Urban expansion simulation under constraint of multiple ecosystem services (MESs) based on cellular automata (CA)-Markov model: Scenario analysis and policy implications

Zhang, Yan; Xia, Chang; Liu, Yanfang; Lu, Yanchi; Wang, Yiheng

doi:10.1016/j.landusepol.2021.105667

Cited by 55 publications

(19 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kappa greater than 0.75 represents high accuracy, while Kappa less than 0.4 represents low accuracy. 42 SWAT model SWAT is capable of simulating the hydrological cycle at a watershed scale. SWAT has been widely used, especially for research into the influence of land-use pattern and climate change 15,35,43 ; it can be used to simulate runoff, evapotranspiration, and groundwater table dynamics with a high level of accuracy.…”

Section: Plus Modelmentioning

confidence: 99%

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Sun,

Wang

2024

J Sci Food Agric

View full text Add to dashboard Cite

BackgroundIrrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land‐use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch‐generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed.ResultsBoth LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 mm and 30.7 mm per growing season under 1.5°C and 2.0°C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5°C and 2.0°C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5°C warming, and 13.1%, 28.7%, and 58.2% under 2.0°C warming, respectively.ConclusionWe suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation.This article is protected by copyright. All rights reserved.

show abstract

Section: Plus Modelmentioning

confidence: 99%

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Sun,

Wang

2024

J Sci Food Agric

View full text Add to dashboard Cite

show abstract

“…The Markov model is a raster-based spatial probability model with subsequent nullity. It is often used for the prediction of geographical events, specifically involving Markov processes, state transfer matrices, and state transfer probability matrices [37,38] that are widely used in studies of land-use simulation, urban sprawl, and ecosystem services [39][40][41].…”

Section: The Coupled Markov-flus Modelmentioning

confidence: 99%

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

Lu,

Xue,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Rapidly predicting and revealing the spatiotemporal characteristics and driving factors of land-use changes in carbon storage within megacities under different scenarios is crucial to achieving sustainable development. In this study, Jinan City (JNC) is taken as the study area, and the Markov-FLUS-InVEST model is utilized to predict and analyze the spatiotemporal variation in carbon storage in 2030 under three scenarios, namely, the natural development scenario (S1), the ecological conservation scenario (S2), and the economic development scenario (S3). The drivers of carbon storage changes were identified using an optimal parameter-based geographic detection (OPGD) model. The findings indicate that (1) land use from 2010 to 2018 shows a trend of continuous expansion of construction land and reduction in arable land. (2) The main types of carbon pools were cropland, forest, and grassland, accounting for more than 96% of the total amount. Carbon storage showed a decreasing trend from 2010 to 2018, and the main type of carbon pool that decreased was cropland. The center of gravity of carbon storage increases and decreases was located in the southern Lixia District, and the center of gravity of increase and decrease moved to the southwest by 3057.48 m and 1478.57 m, respectively. (3) From 2018 to 2030, the reductions in carbon stocks were 3.20 × 106 t (S1), 2.60 × 106 t (S2), and 4.26 × 106 t (S3), and the carbon release was about 9 times (S1), 4 times (S2), and 10 times (S3) that of the carbon sink. (4) The contribution of slope (A2) ∩ nighttime light index (B6) and elevation (A1) ∩ nighttime light index (B6) to the regional heterogeneity of carbon stocks was the largest among the interaction drivers. To sum up, this study deepens the simulation of spatial and temporal dynamics of carbon storage under land-use changes in megacities and the related driving mechanism, which can provide the basis for scientific decision-making for cities to conduct territorial spatial planning and ecological protection and restoration.

show abstract

“…Previous studies focused mainly on the spatial pattern of urban land in megacities, large cities, and urban agglomeration by using DMSP/OLS night light data of the U.S. Department of Defense and its follow-on platform, Landsat data of USGS Earth Resources Observation and Science (EROS) Center, or other data sources [77][78][79]. They paid more attention to the growth of the urban land area and boundary, using simulation models of cellular automaton (CA) to simulate the future urban land distribution [80], and concentric circle and sector analysis to compare expansion rates at different distances and in different directions [81,82]. In recent studies, researchers began to care about the dynamics of spatial structure and growth types in the process of urban land expansion, usually using a series of indexes and methods to evaluate them.…”

Section: Characteristics and Drivers Of Urban Expansionmentioning

confidence: 99%

Analysis of Temporal and Spatial Characteristics of Urban Expansion in Xiaonan District from 1990 to 2020 Using Time Series Landsat Imagery

Liu

Dong

2021

Remote Sensing

View full text Add to dashboard Cite

With the rapid development in the global economy and technology, urbanization has accelerated. It is important to characterize the urban expansion and determine its driving force. In this study, we used the Xiaonan District in Hubei Province, China, as an example to map and quantify the spatiotemporal dynamics of urban expansion from the two perspectives of built-up area and urban land in 1990–2020 by using remote sensing images. The location of rivers was found to be a primary limiting factor for spatial patterns and expansion of the built-up area. The transfer of the city center and the main direction of expansion generally corresponded well to the topography, policies, and development strategies. The built-up area expanded faster than the urban population in 1995–2020, which caused a waste in land resources. The results showed that the urban expansion first decreased and then increased during the research period. The increase in the proportion of the secondary industry was the main driving force of the urban expansion. Based on the characteristics of urban expansion in the past three decades, we conclude that the urban land of Xiaonan District will expand quickly in the future and will occupy vast agricultural land. The government must deploy control measures to balance the benefits and costs of urban expansion.

show abstract

Urban expansion simulation under constraint of multiple ecosystem services (MESs) based on cellular automata (CA)-Markov model: Scenario analysis and policy implications

Cited by 55 publications

References 53 publications

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

Analysis of Temporal and Spatial Characteristics of Urban Expansion in Xiaonan District from 1990 to 2020 Using Time Series Landsat Imagery

Contact Info

Product

Resources

About