Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019–2049) Zarriné-Rūd River Basin in Iran

Ghalehteimouri, Kamran Jafarpour; Ali., Shamsodini; Mousavi, Mir Najaf; Ros, Faizah Che; Khedmatzadeh, Ali

doi:10.1016/j.envc.2021.100399

Cited by 60 publications

(29 citation statements)

References 32 publications

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“…A Markov chain is a special stochastic motion process that describes the “no aftereffect” probability distribution upon moving from one state to another [ 42 ]. The key to predicting urban land demand using a Markov model is to construct a transition probability matrix for mutual transformation between different land uses [ 43 ]. The annual transfer rate of a certain land use type can be calculated by using observational land use data from two points in time, which gives the transition probability matrix for this period.…”

Section: Methodsmentioning

confidence: 99%

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Liao

Tang

Shao

2022

IJERPH

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The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

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Section: Methodsmentioning

confidence: 99%

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Liao

Tang

Shao

2022

IJERPH

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“…Therefore, it is essential to classify and map LULC patterns and dynamics using multi-temporal satellite images to analyze the changes in LULC for different decades and forecast the future pattern of LULC types with different methods and techniques as the Land Change Modeler or (CA)-Markov chain model [19] . The CA-Markov is a hybrid model which can predict the transitions or spatiotemporal dynamics of LULC classes [20], [21]. The Land Change Modeler (LCM) is based on transition probability matrices produced by the Markov-Chain and transition potential maps produced by training the Support Vector Machine (SVM), MLP, Logistic Regression, Decision Forest, WNL, or SimWeight options.…”

Section: Iintroductionmentioning

confidence: 99%

Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Jalayer

Sharifi

Abbasi‐Moghadam

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land use and land cover change (LULCC) is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image 2001, 2014 and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the Support Vector Machine (SVM) algorithm and the Markov Chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318 and KlocationStrata = 0.9320, showing a good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001-2021, the significant increase of Agricultural land (14317 ha) and Barren area (9063 ha), and the sharp decline of Grassland (26215 ha) and Forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that Forest cover will continue to decrease from 29.46% (50720.2667 ha) in 2021 to 25.67% of area (44207.78694 ha) in 2040, as well as, unceasing expansion of Barren area, Agricultural land and Built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.

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“…Usually, the data level division is used to estimate the probability distribution and change of each type, and the evolution and development process of geographical phenomena are approximated as Markov processes [ 37 ]. A particular kind of distribution at time t is represented by the state probability vector of

of 1 × k , and the whole state transition process is described by the probability value k × k , as the Markov probability transition matrix M ij [ 38 , 39 ]. M ij represented the probability that a spatial unit of type i at time t becomes of type j at time t + 1.…”

Section: Methodsmentioning

confidence: 99%

Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China

Tan

Chen²

2022

IJERPH

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During the COVID-19 pandemic, the digital economy has developed rapidly. The airborne nature of COVID-19 viruses has attracted worldwide attention. Therefore, it is of great significance to analyze the impact of the digital economy on particulate matter 2.5 (PM2.5) emissions. The research sample of this paper include 283 prefecture-level cities in China from 2011 to 2019 in China. Spatial Durbin model was adopted to explore the spatial spillover effect of digital economy on PM2.5 emissions. In addition, considering the impact of smart city pilot (SCP) policy, a spatial difference-in-differences (SDID) model was used to analyze policy effects. The estimation results indicated that (1) the development of the digital economy significantly reduces PM2.5 emissions. (2) The spatial spillover effect of the digital economy significantly reduces PM2.5 emissions in neighboring cities. (3) Smart city construction increases PM2.5 emissions in neighboring cities. (4) The reduction effect of the digital economy on PM2.5 is more pronounced in the sample of eastern cities and urban agglomerations.

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Predicting spatial and decadal of land use and land cover change using integrated cellular automata Markov chain model based scenarios (2019–2049) Zarriné-Rūd River Basin in Iran

Cited by 60 publications

References 32 publications

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China

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