Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM)

Rafiee, Reza; Mahiny, Abdolrassoul Salman; Khorasani, Nematollah; Darvishsefat, A A; Danekar, Afshin

doi:10.1016/j.cities.2008.11.005

Cited by 169 publications

(105 citation statements)

References 23 publications

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“…Urban population in Iran has increased from 47 % in 1976 to 61 % in 1996 (Rafiee et al 2009;Fanni 2006) and number of towns and cities in Iran has also increased significantly, from 199 towns in 1956 to 1200 in 2012 (Statistical Center of Iran). The case study in this research is Tehran Metropolis.…”

Section: Study Areamentioning

confidence: 99%

“…Iran as a developing country in Asia, is witnessing continual large-scale urbanization (Rafiee et al 2009;Hosseinali et al 2013). Urban population in Iran has increased from 47 % in 1976 to 61 % in 1996 (Rafiee et al 2009;Fanni 2006) and number of towns and cities in Iran has also increased significantly, from 199 towns in 1956 to 1200 in 2012 (Statistical Center of Iran).…”

Section: Study Areamentioning

confidence: 99%

“…Urban area is increasing faster than the urban population itself (Tewolde and Cabral 2011). With this rapid growth, cities exert a heavy pressure on lands and resources available (Leao et al 2004;Rafiee et al 2009). Urbanization is the most dramatic form of irreversible land transformation of agricultural and prolific lands to urban areas (Luck and Wu 2002), affecting landscapes and people who live in and around cities.…”

Section: Introductionmentioning

confidence: 99%

“…Using these models, policy makers and urban managers can analyze different scenarios of land cover and land use change and also, evaluate their effects. Thus, such models can be useful and unavoidable in land use policy and planning (Rafiee et al 2009;Veldkamp and Lambin 2001). In fact, urban expansion modeling is an interdisciplinary field as it involves numerous scientific areas, such as GIS, complexity theory, urban geography and remote sensing (Cheng 2003;Rui 2013).…”

Section: Introductionmentioning

confidence: 99%

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Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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“…Severe urban sprawl has been observed in all the six large cities of Iran. Several studies focused on the urban sprawl and its consequences in these metropolitan areas such as Tehran (Rafiee et al 2009;Roshan et al 2009;Shahraki et al 2012;Tayyebi et al 2011;Zanganeh 2006), Mashhad (Hosseini 2008;Hosseini et al 2010;Rafiee et al 2009), Isfahan (Mokhtari et al 2015;Soffianian et al 2010), Tabriz (Azimi 2001;Pourmohammadi &Jame Kasra 2011) andShiraz (bin Ibrahim &Sabet Sarvestani 2009;Movahed 2004;Sabet Sarvestani et al 2011). Although much of the attention is set toward the unplanned horizontal growth of the large cities in Iran, mid-sized and small-large cities are also experiencing severe urban sprawl as well.…”

Section: The Extent Of Sprawl In the Middle Eastmentioning

confidence: 99%

The Status of Urban and Suburban Sprawl in Egypt and Iran

2016

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Abstract:The circumstances of urban sprawl in the Middle Eastern cities have been basically examined; now we are aware of the existence of a crawling sprawl in the growth pattern of the region's cities. Nevertheless, the extent and the causes of this phenomenon have not yet been clearly explained. Thus, two questions are still unanswered: (1) to what extent are the Middle Eastern cities sprawled?, (2) what are the main drivers of sprawl in the Middle East? This paper brings together several evidences from international and the national languages to provide explanation to the above. The findings show that urban and suburban sprawl is an inclusive pattern seen in a wide variety of city sizes, planning concepts, times, etc. Sprawl is not limited to large metropolitan areas; mid-sized and small cities of the region are also sprawling. Furthermore, administrative and planning reasons are the strongest causes of urban sprawl in the region.Key words: urban sprawl, suburban sprawl, urbanization, urban land use, the Middle East Highlights for public administration, management and planning:• both large and mid-and small-sized cities are subject to urban sprawl in the Middle East • political/planning decisions are crucial in urban sprawl patterns

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A literature review on satellite image time series forecasting: Methods and applications for remote sensing

Lara‐Alvarez,

Flores,

Rodriguez‐Rangel

et al. 2024

WIREs Data Min & Knowl

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Satellite image time‐series are time series produced from remote sensing images; they generally correspond to features or indicators extracted from those images. With the increasing availability of remote sensing images and new methodologies to process such data, image time‐series methods have been used extensively for assessing temporal pattern detection, monitoring, classification, object detection, and feature estimation. Since the study of time series is broad, this article focuses on analyzing articles related to forecasting the value of one or more attributes of the image time‐series. The image time series forecasting (ITSF) problem appears in different disciplines; most focus on improving the quality of life by harnessing natural resources for sustainable development and minimizing the lethality of dangerous natural phenomena. Scientists tackle these problems using different tools or methods depending on the application. This review analyzes the field's leading, most recent contributions, grouping them by application area and solution methods. Our findings indicate that artificial neural networks, regression trees, support vector regression, and cellular automata are the most common methods for ITSF. Application areas address this problem as renewable energy, agriculture, and land‐use change. This study retrieved and analyzed relevant information about the recent activity of image time series forecasting, generating a reproducible list of the most pertinent articles in the field published from 2009 to 2021. To the author's best knowledge, this is the first review presenting and analyzing a reproducible list of the most relevant state‐of‐the‐art articles focusing on the applications, techniques, and research trends for ITSF.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Machine Learning Technologies > Prediction

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Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM)

Cited by 169 publications

References 23 publications

Urban sprawl assessment and modeling using landsat images and GIS

Urban sprawl assessment and modeling using landsat images and GIS

The Status of Urban and Suburban Sprawl in Egypt and Iran

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

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