Urban Sprawl Evaluation Using Landscape Metrics and Black-and-White Hypothesis (Case Study: Urmia City)

Abedini, Asghar; Khalili, Amin; Asadi, Navid

doi:10.1007/s12524-020-01132-5

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…Landscape metrics offer a wide range of options to evaluate the landscape structure, from agricultural areas to mining/quarry sites, from wetlands to forests. The critical issue here is that the expert who makes the evaluation prefers the metric set that will best reveal the spatial arrangement of the landscape structure, depending on the subject investigated [20,67,[70][71][72][73][74][75][76][77][78][79][80].…”

Section: Pattern Analysismentioning

confidence: 99%

“…Accordingly, the loss of or change in species can be hypothesized, especially in the woodland areas of the Istanbul landscape, which has significant ecological importance. The difference in ENN_AM between patches was considered together with NP and LPI, providing important information about the urban pattern [76]. At the general landscape level, ENN_AM showed that the distance between similar patches had increased.…”

Section: Low-resolution Landscape Characterizationmentioning

confidence: 99%

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Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

et al. 2022

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With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.

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Section: Pattern Analysismentioning

confidence: 99%

Section: Low-resolution Landscape Characterizationmentioning

confidence: 99%

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

et al. 2022

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“…The Urmia city (37 ο 33΄Ν, 45ο04΄Ε) is the center of the West Azerbaijan province in the north west of Iran, which is located at a distance of 18 km from Lake Urmia (Khaledi et al, 2021). The climate of this region is cold, semi-arid with an average of 360 mm annual pre-cipitation and 11 ° C annual temperature (Chitsazan et al, 2019), making it one of the coldest cities in Iran (Abedini et al, 2020). Urmia is one of the most important historical and growing metropolises of Iran that has grown a lot in recent years (Abedini & Khalili, 2019).…”

Section: Study Areamentioning

confidence: 99%

Simulation of Urban Sprawl by Comparison Cellular Automata-Markov and ANN

Asadi¹,

Oshnooei-Nooshabadi²,

Saleh³

et al. 2022

Preprint

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A correctly obtained Land-use/land-cover (LULC) prediction map is essential to under-standing and assessing future patterns. In the study, the LULC map of Urmia/Iran in 2030 was produced using two different prediction methods CA-Markov and Artificial Neural Network (ANN). In general, the study followed a methodology consisting of three steps. In the first steps, Landsat satellite images acquired in 2000, 2010 and 2020 were classified with maximum likelihood algorithm and LULC maps were prepared for each year. In the second stage, to investigate the LULC prediction methods' validation (CA-Markov and ANN) the LULC prediction map of 2020 was produced using the LULC map of 2000 and 2010; In this step, the predicted LULC map of 2020 and the actual LULC map of 2020 were evaluated by correctness, completeness and quality indexes. Finally, The LULC map for 2030 was prepared using all two algorithms and the change map was extracted. The results show that the area of soil and vegetation decreased, and built-up regions increased during the research period. The methods validation results show that the two algorithms are much closer to each other. Nevertheless, in general, ANN has the highest completeness (96.21%) and quality (93.8%) and CA-Markov the most correctness (96.47). This study shows that the CA-Markov algorithm is most successful in predicting the future that had larger areas and a higher percentage in the region (urban and vegetation cover) and the ANN algorithm in predicting phenomena that had smaller levels with fewer percentages (soil and rock).

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“…Urbanization in tier-II cities across the world is becoming proportionately equal as compared to some of the leading metropolitan cities (Ramachandra., et al 2011;Dhanaraj., et al 2022), as a result of people's increased choice of housing in the urban fringes due to the extensive developments in the availability of infrastructure, telecommunication, transport networks and cheap land value (Shaw., 1999). Unmanaged urban growth leads to improper land use patterns (Liu., et al 2011;Satir., et al 2016); which proceeds to the fragmentation of the urban landscape and its neighbourhood areas; landscape fragmentation often results in disruptions to the urban centres (Jaeger., et al 2000;Abedini., et al 2020). Much like fragmentation, urban sprawl is another form of urban growth resulting from improper planning that decays the surrounding environment (Fang., et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Analysing spatial and geometrical patterns of urbanization using spatial metrics – A case study of Tiruchirappalli urban, India

Prakash

Jegankumar

Libina

2023

Preprint

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The understanding of spatial and geometrical patterns in urbanization is crucial in tackling its associated problems. As urbanization progresses through various stages of development, it reflects different forms, patterns, and interactions based on physical and functional aspects. Spatial metrics, a well-regarded technique for quantifying urban characteristics, is one of several methods developed to achieve this. The current study was conducted in Tiruchirappalli and six other urban centres located within a 40-kilometre radius; which helps to comprehend the comparative growth and spatial patterns in relation to Tiruchirappalli urban centre. The urban centres were divided into eight zones based on direction for more precise quantification. The study employed Landsat 5 and 8 satellite images to classify land use/cover for the periods 1996, 2008, and 2020, which were then used to assess urban spatial growth and quantify spatial and geometrical patterns. Fragstat, a comprehensive application with several metrics, was used to analyse spatial metrics, including patch, class, and landscape levels, which focus on spatial configurations such as area and edge, aggregation, diversity, and shape. The study generated significant data about spatial and geometric properties, patterns, and obstructions of growth. The area and edge metrics and aggregation metrics indicated that Manachanallur, Manapparai, Musiri, Thiruverumbur, and Thuraiyur had the most unpredictable, random growth with highly protrusive urban growth in few zones across the study period. Diversity metrics demonstrated that Tiruchirappalli had the most abundant urban patches, with few patches from other classes. Shape metrics revealed significant improvements from 2008–2020 across all urban centres, whereas from 1996–2008, urban patches were more irregular in shape. The current study is a significant contribution to the urban research in understanding the relative spatial and geometric patterns of urbanization. The knowledge generated could aid in sustainable urban development planning and focus.

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Urban Sprawl Evaluation Using Landscape Metrics and Black-and-White Hypothesis (Case Study: Urmia City)

Cited by 15 publications

References 15 publications

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

Simulation of Urban Sprawl by Comparison Cellular Automata-Markov and ANN

Analysing spatial and geometrical patterns of urbanization using spatial metrics – A case study of Tiruchirappalli urban, India

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