A global analysis of land take in cropland areas and production displacement from urbanization

Vliet, Jasper van; Eitelberg, David A.; Verburg, Peter H.

doi:10.1016/j.gloenvcha.2017.02.001

Cited by 291 publications

(126 citation statements)

References 59 publications

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“…However, the trend of the reduction in farmland/grassland experienced historically in the study area is not new. Many urban landscapes around the world have experienced similar reduction (e.g., [44][45][46]). Ji et al [2] found that there was a large loss in farmland/grassland.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Zubair

Weilert

2017

Sustainability

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Urban wetlands play important roles in providing several ecosystem services that support the urban environment. As such, scientists have studied them to understand the urban processes that lead to their continued decline. However, little attention has been given to the drivers of land-use change that may affect this fragile ecosystem in the future. Understanding this could serve as a critical step towards urban wetland management and sustainability. In this study, we utilized an integrated approach that combined Similarity Weighted Instance-based Machine Learning and Markov chain, both embedded in the IDRISI Land Change Modeler to simulate change in the landscape of three watersheds in the Kansas City Metropolitan area. The purpose was to assess the possible future impacts of urban expansion-induced landscape change on wetlands within the study area, using a retrospective approach. To achieve this, classified SPOT satellite data covering the three watersheds were used to generate historical land cover maps of the study area between 1992 and 2010 to analyze changes to the landscape. In addition, the study identified several drivers of land change associated with the historical change process in the study area, and accounted for their role in the modeling process. On this basis, the study made the prediction of urban landscape transformation to the end date of 2014. The prediction result was verified with a more accurate map that was derived from independently classifying a 2014 SPOT image of the study area. Results from this study show that impervious surfaces, which were used as an index of urban expansion, may increase by approximately the same magnitude experienced historically, which may result in a small but significant loss of wetlands and other land cover classes within the study area.

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Section: Discussionmentioning

confidence: 99%

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Zubair

Weilert

2017

Sustainability

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“…A particular area of interest has been the development and improvement of predictive simulation models, such as the agent-based model (Matthews et al, 2007;Yuan et al, 2017), the cellular automata model and its evolved models based on grid neighborhood relationship analysis (Grinblat et al, 2016;Van Vliet et al, 2017), or the dynamics of land system model and state-and-transition simulation model based on analyses of changes in land system structures and spatial configuration succession (Wilson et al, 2016;Daniel et al, 2016;Najmuddin et al, 2017). Using these simulation models, it is possible to construct rational scenarios for different sustainability objectives, such as maximizing economic effects (Wu et al, 2012), minimizing pollutant emissions or environmental impacts (Bohnes et al, 2017;Degraeuwe et al, 2017), prioritizing ecological security (Brunner et al, 2017;Eitelberg et al, 2016), limiting climate change and carbon emissions (Anaya-Romero et al, 2015;Prestele et al, 2017), as well as for other sustainability scenarios, such as water resources (Proskuryakova et al, 2018), agricultural production (Chaudhary et al, 2018;Krasa et al, 2010;Van Vliet et al, 2017), or environmental protection (Najmuddin et al,2017;Zarandian et al, 2017). Obviously, most of these models are based on historical or current scenarios, and they assume consistent historical trajectories and development statuses, which can be problematic for developing countries with large-scale and disorderly land development (Fan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Reshaping the Sustainable Geographical Pattern: A Major Function Zoning Model and Its Applications in China

Fan

Wang

et al. 2019

Earth's Future

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The imbalance between human activities and Earth's natural surface adversely affects the sustainability of the Earth system. From an understanding of the suitability of surface functions and potential ramifications of policy decisions, this study proposes a sustainable geographical pattern. It introduces a national‐to‐provincial downscaling approach to optimizing a national‐scale zoning scheme that covers 60%–80% of land development and protection functions in China and to creating a major function zoning model. Taking China as the case study area, the following function zones are identified: urbanization zones, food security zones, ecological security zones, and heritage protection zones. This study then proposes a three‐dimensional index system and algorithm, develops individual and integrated assessments for the major functions of the land grid, and produces national‐scale and provincial‐scale major function zoning schemes consisting of county‐level administrative cells, the first schemes to create a blueprint for a sustainable national land pattern in China through optimized convergence. In total, 5% of China's land area is covered by urbanized zones, 28% by food security zones, 55% by ecological security zones, and 12% by heritage protection zones. In the future, there will be 805 urbanization administrative units, 790 food security counties, 780 ecological security counties, and 8,151 heritage protection zones. The scheme created using the model developed in this study is 95% consistent with the final scheme implemented by China's central government, and it has a high degree of robustness and precision. It can serve as a reference for scientific research and decision‐making concerning sustainable development in developing countries.

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“…In most of above models, spatial and temporal evolution rules are applied to simulate historical or current scenarios, and key development parameters are adjusted to identify the differences among various scenarios, although they are insufficient to diagnose deviation (s) caused by land development policies. However, reasonable scenarios from different dimensions can be constructed by these simulation models, such as maximizing economic effects (Wu, Peng, Zhang, Skitmore, & Song, ); minimizing pollutant emissions or environmental impacts (Bohnes, Gregg, & Laurent, ; Degraeuwe et al, ; Manuel‐Navarrete & Pelling, ); prioritizing ecological protection (Brunner, Huber, & Grêt‐Regamey, ; Eitelberg, van Vliet, Doelman, Stehfest, & Verburg, ); and constraining climate change and carbon emissions (Anaya‐Romero et al, ; Prestele et al, ) as well as sustainable scenarios for relatively singular systems such as water resource systems (Proskuryakova, Saritas, & Sivaev, ), agricultural production systems (Krasa, Dostal, Vrana, & Plocek, ; van Vliet et al, ), and ecological conservation systems (Najmuddin et al, ; Zarandian et al, ). In policy study, those scenario simulation methods could primarily be applied to analyze a single policy dimension or to compare scenarios from different dimensions, but it is difficult to use these models to determine the reasonableness of a policy and even more challenging to quantify the policy deviation.…”

Section: Introductionmentioning

confidence: 99%

Assessment of coastal development policy based on simulating a sustainable land‐use scenario for Liaoning Coastal Zone in China

Fan

Wang

et al. 2018

Land Degrad Dev

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Unsustainable land development policy can result in serious eco‐environmental issues, leading to unsustainable development in coastal zones, the phenomenon of which is particularly prominent in developing countries. How to assess the impact of policy on the sustainable development of land resource in the future is a challenge that is well worth exploring. With the coastal zone of Liaoning Province in China as an example, this paper proposes a new framework for assessing coastal development policy based on simulating the sustainable land‐use scenario (S). The results show that under Scenario D that driven by government's development policies, the proportions of highly exploited construction land and shoreline would be 11 and 18 percentage points higher, respectively, than those under Scenario S, whereas the potential losses of agricultural land and natural land in Scenario S would increase by 14.5% and 12.6%, respectively, the natural shoreline would decrease by 25.1% in comparison with Scenario S. Policy‐led deviation indexes in industrial, urban, and port land use are 12.98, 7.15, and 2.02, respectively, on the contrary in natural land use, it even reaches a high level of −15.07. This indicates that, being driven by the government development policies, the development scales of industrial, urban, and port land/shoreline use significantly will exceed the corresponding sustainably healthy ranges, leading to a massive diversion of natural land/shoreline, which thereby becomes the type most remarkably impacted by policy errors. The findings can provide case‐study analysis and reference for assessing the impact of development policy on resource‐environment sustainability.

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A global analysis of land take in cropland areas and production displacement from urbanization

Cited by 291 publications

References 59 publications

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Modeling the Impact of Urban Landscape Change on Urban Wetlands Using Similarity Weighted Instance-Based Machine Learning and Markov Model

Reshaping the Sustainable Geographical Pattern: A Major Function Zoning Model and Its Applications in China

Assessment of coastal development policy based on simulating a sustainable land‐use scenario for Liaoning Coastal Zone in China

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