Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Aina, Yusuf A.; Adam, Elhadi; Ahmed, Fethi; Wafer, Alex; Alshuwaikhat, Habib M.

doi:10.1080/22797254.2019.1691469

Cited by 15 publications

(10 citation statements)

References 57 publications

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“…Vegetation is frequently present in large uniform patches and is, therefore, not as influenced by observation unit size as gravel and water which both commonly occur in narrow strips. The higher accuracy of soft classification compared to the hard classification is in line with existing literature (Aina et al, 2019). Contrary to previous studies (Dennison et al, 2004), we are not seeing a large negative effect of topographic shadow on SSMA accuracy.…”

Section: Suitability Of a Soft Classificationsupporting

confidence: 91%

Fluvial gravel bar mapping with spectral signal mixture analysis

Stančič

Oštir

Kokalj

2020

European Journal of Remote Sensing

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The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in northwest Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.

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Section: Suitability Of a Soft Classificationsupporting

confidence: 91%

Fluvial gravel bar mapping with spectral signal mixture analysis

Stančič

Oštir

Kokalj

2020

European Journal of Remote Sensing

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“…Dereli (2018) revealed urban land use change in Istanbul, Turkey, from 2003 to 2016, by using multi-layer perceptron artificial neural network. Aina et al (2019) compared spectral angle mapping, spectral mixture analysis and band ratioing for land-cover classification in Riyadh Saudi Arabia, from 1972 to 2014.…”

Section: Literature Reviewmentioning

confidence: 99%

Urban growth sustainability of Islamabad, Pakistan, over the last 3 decades: a perspective based on object-based backdating change detection

Liu

din

Jiang³

2020

GeoJournal

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Urban growth copes with problems in sustainable development. In developing countries, particularly, sustainable development of urban growth copes with severe challenges with respect to sluggish economic and social growth, population boom, environmental deterioration, unemployment, slums and so on. Time series of remote sensing data provide critical support on sustainability assessment. However, the urban spatial extend cannot be accurately extracted from land cover data. Targeting the urban growth and its sustainability in Islamabad, the capital of Pakistan, this study extracts urban area from four periods of Landsat images between 1990 and 2018 using an innovative object-based backdating change detection method and two criteria for extracting urban land from impervious surface. We prove that impervious surface cover and urban area increased 273.10% and 426.21%, respectively, over the last 3 decades. We identify five factors playing important role in urban growth: population, transportation systems, master planning, industrial and real estate development, and neighbor urban effect. In this study, we assess the socioeconomic sustainability associated with slum growth and census data, and the environmental sustainability in relation to the variations of normalized difference vegetation index (NDVI) in forest areas. We found that slums increased with the corresponding growth of urban area and population, reflecting sluggish economic increase in Islamabad. We found that the area of woodland increased 9.29%, but its NDVI decreased from 0.668 to 0.551, implying a deteriorative trend of environmental condition. Keywords Land cover Á Impervious surface Á Change detection Á Urban growth Á Islamabad Á Sustainability Y. Liu (&) Á Shaker ul din

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“…During the last few decades, the city has experienced significant population growth from 80,000 in 1952 to 6,700,000 in 2015 [21,25]. Several studies have examined changes in land cover/use (LCLU) in Riyadh City (e.g., [26][27][28][29]) and a few have looked at future expansion (e.g., [25,30]). According to these studies, the urban area has substantially increased over the last few decades, with a higher likelihood of significant future expansions.…”

Section: Study Areamentioning

confidence: 99%

Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

2021

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Using the local climate zone (LCZ) framework and multiple Earth observation input features, an LCZ classification was developed and established for Riyadh City in 2017. Four land-cover-type and four urban-type LCZs were identified in the city with an overall accuracy of 87%. The bare soil/sand (LCZ-F) class was found to be the largest LCZ class, which was within the nature of arid climate cities. Other land-cover LCZs had a lower coverage percentage (each class with <7%). The compact low-rise (LCZ-3) class was the largest urban type, as urban development in arid climate cities tends to extend horizontally. The daytime surface thermal characteristics of the developed LCZs were analyzed at seasonal timescales using land surface temperature (LST) estimated from multiple Landsat 8 satellite images (June 2017–May 2018). The highest daytime mean LST was found over large low-rise (LCZ-8) class areas throughout the year. This class was the only urban-type LCZ class that demonstrated a positive LST departure from the overall mean LST across seasons. Other urban-type LCZ classes showed lower LSTs and negative deviations from the overall mean LSTs. The overall thermal results suggested the presence of the surface urban heat island sink phenomenon as urban areas experienced lower LSTs than their surroundings. Thermal results demonstrated that the magnitudes of LST differences among LCZs were considerably dependent on the way the region of interest/analysis was defined. This was related to the types of LCZ classes presented in the study area and the spatial distribution and abundance of these LCZ classes. The developed LCZ classification and thermal results have several potential applications in different areas including planning and urban design strategies and urban health-related studies.

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Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Cited by 15 publications

References 57 publications

Fluvial gravel bar mapping with spectral signal mixture analysis

Fluvial gravel bar mapping with spectral signal mixture analysis

Urban growth sustainability of Islamabad, Pakistan, over the last 3 decades: a perspective based on object-based backdating change detection

Local Climate Zones and Thermal Characteristics in Riyadh City, Saudi Arabia

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