A multi-scenario forecast of urban change: A study on urban growth in the Algarve

Vaz, Eric; Nijkamp, Peter; Paìnho, Marco; Caetano, Mário

doi:10.1016/j.landurbplan.2011.10.007

Cited by 135 publications

(64 citation statements)

References 32 publications

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“…In contrast, LULCC is one of the most important variables that decide most of the resource planning and control measures (Weng 2007), as well as pollution of many natural resources such as water and soil (Grimm et al 2008;Srivastava et al 2012a). Several landscape pattern scenarios, considering changing environmental conditions for, e.g., climate change, land use change, establishing new road networks etc., have also been found responsible for urbanization by several researchers (Lambin et al 2001;Satterthwaite 2009;Csorba and Szabó 2012;Srivastava et al 2012c;Vaz et al 2012;.…”

Section: Introductionmentioning

confidence: 89%

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

et al. 2015

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Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multitemporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

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Section: Introductionmentioning

confidence: 89%

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

et al. 2015

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“…In order to alleviate these problems, many scholars have carried out numerous studies, which cover the following aspects: urban spatial extension and sprawl [6][7][8][9][10][11], industrial spatial layout [12,13], urban land use changes [14][15][16][17], and traffic organization [18,19], all of which has provided an important theoretical support for the study of UCLE. …”

Section: Introductionmentioning

confidence: 99%

Monitoring the Different Types of Urban Construction Land Expansion (UCLE) in China’s Port City: A Case Study of Ningbo’s Central City

et al. 2017

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Abstract:The internal structure of urban construction land expansion (UCLE) is closely related to urban sustainability, and the UCLE in China's port city has a unique pattern. This study attempted to monitor the internal structure evolution of UCLE based on construction land transfer data in Ningbo's central city from 2002 to 2015. Equality fan analysis and kernel density analysis were applied to characterize the spatial-temporal dynamics of different types of UCLE and to summarize their evolution patterns. The results revealed that in the whole study area, the rank of the expansion size and intensity of construction land was: industrial land > traffic land > residential land > public land > commercial land. The dominant directions of all types of UCLE were the directions of the port zones. In the port zones, there were more apparent expansion trends and higher expansion intensities of industrial, traffic and public lands, which meant that the port had significant impact on UCLE, especially in terms of industrial land. Additionally, commercial and residential land expansions showed a circle-shaped gradient pattern; industrial land sprawl was in a leapfrog pattern; public and traffic land sprawl was illustrated in a bi-centric pattern. In the future, local government should encourage the development of residential and public land rather than industrial land, and shift the present industry structure which dominated by industry to a more balanced structure, in order to achieve sustainable development.

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“…Among the dynamic models, CA models, originally proposed by Ulam and Von Neumann in the 1940s, are among the most remarkable urban growth models because of their flexibility, simplicity in application, and their close ties to remote sensing data and geographic information systems (GIS) [13,[22][23][24]. Since Couclelis [25,26] provided the theoretical relationship between geographical CA and urban land use change dynamics, a variety of CA models such as SLEUTH [27], the dynamic urban evolution model (DUEM) [28], the multi criteria evaluation (MCE)-CA model [29], the multi-agent system (MAS)-CA model [30], the GeoCA-Urban model [31], the Voronoi-CA model [32] and the Markov-CA model [33] have been developed to simulate urban land use change and urban expansion. Major achievements in various aspects of urban CA modelling have been discussed extensively [27,[34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China

et al. 2014

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China's southeast coastal areas have witnessed rapid growth in the last two decades, owing mostly to their economic and social attractions. In this paper, we chose Jimei, a coastal peri-urban district of Xiamen city on the southeast coast of China, as a study area to explore the district's growth dynamics, to predict future sprawl during the next decade and to provide a basis for urban planning. The SLEUTH urban growth model was calibrated against historical data derived from a series of Landsat TM 5 satellite images taken between 1992 and 2007. A Lee-Sallee value of 0.48 was calculated for the district, which is a satisfactory result compared with related studies. Five coefficients of urban growth, diffusion, spread, breed, slope resistance and road gravity had values of 25, 68, 86, 24 and 23, respectively, in 2007. The growth coefficients (i.e., urban character) can capture urban growth characteristics in Jimei district. The urban DNA revealed that, over the study period, urban growth in the district was dominated both by urbanization through establishment of new urban centers, and by expansion outward from existing urban centers. In contrast to interior cities, in which expansions are dramatically shaped by actual road patterns, urban expansion in the district was likely constrained by the nearby coastline. Future urban growth patterns were predicted to 2020 assuming three different development scenarios. The first scenario simulated a continuation of historical urban growth without changing current conditions. The second scenario projected managed growth in which OPEN ACCESS Sustainability 2014, 6 3900 urban growth is limited by a layer with areas excluded from urbanization, which is the future development plan for Jimei district and Xiamen city. The third scenario depicted a growth with maximum protection in which growth was allowed to continue, similar to the second scenario, but with lower diffusion and spread coefficients applied to the growth pattern. The third scenario demonstrated that valuable land could be saved, which is the most desirable outcome for Jimei urban development. The study showed that SLEUTH can be an extremely useful tool for coastal city managers to explore the likely outcomes of their city development plans.

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A multi-scenario forecast of urban change: A study on urban growth in the Algarve

Cited by 135 publications

References 32 publications

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Monitoring the Different Types of Urban Construction Land Expansion (UCLE) in China’s Port City: A Case Study of Ningbo’s Central City

Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China

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