Simulation of changes in the spatial pattern of land use in China

Verburg, Peter H.; Veldkamp, A.; Fresco, L.O.

doi:10.1016/s0143-6228(99)00003-x

Cited by 149 publications

(60 citation statements)

References 13 publications

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“…CLUE was developed and tested at country level for Costa Rica (Veldkamp and Fresco, 1996a,b;Schoorl et al, 1997). Since, the model has been successfully run for various countries in Latin America and Asia: Java (Verburg et al, 1999a); China (Verburg et al, 1999b); Atlantic zone of Costa Rica (Kok and Veldkamp, 2000); Ecuador (De Koning et al, 1999). It uses a (statistical) description of land use patterns and their dependency on a set of socio-economic and biophysical variables at a number of spatial resolutions.…”

Section: The Clue Modelling Frameworkmentioning

confidence: 99%

A method and application of multi-scale validation in spatial land use models

Kok

Farrow

Veldkamp

et al. 2001

Agriculture, Ecosystems & Environment

103

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The majority of the large number of existing land use models lack a proper validation, often because of data problems. Moreover, despite recognition of the necessity to incorporate a multi-scale analysis, scale dependencies are normally not considered during validation. In this paper, a multi-scale land use change modelling framework, conversion of land use and its effects (CLUE), is calibrated for Costa Rica and validated at five spatial resolutions for Honduras and Costa Rica. Both countries experienced locally very strong actual land use changes. Calibration runs show that the model is very sensitive to changes in the autonomous development parameter, which defines the influence of the finest resolution. Validation results are very satisfactory for both countries. Especially, changes in major land use types are reproduced with the model. Changes in localised land use types are more difficult to project. The magnitude of gains and magnitude of losses are slightly underestimated in all cases. The multi-scale validation demonstrates that results improve strongly, and exponentially, with decreasing spatial resolution. Strong reduction of the number of observations results in a correlation between actual and modelled changes that approximates the perfect value of 1. The study demonstrates that the CLUE modelling framework can reproduce changes as they took place in Central America in the 1970s and 1980s, and shows how conclusions can differ depending on the scale at which validation is performed.

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Section: The Clue Modelling Frameworkmentioning

confidence: 99%

A method and application of multi-scale validation in spatial land use models

Kok

Farrow

Veldkamp

et al. 2001

Agriculture, Ecosystems & Environment

103

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“…During 1986During -2003 expansion in China occurred on more than 33,400 km 2 of farmland, accounting for 21% of total farmland loss (Chen, 2007). Much recent research has examined the characteristics of urban expansion and the consequent farmland loss in different regions (Verburg et al, 1999;Batisani and Yarnal, 2009;Deng et al, 2010;Jiang et al, 2012;Kong et al, 2012;Lu et al, 2011) However, less attention has been paid to the quality of farmland lost through urban expansion. In particular, no quantitative indicator of high-quality farmland (HQF) consumption due to urbanization has been developed.…”

Section: Introductionmentioning

confidence: 99%

Urban expansion and its consumption of high-quality farmland in Beijing, China

Song

Pijanowski

Tayyebi

2015

Ecological Indicators

162

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a b s t r a c tChina faces the challenge of using limited farmland to feed more than 1.3 billion people. Accelerated urbanization has exacerbated this challenge by consuming a large quantity of high-quality farmland (HQF). It is therefore essential to assess the degree to which urban expansion has preferentially consumed HQF, and discern the mechanism behind this. We found urban areas in Beijing to expand at speeds of 48.97 km 2 /year, 21.89 km 2 /year, 62.30 km 2 /year and 20.32 km 2 /year during the periods 1986-1995, 1995-2000, 2000-2005 and 2005-2020, respectively. We developed an indicator of HQF consumption due to urban expansion, representing the ratio of HQF consumed to its proportion of overall farmland, and found its values were 2.21, 1.57, 1.99 and 1.10 for 1986-1995, 1995-2000, 2000-2005 and 2005-2020, respectively. Thus, although HQF has been overrepresented in the farmland consumed by Beijing's urbanization, this phenomenon has decreased over time. Centralized expansion has contributed greatly to consumption of HQF. Topography and distances to urban and water bodies determine the relative consumption of HQF in urbanization.

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“…The simulation results from neural network based CA should not be interpreted as forecasts of future events (Verburg et al, 1999b), which are based on a scenarios design. But results do reflect possible urban expansion patterns under different types of scenarios based on present urban development; therefore it can also be used as a tool for land managers and planners to understand the possible impacts of urban expansion on the urban complex ecosystem, and furthermore, under the created platform, urban managers can make scientific decisions regarding urban ecosystem development.…”

Section: Discussionmentioning

confidence: 99%