Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

Gadal, Sébastien; Ouerghemmi, Walid

doi:10.3390/ijgi8030129

Cited by 12 publications

(5 citation statements)

References 46 publications

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“…The selection of the spectral indices was motivated by their widespread application in the literature and by considering that most built-up indices require SWIR bands, which are available only in a coarse resolution for Sentinel-2. The BI2 index has been tested for built-up detection after applying NDVI and NDWI2 to mask vegetation and water [51]. BAI has proven to be useful to detect asphalt and concrete surfaces [47], and SBI has been successfully investigated by [47] and [52].…”

Section: Features Extraction and Temporal Profilesmentioning

confidence: 99%

“…The Sentinel-1 sigma0 VH and Sentinel-2 indices temporal profiles were analyzed to determine suitable threshold boundaries that would represent a change for each land cover type (vegetation, building or soil), and the data from the ground truth datasets were used to validate the method. Thresholding-specific indices have been proposed and successfully applied in many studies [47,51], e.g., thresholding NDVI has been used to qualify land-cover change [53] and detect forest cuts [25]. The use of Sentinel-1 data, which is radar sensitive to variations in height and shape, allowed us to complement the information provided by the Sentinel-2 indices and improve the characterization of the changes to buildings.…”

Section: Change Classificationmentioning

confidence: 99%

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A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium

Petit

Stasolla

Wyard

et al. 2022

Land

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Urban planning is a challenge, especially when it comes to limiting land take. In former industrial regions such as Wallonia, the presence of a large number of brownfields, here called “redevelopment sites”, opens up new opportunities for sustainable urban planning through their revalorization. The Walloon authorities are currently managing an inventory of more than 2200 sites, which requires a significant amount of time and resources to update. In this context, the Sentinel satellites and the Terrascope platform, the Sentinel Collaborative Ground Segment for Belgium, enabled us to deploy SARSAR, an Earth observation service used for the automated monitoring of redevelopment sites that generates regular and automatic change reports that are directly usable by the Walloon authorities. In this paper, we present the methodological aspects and implementation details of the service, which combines two well-known and robust methods: the Pruned Exact Linear Time method for change point detection and threshold-based classification, which assigns the detected changes to three different classes (vegetation, building and soil). The overall accuracy of the system is in the range of 70–90%, depending on the different methods and classes considered. Some remarks on the advantages and possible drawbacks of this approach are also provided.

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Section: Features Extraction and Temporal Profilesmentioning

confidence: 99%

Section: Change Classificationmentioning

confidence: 99%

A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium

Petit

Stasolla

Wyard

et al. 2022

Land

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“…The recognition of some built-up areas is seriously affected by seasonal changes. In [110], the authors discuss the use of various spectral indices, spectral classification, and morphological processing to recognize built-up areas in Yakutsk. In addition, socio-economic modeling was also performed.…”

Section: Supervisedmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…Bouzekri et al ((Bouzekri et al 2015) calculated the Built-up Area Extraction Index (BAEI) based on short-wave (red and green infrared) to extract the built-up area of the city of Djelfa (Algeria). Similarly, Gadal and Ouerghemmi (Gadal and Ouerghemmi 2019) applied it for the same purposes in Yakutsk (Russian Federation). Ettehadi Osgouei (Ettehadi Osgouei et al 2019) applied NDTI (Normalized Difference Tillage Index) for the first time in the Land Cover/Land Use (LULC) domain in some Turkish cities (Istanbul, Ankara and konya).…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of build-up areas from bare land using Sentinel 2A imagery in El Khroub city, Algeria

Tabet,

Abdaoui,

Layeb

2023

Bull. Soc. R. Sci. Liege

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In this research work, the separation of built-up areas from bare lands in El Khroub city is carried out using a supervised classification approach involving several indices and combining spectral bands of the Sentinel-2A images sensor. The multi-index approach is based on the combination of seven indices in order to discriminate between the three main categories of land cover, which are water bodies, green areas and buildings. 3First, this operation requires the use of NDVI, BAEI, NDBI, NDTI, BUI, MNDWI and the NDVIre index, which have a strong discrimination capacity between build-up area and the other land cover features. The neo-images obtained from the combination of the above indices are then classified with the Likelihood algorithm for the extraction of the six class types of land cover (built-up areas, bare land, vegetation, forest, water bodies and asphalt). The multi-index obtained from the combination of BUI, NDTI and NDVIre is the most effective; shown by the evaluation values, where the Overall accuracy is of 96.44%, the Kappa Coefficient (K) of 95.72% and a User Accuracy for built-up class of the order of 100%, with a zero rate of commission. Therefore, the multi-index (BUI, NDTI and NDVIre) is retained for build-up area extraction due to its best discrimination capability.

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Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

Cited by 12 publications

References 46 publications

A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium

A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Automatic extraction of build-up areas from bare land using Sentinel 2A imagery in El Khroub city, Algeria

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