Urban Sprawl Analysis and Modeling in Asmara, Eritrea

Tewolde, Mussie G.; Cabral, Pedro

doi:10.3390/rs3102148

Cited by 158 publications

(102 citation statements)

References 30 publications

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“…The acquired data of the study area were processed and analyzed using GIS and Remote sensing techniques to obtain information for environmental and urban growth monitoring [20].…”

Section: Land Change Modeler and Logistic Regressionmentioning

confidence: 99%

“…In this study, in order to detect, quantify and analyze the changes, post classification change analyses with ArcMap and 'Land Change Modeler' in IDRISI®Selva have been employed [20], LCM module provides quantitative assessment of category-wise land use changes in terms of gains and losses with respect to each land use class [21].…”

Section: Land Change Modeler and Logistic Regressionmentioning

confidence: 99%

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Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Hamdy¹,

Zhao²,

Salheen³

et al. 2017

IJET

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Abstract-Urban growth is very dynamic and complex processes, It has many factors affects on the directions and value of urban extensions, the factors that drive urban development must be identified and analyzed, The study of relative research shows that the driving forces which lead and redirect the urban sprawl, and try to classified these driving forces based on the value of its effectives on urban growth, Achieving the classification of driving forces needs using statistical method, our study used the logistic regressions to analysis and classes the driving forces for urban sprawl, Identifying the driving forces is the most important step to prediction the urban growth in future by using the cellular automata models so that the research try to prepare this step to complete the procedure to expect the urban extensions, This study takes Aswan area as a case study in period from 2001 to 2013 by analysis the official detailed plan and google earth historical imagery, Almost data prepared to logistic regressions analysis using ArcGIS software and IDRISI®Selva. We studied historical imagery of the area using Google Earth to examine changes in urban growth in 2001 and 2013, over a span of 12 years. The results showed urbanization in risk areas to be 59.79 % in 2001, then rising to 65.45 % in 2013, by the end of this paper it can be classified the effect of the driving forces in study area.

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“…The acquired data of the study area were processed and analyzed using GIS and Remote sensing techniques to obtain information for environmental and urban growth monitoring [20].…”

Section: Land Change Modeler and Logistic Regressionmentioning

confidence: 99%

Section: Land Change Modeler and Logistic Regressionmentioning

confidence: 99%

Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Hamdy¹,

Zhao²,

Salheen³

et al. 2017

IJET

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show abstract

“…In the comparison, the kappa value was calculated as 0,82. The obtained accuracy was assessed based on literature surveys [32,35,36] and the resolution and classification errors of satellite images used in the analyses and it was thought that the model accuracy was sufficient. Thus, the simulation performed for 2034 and the forecasts arising as the result of this simulation might provide important information for the future.…”

Section: Resultsmentioning

confidence: 99%

Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method

Öztürk

2017

Teh. vjesn.

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Original scientific paper The evolution of cities over a specific time period and the determination of relevant trends are important to simulate for the proper development and planning of a city. In particular, coastal areas experience intense pressure from developers with respect to settlements, tourism, trade and industry, and built-up areas are being observed near coasts. The inability to direct development and control growth is destroying natural resources in coastal areas at a rapid pace. Thus, determining these trends is the key component for ensuring the protection of natural resources and planned growth. Because a complete estimation of urban expansion is not possible, likely changes can be determined using simulations. In this study, a 30-year urban expansion simulation was obtained using land use/land cover (LU/LC) data for 1987 and 2004 at Samsun (Turkey) coastal areas and using the Cellular AutomataMarkov Chain (CA-Markov) method. To verify the method, urban expansion simulation for the year 2014 was compared with real LU/LC data for the same year, and the kappa value was found to be 0,82. To determine LU/LC, Landsat TM/ETM+/OLI satellite images were used, and the analyses were realised in a geographic information system (GIS) environment. As a result of the study, the CA-Markov Chain approach integrated with GIS and remote sensing was shown to be effective in the study of urban growth dynamics. Using the simulation for the year 2034, probable urban expansion in the 2014-2034 period was estimated as approximately 3683 ha and the probable destructions of absolute agricultural lands, forests and pastures were predicted as approximately 968 ha, 228 ha and 24 ha, respectively. Keywords: cellular automata; GIS; Markov Chain; remote sensing; urban expansion Modeliranje prostornih promjena u priobalnim područjima Samsuna (Turska) primjenom metode Stanični automati-Markovljev lanacIzvorni znanstveni članak Promjene gradova kroz određeni vremenski period i određivanje relevantnih trendova važni su za simuliranje odgovarajućeg razvoja i planiranja grada. U priobalnim područjima naročito dolazi do izražaja intenzivni pritisak za izgradnjom, razvojem turizma, trgovine i industrije od strane velepoduzetnika, institucija i poduzeća koja se bave izgradnjom. Zbog nesposobnosti usmjeravanja razvoja i jačanja kontrole ubrzano dolazi do uništavanja prirodnih resursa u priobalnim područjima. Stoga je određivanje tih trendova ključno za osiguranje zaštite prirodnih resursa i planiranog razvoja. Budući da nije moguća potpuna procjena urbanog širenja, vjerojatne se promjene mogu odrediti simulacijama. U ovom se radu simulacija 30-godišnjeg urbanog širenja (2004-2034) dobila prema podacima o korištenju zemljišta/izgrađenosti zemljišta -land use/land cover (LU/LC) iz 1987 i 2004 za Samsun, priobalna područja Turske, uz primjenu metode Stanični automati-Markovljev lanac (CA-Markov). Kako bi se provjerila ta metoda, simulacija urbanog širenja za 2014. uspoređena je sa stvarnim LU/LC podacima za istu godinu te je dobivena kapp...

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“…Based on current growth rate, it is estimated that over 60 % of global population will be found in urban areas by 2030 (Moeller and Blaschke 2006;Odindi and Mhangara 2012). 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).…”

Section: Introductionmentioning

confidence: 99%

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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Urban Sprawl Analysis and Modeling in Asmara, Eritrea

Cited by 158 publications

References 30 publications

Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh - Aswan as a Case Study

Modelling spatial changes in coastal areas of Samsun (Turkey) using a Cellular Automata-Markov Chain method

Urban sprawl assessment and modeling using landsat images and GIS

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