation -The Data2times Modulementioning

confidence: 99%

Exploiting big earth data from space – first experiences with the timescan processing chain

et al. 2018

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The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data (>250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat-8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010-2012, and regional Sentinel-1 and Sentinel-2 collections of ∼1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics. ARTICLE HISTORY

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“…Pansharpening is an image fusion technique in which high resolution panchromatic data is combined with lower resolution multispectral data to obtain a colorized high-resolution dataset. An atmospheric correction has been applied to remove the influence of atmospheric scattering (Zhou et al 2014). The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FlAASH) and the ATmospheric CORection (ATCOR) modules are used.…”

Section: Data Preprocessingmentioning

confidence: 99%

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Bouhennache

Bouden

Taleb-Ahmed

et al. 2018

Geocarto International

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A new index for mapping built-up and bare land areas from Landsat-8 OLI data

Cited by 71 publications

References 10 publications

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

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

Exploiting big earth data from space – first experiences with the timescan processing chain

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

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