Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion

Valdiviezo-N., Juan C.; Téllez-Quiñones, Alejandro; Salazar-Garibay, A.; López‐Caloca, Alejandra A.

doi:10.1364/josaa.35.000035

Cited by 50 publications

(29 citation statements)

References 35 publications

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“…NDVI is common in remote sensing literature and is often used for vegetation detection and change analyses [25,26]. The second index tested was the Normalized Built-up Area Index (NBAI), an index which associates higher values to impervious surfaces (including buildings) and lower values to water, soil, and vegetation [27].…”

Section: Spectral Datamentioning

confidence: 99%

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Shandas

Voelkel

Williams

et al. 2019

Climate

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The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

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Section: Spectral Datamentioning

confidence: 99%

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Shandas

Voelkel

Williams

et al. 2019

Climate

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“…NDBI is limited by the semiautomatic approach which depend on training samples [62]. The performance of NBI and NBAI are strongly affected by drier month images [63] where there is difficulty to differentiate between bare land and urban areas. Scenes with some pixel values of zero, end up giving blank or plain result especially with most normalized equations or index algorithms.…”

Section: Established Satellite Isa Extraction Indexesmentioning

confidence: 99%

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

et al. 2020

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Decentralization problems in Africa have caused some infrastructure disparity between country capitals and distant districts. In Ghana, less public investment has created a gap between implementation results and theoretical benefits. Spectral indices are a good approach to extracting impervious surfaces, which is a good method of measuring urbanization. These are restricted by complexity, sensor limitation, threshold values, and high computational time. In this study, we measure the urbanization dynamics of Wa District in Ghana by applying a proposed method of impervious surface extraction index (ISEI), to evaluate the decentralization policy using Landsat images from 1984–2018 and a single S2A data. Comparing our proposed method with five other existing indexes, ISEI provided good discriminated results between target feature and background, with pixel values ranging between 0 and +1. Other indexes produced negative values. ISEI accuracy varied from 84.62–94.00% while existing indexes varied from 73.85–90.00%. Our results also showed increased impervious surface areas of 83.26 km2, which is about 7.72% of total area while the average annual urban growth was recorded as 4.42%. These figures proved that the quantification of decentralization is very positive. The study provides a foundation for urban environment research in the context of decentralization policy.

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“…Built-up areas yield a higher reflectance response in the SWIR than in other bands [52], and it may help in alleviating the problem of confusion between built-up areas and other types of land cover, such as artificial open spaces, river gravel, and sand dunes [53]. As Sentinel-2 has two SWIR bands, it is, therefore, inherently advantageous when applied to built-up areas extraction.…”

Section: Optimal Band Selectionmentioning

confidence: 99%

An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

2018

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Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.

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Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion

Cited by 50 publications

References 35 publications

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

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