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Peripheral urban sprawl configures new, extensive conurbations that transcend current administrative boundaries. Land use planning, supported by the analysis of future scenarios, is a guide to achieve sustainability in large metropolitan areas. To understand how urban sprawl is consuming natural and agricultural land, this paper analyzes land use c...
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... is an explicit model which generates a multilayer spatial and quantitative description of land use changes, including the simulation of likely future land use changes. The Dyna-CLUE methodology analyzes land use systems as complex multi-level systems at the interface of multiple social and ecological systems ( Figure 3). Thus, interactions which are more consistent with the reality of the study area are generated [24]. ...
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Urban sprawl has become a global phenomenon as an outcome of growing population and rapid urbanization. Previous studies have addressed the rising incidence of uncontrollable urban development, particularly in peri‐urban areas of cities, leading to chronic urban sprawl. The city of Guwahati, a million city in north east India, has expanded signific...
Citations
... Outlying Forman, 1995,Zhang,2000, Kob, 2000,Lambert et al., 2015, p. 835,Gielen et al., 2018, Yang et al., 2019, li et al., 2019, Salazar et al., 2020,Le Bivic & Melot, 2020,Wang et al., 2020 Burchell et al., 1998, Sierra Club, 1999:1 cited in Jaeger et al., 2010, p. 399, Sudhira & Ramachandra,2007:1, Noor et al., 2014, Seevarethnam et al., 2021 Local government/fragmentation Pendall, 1999., Downs, 1999:956, Glaeser and Kahn, 2004 which lacks planning or has weak planning, particularly occurring in proximity to peri-urban and rural lands. In total, the presented definitions in this category form the basic definition of "unplanned development and expansion process" and the complementary phrase "spillingover of urban settlements, changes of land use and transformation of rural landscapes into urban parcels". ...
... A review of the literature indicates that, generally, there are three types of definitions for urban sprawl based on the location of occurrence: infilling, edge, and outlying sprawl. These location-based definitions are 19 cases, and based on the conducted studies, the highest frequency (50%) is associated with "outlying sprawl" (Forman, 1995;Zhang, 2000;Kob, 2000;Lambert et al., 2015;Gielen et al., 2018;Yang et al., 2019;Li et al., 2019;Salazar et al., 2020;Le Bivic & Melot, 2020;Wang et al., 2020;Litynski, 2021). This indicates that most instances of urban sprawl are referring to new urban areas that have expanded from the periphery of existing urban patches. ...
Urban sprawl is one of the significant topics in urban planning and land use management, yet it remains an ambiguous and debatable concept with different meanings for individuals and various fields. Each definition tends to focus on one or a few aspects of urban sprawl, lacking an integrated and holistic perspective that covers all relevant dimensions of this phenomenon. Various categorizations of definitions have been attempted, but they come with certain shortcomings and gaps. Thus, it is necessary to establish a new classification for definitions of urban sprawl and subsequently present an integrated and holistic definition for it. Therefore, the present study examines the key approach of different disciplines in the definition of urban sprawl. It has also focused on a systematic review and qualitative coding of the definitions provided by different authors and considered different categories of definitions of urban sprawl. Its output is a six-fold category (pattern-oriented, process-oriented, nature-oriented, location-oriented, actors-oriented, and factors-oriented). This category provides a suitable field for theorists and scholars to fill the gaps in academic discourses in providing definitions. It also helps them make a clear framework for the definition of urban sprawl. Also, removing the ambiguity of the definition of this phenomenon, facilitates the possibility of providing effective policies for its management.
... En Ecuador se han desarrollo pocos estudios para el pronóstico de las coberturas y usos de suelo. Los modelos desarrollados se centran en modelar los cambios de LULC, pero asociando al efecto del desarrollo de actividades humanas como propulsores directos (Herrera-Franco et al., 2022;Salazar et al., 2020;Ulloa-Espíndola & Martín-Fernández, 2021). Estos modelos proponen asociar factores económicos, sociales y ambientales con la planificación del uso del suelo mediante el uso de SIG. ...
... Estos modelos proponen asociar factores económicos, sociales y ambientales con la planificación del uso del suelo mediante el uso de SIG. Un ejemplo es el caso de Salazar et al. (2020) que mediante el uso del modelo Dyna-CLUE simularon los límites del crecimiento urbano en Quito, que los explicaron como resultado del consumo de suelo natural y agrícola por la necesidad de viviendas de la población. Ulloa-Espíndola y Martín-Fernández (2021) para la misma área de estudio, la ciudad de Quito, utilizaron con éxito un modelo autómata celular en GIS y determinaron que la expansión urbana en Quito continuará hacia las afueras de la ciudad en forma de asentamientos irregulares y de forma regular en sectores económicamente consolidados con buen acceso vial y a servicios. ...
El empleo de sensores remotos junto a la información de los factores sociales propios de cada población permite el monitoreo del uso de los recursos naturales. El objetivo del presente estudio fue determinar el cambio y proyección a futuro de los usos y coberturas de suelo, y a la vez comprender, desde la perspectiva de los principales actores, los factores propulsores y subyacentes que impulsan estos cambios en la cuenca hidrográfica media-alta del río Mira. Para ello, se utilizaron imágenes multiespectrales, Landsat y Sentinel del año 1996, 2007 y 2018, a las cuales se las realizó un pretratamiento y tratamiento. Se efectuó una proyección de los cambios de coberturas y usos del suelo del 2018-2030 mediante el software TerrSet. Después, utilizando el método Delphi se identificaron los factores propulsores y subyacentes. Los resultados encontrados muestran que bosques y pastos presentaron una disminución sostenida, mientras que las áreas de cultivo y zonas urbanas aumentaron dentro de los periodos 1996-2018 y 2018-2030. Estos cambios se relacionan con el crecimiento urbano, agrícola, ganadero, minero y de la industria forestal; impulsados generalmente por el aumento poblacional, demanda de mercado, cambios de patrones de consumo, expansión de las carreteras e inexistencia de políticas ambientales.
... Participatory scenario development approaches offer the integration of local knowledge and divers perspectives, capacity building and increase the usability of scenarios and modeling results by stakeholders. Only two studies mention the engagement of stakeholders in the development of scenarios and for validation purposes (92,101). Three techniques of operationalizing scenarios and manipulating the different drivers of urbanization can be found in the analyzed studies (Criterion 6): The first approach is most straightforward and usually referred to as "business as usual projection". ...
Urban climate and disaster risks are set to rise, driven by the interaction of two global megatrends: urbanization and climate change. A detailed understanding of whether, where and how cities are growing within or into hazard-prone areas is an urgent prerequisite for assessing future risk trajectories, risk-informed planning, and adaptation decisions. However, this analysis has been mostly neglected to date, as most climate change and disaster risk research has focused on the assessment of future hazard trends but less on the assessment of how socio-economic changes affect future hazard exposure. Urban growth and expansion modelling provide a powerful tool, given that urban growth is a major driver of future disaster risk in cities. The paper reviews the achievements lately made in urban growth and exposure modelling and assesses how they can be applied in the context of future-oriented urban risk assessment and the planning of adaptation measures. It also analyses which methodological challenges persist in urban growth and exposure modelling and how they might be overcome. These points pertain particularly to the need to consider and integrate (1) urban morphology patterns and potential linkages to exposure as well as vulnerability, (2) long-term time horizons to consider long-term developments, (3) feedbacks between urbanization trajectories and hazard trends, (4) the integration of future urban growth drivers and adaptation responses, (5) feedbacks between adaptation and urbanization, and (6) scenarios, which are developed within a commonly defined scenario framework.
... For instance, Lou et al. [38] studied the impact of land use change scenarios on the ecosystem service value of the Yellow River Basin, and Salazar et al. [39] conducted the land use changes in the Quito metropolitan area and its related urban agglomerations by combining risk assessment and nature conservation. Regarding smaller scales, like county-level urban land use analysis [39][40][41], the focus is on studying land use suitability, efficiency, and their influencing factors at the county level. As for even smaller scales, like townships [42], the analysis is focused on land use evolution characteristics within townships. ...
Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation, and urban planning, this study first established three scenarios: economic priority (Econ. Prior.), ecological priority (Ecol. Prior.), and balanced development (BD), and then used the Multi-Objective Planning (MOP) model to calculate the optimal land use structure. Finally, it carried out land use spatial layout optimization based on the Patch-generating Land Use Simulation (PLUS) model in 2035, Caijia Cluster, Chongqing, China. This approach, known as MOP-PLUS modeling, aimed to optimize land use. Meanwhile, the applicability of the PLUS model in simulating land use changes was discussed in small-scale mountainous areas. The results show the following: (1) The “quantity + space” approach in the MOP-PLUS model demonstrated the feasibility of the PLUS model in simulating land use change in small-scale mountainous areas. The overall accuracy (OA) of land use change simulation reached 81.60%, with a Kappa value of 0.73 and a Figure of Merit (FoM) coefficient of 0.263. (2) Land use optimization: Under the Econ. Prior. scenario, economic benefits peaked at 4.06 × 1010 CNY. Urban expansion was the largest, leading to increased patch fragmentation. The Ecol. Prior. scenario yielded the highest ecological benefits, reaching 7.46 × 107 CNY. The urban development pattern exhibited inward contraction, accompanied by urban retrogression. In the BD scenario, economic benefits totaled 3.89 × 1010 CNY, and ecological benefits amounted to 7.16 × 107 CNY. Construction land tended to concentrate spatially, leading to relatively optimal land use efficiency. Therefore, based on a comprehensive consideration of the regional land use constraint policies and spatial layout, we believe that a balance point for land use demands can be found in the BD scenario. It can ensure economic growth without compromising the ecological environment.
... Among other models are: i) SECLAND, which projects 2050 climatic and socioeconomic factors in the interaction between agriculture and forest areas (Egger et al., 2022). ii) Dyna-CLUE, used to simulate urban growth limits in Quito, explains how urban sprawl consumes natural and agricultural land for housing (Salazar et al., 2020). iii) The Conversion of Land Use and its Effect at Small Regional Extent (CLUE-S), applied to cases of future changes in land use concerning agricultural activity (Lambin et al., 2000), urbanization, and the abandonment of agricultural land in European landscapes Verburg et al., 2006). ...
Some cities worldwide have oil wells, which directly affect the management of the territory. For example, La Libertad and Salinas districts contain 467 oil wells in urban areas representing a major land-use planning challenge. The objective is to apply the CLUE-S land use model in coastal cities with oil wells (Salinas-La Libertad), using geographic information systems considering environmental and security characteristics for territorial development. The stages of the study are: i) Classification and categorisation of oil wells. ii) Application of the GIS-CLUE-S method and visualisation of land use dynamics. iii) Use the SWOT-TOWS matrix, analysis of the situation and the main factors affecting the territory. The results indicate high vulnerability in many urban sectors and those close to the coastline. Furthermore, the CLUE-S analysis shows that the population growth in the urban sector is close to oil well areas, making it a complex pole of human-industry interaction that impacts the management of the territory. This study synthesises three technical aspects: some oil wells do not comply with municipal ordinance regulations; identification of vulnerable zones due to environmental and security factors, which recommends a territorial reordering policy; as well as an education plan for the application of territorial ordering policies, with awareness and sustainability projections.
... One study in Argentina focuses its analysis on the accelerated increase of urban land in the metropolitan area of Rosario (Galimberti 2021). Quito was another city selected by Salazar et al. (2020) and Serrano & Durán (2020) to identify the effects of inefficient land-use policies in urban sprawl and preservation of land consumption. There is little research that systematically studies the urban growth and expansion patterns in Colombian cities. ...
Colombia has experienced rapid urbanization over the past three decades. Most Colombian cities doubled their urban extent in the last 10 years which resulted in a high demand for land. This research identifies urban expansion patterns in five of the largest urban agglomerations in Colombia. Urban growth is analysed using satellite images (Landsat 5) and census data to assess spatiotemporal land-use changes over a 30-year period (1990–2020). The actual urban extents are compared with each city’s land-use predictions, instruments, and regulations to ascertain whether local authorities have the capabilities to predict land demand and to guide development processes at the municipal level. The predictions made by local authorities are inaccurate and often underestimate the rates of population growth and land requirements. The findings reveal that local authorities lack the capabilities to manage urban growth and deliver sustainable urban development. This is leading to poor outcomes: low planning standards, urban informality and inefficient land use. 'Policy relevance' This research reveals a significant gap between urban land-use policy instruments and actual urban growth patterns in Colombian cities. The findings reveal that the current urban expansion surpassed what local authorities had anticipated and allocated for land use. Although existing policies empower local authorities, their capabilities to effectively monitor and model population growth and associated land requirements are poor. This led to an underestimation of land requirements and poor planning outcomes. To improve future decision-making and management of urban growth, urban planners should use empirical data to inform urban growth scenarios: allocate sufficient land to allow for orderly growth, designate specific areas for development and provide for sufficient infrastructure.
... The simulation model enables the researchers in understanding more about the system, involved variables, process, and its impact as well. Currently, simulation models are widely developed and implemented for urban analysis, agricultural and forestry uses (Salazar et al. 2020). ...
... However, the CA model has no power to compute the interactions between the drivers of landuse change, whereas the Dyna-CLUE model has the advantages of as it can specify the details of landuse change scenarios through the model parameters. Since the Dyna-CLUE is based on a probabilistic logistic regression method, which analyzes landuse systems as a complex multi-level system at the interface of multiple social and ecological systems (Salazar et al., 2020), it can simulate better the landuse change under designed-scenarios by taking into consideration of driving forces, spatial policies and restriction zone (Wang et al. 2019). ...
Rice is the staple food and its cultivation requires a specific land condition. The population growth, urbanization, and plantation expansion together with socio-economic development are the driving factors of the riceland decline in Deli Serdang Regency of North Sumatera, Indonesia. As a consequence, likely availability and sustainability of rice production are threatened. Hence, it is important to understand how the future landuse and population change will affect the riceland area and production. In the lack of spatially simulated information for the future which could be useful in planning the riceland areas, the study objectives were to project the landuse change by 2040 under three scenarios, Business as Usual (BAU), Potential Riceland Protection (PRP) and Conservation Oriented (CO), and to investigate the impact of consumption demand on the sustainability of rice production. Landsat satellite data of 2009 and 2018, several spatial GIS data, and survey data were analyzed in ArcGIS, Dyna-CLUE, and SPSS software to generate the landuse classification and to simulate the future landuses; while the population projection by 2040 was derived from a Geometric Model. The results showed that forest and riceland areas will decrease with the continuous increase of plantation and urban areas under BAU scenario, but could be protected and increased under PRP scenario. The sustainability of rice production depends not only on the total riceland area, but also the productivity, the population growth, the consumption rate, and the policy. The simulated results of three scenarios serve as an important input to planning for protecting the riceland areas and thus sustained rice production in Deli Serdang Regency.
... The first step is to identify the explanatory variables of the land use change model. For this purpose, 19 variables have been proposed which have been used in similar studies (Arsanjani et al., 2013;Henríquez-Dole et al., 2018;Poelmans & Van Rompaey, 2010;Salazar et al., 2020). The second step entails applying Cramer's V test to obtain the statistical significance of dependent variables (land use/cover) and exploratory variables (driving forces). ...
... Internationally, studies that have applied the CLUE model have shown that by simulating and projecting changes in land use/cover, incorporating both socio-economic and bio-geographic variables, it is possible to estimate diverse scenarios successfully. For example, business-asusual scenarios, regulated scenarios, strategic planning scenarios, among others (Aduah and Mantey, 2020;Henríquez-Dole et al., 2018;Kang et al., 2019;Luo et al., 2010;Pokojska, 2019;Price et al., 2015;Salazar et al., 2020;Xu et al., 2013). These cases demonstrate the importance of comparing scenarios with different degrees of urbanization and planned scenarios (different objectives) for better decision-making. ...
Latin America’s intensive urbanization processes are triggering rapid peri-urban transformations and the expansion of cities. These include accelerated metropolization processes, urban sprawl, and the emergence of new conurbations. These changes parallel the expansion of highly profitable agricultural activities and plantations linked to international markets. This paper aims to analyze land use/cover changes between 1990 and 2050 in the Quillota Province, Valparaíso Region, Chile. Specific objectives considered (1) analyzing changes in land use/cover trajectories between 1990 and 2017, (2) simulating changes in land use/cover based on three scenarios of territorial planning to 2050 (trending, ecological planning, and spatial planning), and (3), identifying the areas most likely to be modified by urbanization and agricultural activity as a result of biodiversity loss in the study area. The Dyna-CLUE model implemented was complemented with GIS techniques for the analysis of land use/cover trajectories that allowed classifying and characterizing the most dynamic land uses/cover within the Quillota Province, such as urban land uses. The results of simulations to 2050 show a probable conurbation of medium-sized cities of Quillota-La Cruz-Calera, and future land use conflicts between peri urban-agricultural land use and plantation-natural conservation land use. The results suggest that it is essential to choose scenarios to ensure sustainable land use planning to control urban and peri-urban sprawl and protect areas of high natural value.
... Further impetus to the development of CLUE was given by later editions created by Peter Verburg in collaboration with colleagues at Wageningen University (Verburg et al. [24], de Koning et al. [25]) and as CLUE-S by Verburg et al. [26] and Verburg and Overmars [27]. It has been applied to the analysis of land use/cover changes in many countries, such as the Netherlands (Verburg and Overmars [27]), Malaysia (Verburg et al. [26], Verburg and Overmars [27]), and Ecuador (de Koning et al. [28]), and as DYNA-CLUE by Salazar et al. [29], the Philippines (Verburg et al. [26,30]), Costa Rica (Veldkamp and Fresco [16], Schoorl et al. [31]), Indonesia (Verburg et al. [32]), China (Verburg et al. [33]), and Greece (Chouvardas [34]). The CLUE modeling framework is spatially clear and is used to analyze land use/cover dynamics at various spatial scales. ...
Investment in biology-based technological innovations is a key requirement for the development of modern agriculture/forestry. The expansion of innovative biological technologies includes changes in crops/cultivations, such as the transition from intensive monocultures to multiple crops of lower agrochemical inputs with the integration of woody trees/shrubs or animals, represented by Agroforestry. This innovative biological technology is further promoted at the European Union (EU) level by powerful institutions such as the Green Deal and the new CAP, mainly by tools such as ecoschemes and agri-environmental and climate measures (AECMs). The use of integrated regional spatiotemporal models, such as CLUE-S, to predict land use changes in the framework of Agroforestry is rather restricted. This paper examines Agroforestry as a vehicle that can contribute to achieving the rural development of the region of Thessaly, Greece. It sets a time horizon for reviewing the changes that are expected in the most important units of land uses of the rural landscape of the municipality of Mouzaki, western Thessaly plain, in the year 2040, which serves as model land for the region of Thessaly. It examines these changes with the effect of three (3) socio-economic scenarios: (a) a linear operating scenario (business as usual, BAU), (b) an ecological land protection (ELP) scenario, and (c) a rapid economic development (RED) scenario. These scenarios were introduced in the non-spatial module of the CLUE-S spatiotemporal model, while in the spatial module sixteen (16) characteristic landscape parameters were introduced as independent variables. The most important land use units, including traditional silvoarable and silvopastoral woodland systems, were the dependent variables. The simulations of the changes of the land use units showed that under the RED scenario, in the year 2040 the extent of the silvoarable systems is expected to increase significantly (57%) compared to the reference year of 2020, while the rest of the land use units under the other scenarios are mainly regulated by depopulation/abandonment of the rural areas and the processes of natural succession. The fact that the extent of silvoarable systems is increasing, in combination with the favorable institutional environment created by European rural policies, gives impetus to regional rural development through investments in the agricultural sector and mainly in Agroforestry systems.
... Process of Path Optimization Method. For route selection design of AI circuit, besides quantifying geographical elements that can influence route selection, comprehensive action index of each element should also be calculated [14,[22][23]. Therefore, the following optimization of AI transmission cable path can only be performed by discovering 2 Mobile Information Systems the appropriate method to get the geographical information model of the smart city. ...
With the development of smart cities, it is particularly important to realize scientific and reasonable planning of transmission lines as the backbone of the power transmission system. Through the geographic information system (GIS) platform, this paper obtains the geographic information of the planning area. Then, through the typical design cost of the 110 kV transmission line subvolume of the typical cost of the power transmission and transformation project of the State Grid Corporation and the local documents of the smart city planning area, this paper obtains the estimated cost of each grid after gridding in the smart city area. After that, the research uses the ant colony algorithm to search for the optimal transmission line route in the smart city. Finally, the achievements of primary economic and technical indexes are evaluated in transmission line planning of smart city.
