Dynamic simulation of Spartina alterniflora based on CA-Markov model—a case study of Xiangshan bay of Ningbo City, China

Qiu, Yahui; Li, Jianbo

doi:10.3391/ai.2018.13.2.10

Cited by 7 publications

(6 citation statements)

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“…LULC change prediction was conducted by using the cellular automata and Markov chain (CA-Markov) model in IDRISI software (Nouri et al 2014;Qiu & Lu 2018). The model is proficiently employed to simulate temporal and spatial land-use changes.…”

Section: Land-use Change Modeling and Validationmentioning

confidence: 99%

Hydrologic responses to climate and land-use/land-cover changes in the Bilate catchment, Southern Ethiopia

Kuma

Fufa

Demissie

2021

Journal of Water and Climate Change

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The likely effects of climate and land-use/land-cover (LULC) changes on hydrologic processes in Bilate catchment, Ethiopia were evaluated. The study emphasizes the evaluation of individual and combined impacts on hydrologic responses of climate and LULC changes. Climatic scenarios included a downscaled regional climate model from CORDEX-Africa. The CA–Markov model was used to project LULC. The results revealed that distinct changes on hydrologic responses occurred which follow the direction of climate and LULC changes. A 30.87% decline in rainfall resulted in about 4.09, 1.43 and 3.57% decline in runoff, groundwater and water yield, respectively. A rise in mean temperature by 1.3 °C resulted in 7 and 0.8% increase in potential and actual evapotranspiration, respectively. Runoff, groundwater and water yield are projected to decrease by 11.24, 12.54 and 11.54%; however, potential and actual evapotranspiration are projected to increase by 19 and 14.7%, respectively, under combined climate and LULC changes. The joint effects of climate and LULC changes on hydrologic responses in the forthcoming were higher than the variation trend of climate or LULC change alone. Climate change compared with LULC change has a higher impact on hydrologic responses. The results obtained provide further insight into future water balance, assistance in water resources planning and management.

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Section: Land-use Change Modeling and Validationmentioning

confidence: 99%

Hydrologic responses to climate and land-use/land-cover changes in the Bilate catchment, Southern Ethiopia

Kuma

Fufa

Demissie

2021

Journal of Water and Climate Change

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“…Therefore, a Kappa value below 0.4 indicates a low likelihood of fair agreement, while values within the range of 0.4 ≤ Kappa ≤ 0.6 denote moderate accuracy. A Kappa value exceeding 0.6 signi es minimal disparities between observed and simulated locations, indicative of substantial agreement (Wu et al, 2008;Qiu and Lu, 2018).…”

Section: Ca-model Validationmentioning

confidence: 99%

Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques

Farhan,

Mahafdah,

Sathiamurthy

et al. 2024

Preprint

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Situated at Earth's lowest continental point, the Dead Sea experiences a worrying decline in water levels. The primary drivers include the diversion of water from the Jordan River and its tributaries, as well as mineral extraction activities on both sides of the lake. The aim of this study is to analyze the thematic map of 1971 and satellite images of 1984, 1994, 2004, 2014 and 2022 of the Dead Sea to determine the surface area and water level of the Dead Sea and its declining rate. CA-Markov model were employed to generate projected surface area of Dead Sea for periods 2034 and 2050. Time series of observed and future using RPC’s 4.5 and 8.5 of climate data especially temperature were analysis has been implemented to track the climate behavior. Statistical analyses of Kendall correlation matrix were performed on observed and predicted of surface area, water level and temperature. The study shows that the Dead Sea has shrunk by 41.8% during the period from 1971 to 2022, while the water sea level is expected to decrease 12.63 m and 33 m for period 2034 and 2050 respectively. In addition, there were a significant inverse relationship between surface area, water level and temperature with correlation (r=-0.79; p = 0.001) and (r=-0.82; p = 0.001), respectively. It is worth highlighting that from 2022 to 2050, the mean annual temperature is expected to rise by at least 1 ˚C. The long-term strategic vision for stabilizing Dead Sea water levels envisions a two-fold approach: (1) augmenting natural inflow through the introduction of 300–400 million MCM from manufactured sources channeled into the Jordan River, and (2) implementing a reduction in water extraction by Dead Sea industries up to a maximum of 330 million MCM.

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“…However, the factors affecting land cover change are often nonlinear. It is difficult to accurately predict the spatio-temporal dynamics of S. alterniflora by only using the local transformation rules of cellular automata (Qiu and Lu, 2018;Guo, 2019).…”

Section: Cellular Automata Model (Ca)mentioning

confidence: 99%

“…The CA model and Markov model are combined to generate a CA Markov model that has both the capacity of the CA model to simulate the spatial variation of complex systems and the numerical analysis capacity of the Markov model to forecast the long-term dynamic change. The CA Markov model can predict the temporal and spatial patterns of land cover change with high precision (Qiu and Lu, 2018;Qin et al, 2020;Yao et al, 2022a).…”

Section: Mce-ca-markovmentioning

confidence: 99%

“…Multi criteria evaluation (MCE) is applied to the suitability atlas module of the CA-Markov model, which can combine multiple factors into restrictive factors of the transformation model, making the model both more scientific and more practical. Qiu and Lu (2018) added driving mechanisms for suitability analysis and predicted the expansion dynamics of S. alterniflora in 2018, 2022, 2026, and 2030 with a CA Markov model (Qiu and Lu, 2018). Qin et al (2020) constructed an MCE-CA-Markov composite model to simulate landscape pattern changes using four Landsat remote sensing images from 2000-2015 (Qin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

Yan

Xie

et al. 2022

Front. Mar. Sci.

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The spread of Spartina alterniflora (smooth cordgrass) has put biodiversity and ecosystem function at risk since it was introduced to China just a few decades ago. A better understanding of how the range of S. alterniflora will expand in the future will help manage the invasion of this species in coastal wetlands. However, it is difficult to model the future extent of Spartina saltmarshes in China. To address this issue, we combined multi criteria evaluation with traditional CA Markov model to provide robust forecasting of the spatial expansion of S. alterniflora for the next ten years, at Dafeng Milu National Nature Reserve, Jiangsu, China. Our results showed that, compared with the interpretation results of high-resolution remote sensing images in 2020, the kappa coefficient of verification accuracy was 82.63%, indicating that the MCE-CA-Markov model has good prediction results. Therefore, the model can forecast the expansion process of S. alterniflora over the next ten years. The model predicts that the area of S. alterniflora continued to decrease from 910.25 ha in 2020 to 881.21 ha in 2030. The spatial distribution of S. alterniflora has been decreasing on the landward side while it has been expanding towards the sea on the seaward side. This study provides some suggestions for effective management and control of invasive species, which could be important for wetland biodiversity conservation and resource management.

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Dynamic simulation of Spartina alterniflora based on CA-Markov model—a case study of Xiangshan bay of Ningbo City, China

Cited by 7 publications

References 0 publications

Hydrologic responses to climate and land-use/land-cover changes in the Bilate catchment, Southern Ethiopia

Hydrologic responses to climate and land-use/land-cover changes in the Bilate catchment, Southern Ethiopia

Assessment and monitoring of the Dead Sea surface area and water level using remote sensing and GIS techniques

Examining the expansion of Spartina alterniflora in coastal wetlands using an MCE-CA-Markov model

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