Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries

Huang, Deng-Shing; Huang, Jing; Liu, Tao

doi:10.1016/j.landusepol.2018.12.028

Cited by 145 publications

(64 citation statements)

References 46 publications

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“…As prime agricultural land can be strictly protected by agricultural land protection policies, the identification of reclaimed construction land as prime agricultural land is suggested as one of the key issues in spatial regulation when aiming to guarantee the agricultural use of reclaimed construction land. The government should also accelerate the drawing of the redline of urban sprawl-the urban growth boundary-and control the occupation of agricultural land and forest by construction land outside the line [76]. This, together with the construction and improvement of urban storm drainage systems, can contribute to the integrated and sustainable management of water and a reduction in flood risk in Ningbo city.…”

Section: Policy Implications For Spatial Regulations In Ningbo Citymentioning

confidence: 99%

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

et al. 2019

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Coastal cities have been experiencing tremendous land use changes worldwide. Studies on the consequences of land use change in coastal cities have provided helpful information for spatial regulations and have attracted increased attention. Changes in forests and water bodies, however, have rarely been investigated, challenging the formation of a holistic pattern of land use change. In this study, we selected Ningbo, China, as a case study area and analyzed its land use change from 1990 to 2016. Random forest (RF) classification was employed to derive land use information from Landsat images. Transition matrices and a distribution index (DI) were applied to identify the major types of land use transitions and their spatial variations by site-specific attributes. The results showed that the entire time period could be divided into two stages, based on the manifestations of land use change in Ningbo: 1990–2005 and 2005–2016. During 1990–2005, construction land expanded rapidly, mainly through the occupation of agricultural land and forest, while during 2005–2016, the main change trajectory turned out to be a small net change in construction land and a net increase in agricultural land sourced from construction land, forests, and water bodies. In terms of land use change by site-specific attributes, the rapid expansion of construction land around the municipal city center during 1990–2005 was restrained, and similar amounts of land conversion between construction and agricultural use occurred during 2005–2016. During the study period, areas undergoing land use change also showed trends of moving outward from the municipal city center and the county centers located adjacent to roads and the coastline and of moving up to hilly areas with steeper slopes and higher elevations. Protecting reclaimed agricultural land, improving the efficiency of construction land, and controlling forest conversion in hilly areas are suggested as spatial regulations in Ningbo city.

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Section: Policy Implications For Spatial Regulations In Ningbo Citymentioning

confidence: 99%

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

et al. 2019

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“…From 2000 to 2015, the expansion area and rate of each region were ranked from high to low as follows: eastern region, western region, central region, and northeastern region. The extended area in the eastern region was 8356.14 km 2 , and the expanded area in the northeastern region was 3563.97 km 2 . From 2000 to 2015, the analysis of different economic zones showed that the expansion intensity of each region was ranked from high to low as follows: western region, eastern region, northeastern region, and central region.…”

Section: Analysis Of Urban Expansion Characteristicsmentioning

confidence: 99%

“…The total area of Chinese cities was 16,012.84 km 2 After abandoning the unreliable tail of the ranks [61], the results in Figure 7 show that the model fitting coefficient R 2 of each period is above 0.97, which indicates that the model fitting degree is better. Moreover, the order-scale method can better describe the distribution of Chinese city size.…”

Section: Analysis Of City Size Distributionmentioning

confidence: 99%

“…In particular, China's urbanization entered a new stage from 2000 onwards. The 19th National Congress of the Communist Party of China, the 13th Five-Year Plan, the National New Urbanization Plan (2014-2020), and the Central City Work Conference (2015) proposed to rationally control urban development boundaries, protect basic farmlands, and optimize cities [2,3]. The internal space structure promotes compact and intensive urban development and efficient green development to solve the blind expansion of cities, faster land urbanization than population urbanization, extensive and inefficient land construction, urbanization and industrialization mismatch, urban spatial distribution, and unreasonable scale structures [4].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Pattern Analysis of China’s Cities Based on High-Resolution Imagery from 2000 to 2015

Zhang

Ning

Shao

et al. 2019

IJGI

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The urbanization level in China has increased rapidly since beginning of the 21st century, and the monitoring and analysis of urban expansion has become a popular topic in geoscience applications. However, problems, such as inconsistent concepts and extraction standards, low precision, and poor comparability, existing in urban monitoring may lead to wrong conclusions. This study selects 337 cities at the prefecture level and above in China as research subjects and uses high-resolution images and geographic information data in a semi-automatic extraction method to identify urban areas in 2000, 2005, 2010, and 2015. City size distribution patterns, urban expansion regional characteristics, and expansion types are analyzed. Results show that Chinese cities maintained a high-speed growth trend from 2000 to 2015, with the total area increasing by 115.79%. The overall scale of a city continues to expand, and the system becomes increasingly complex. The urban system is more balanced than the ideal Zipf distribution, but it also exhibited different characteristics in 2005. Urban areas are mostly concentrated in the eastern and central regions, and the difference between the east and the west is considerable. However, cities in the western region continuously expand. Beijing, Shanghai, Tianjin, and Guangzhou are the four largest cities in China. Approximately 73.30% of the cities are expanding in an extended manner; the urban form tends to be scattered, and land use efficiency is low. The new urban areas mainly come from cultivated land and ecological land.

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“…CA is a basic model used to simulate the spatiotemporal evolution characteristics of urban complex systems [32][33][34]. The model has been continuously improved and now includes the Artificial Neural Network-CA (ANN-CA) model [35], the Conversion of Land Use and its Effects (CLUE) model [36], and the Geographical Simulation and Optimization (GeoSOS) model [37]. Compared with other models that depend on historical data of land use over multiple periods, the CA-based FLUS model is based on one phase of the data, which is combined with the various driving factors of natural effects and human interventions.…”

Section: Introductionmentioning

confidence: 99%

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

Liu

Zhang

2019

Sustainability

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The development and evolution of an urban green space system is affected by both natural effects and human intervention. The simulation and prediction of an urban green space system can enhance the foresight of urban planning. In this study, several land use change scenarios of the main urban area of Xuchang City were simulated from 2014 to 2030 based on high-resolution land use data. The layout of each scenario was evaluated using landscape indexes. A Cellular Automata–based method (i.e., future land use simulation, FLUS) was applied to develop the urban green space system, which we combined with urban land use evolution. Using recent data, the FLUS model effectively dealt with the uncertainty and complexity of various land use types under natural and human effects and solved the dependence and error transmission of multiperiod data in the traditional land use simulation process. The root mean square error (RMSE) of probability of the suitability occurrence module and the Kappa coefficient of the overall model simulation accuracy verification index both met accuracy requirements. It was feasible to combine the evolution of the urban green space system with urban land development. Moreover, under the Baseline Scenario, the urban land use layout was relatively scattered, and the urban green space system showed a disordered development trend. The Master Plan Scenario had a compact urban land use layout, and the green space system was characterized by networking and systematization, but it did not consider the service capacity of the green space. The Planning Guidance Scenario introduced constraint conditions (i.e., a spatial development strategy, green space accessibility, and ecological sensitivity), which provided a more intensive and efficient urban space and improved the service function of the green space system layout. Managers and planners can evaluate the urban future land use development mode under different constraints. Moreover, they would be able to adjust the urban planning in the implementation process. This work has transformed the technical nature of the planning work from “static results” to a “dynamic process”.

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Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries

Cited by 145 publications

References 46 publications

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

Land Use Change in Coastal Cities during the Rapid Urbanization Period from 1990 to 2016: A Case Study in Ningbo City, China

Spatiotemporal Pattern Analysis of China’s Cities Based on High-Resolution Imagery from 2000 to 2015

The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China

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