Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: a study in Northern Turkey

Aksoy, Hasan Hüseyin; Kaptan, Sinan

doi:10.1007/s10661-021-09281-x

Cited by 28 publications

(15 citation statements)

References 68 publications

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“…If all the data was available with sufficient accuracy, the research could immediately focus on modeling the LULCC and finding the most influential driving variables. The other study also suggests that data preparation stages in the LULCC study are crucial [32].…”

Section: Lulcc Modeling Methods Developmentmentioning

confidence: 96%

“…The integration of remote sensing (RS) and geographic information systems (GIS) provides spatial data processing capabilities for the identification, monitoring, and evaluation of an area [25], [26]. This integration was widely used for environmental protection [27], [28], land use management [29], [30], environmental carrying capacity analysis [31], and deforestation monitoring [32]- [35]. This knowledge plays an important role in the decision-making process [36], [37] and is fundamental knowledge for regional spatial planning and policy [38], [39].…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing and geographic information systems technics for spatial-based development planning and policy

Arimjaya

Dimyati

2022

IJECE

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Indonesia's land-use and land-cover change (LULCC) is a global concern. The relocation plan of the capital city of Indonesia to East Kalimantan will be becoming an environmental issue. Knowing the latest land cover change modeling and prediction research is essential for fundamental knowledge in spatial planning and policies for regional development. Five articles related to integrated technology of geographic information systems (GIS) and remote sensing for spatial modeling were reviewed and compared using nine variables: title, journal (ranks), keywords, objectives, data sources, variables, location, method, and main findings. The results show that the variables that significantly affect LULCC are height, slope, distance from the road, and distance from the built-up area. The artificial neural network-based cellular automata (ANN-CA) method could be the best approach to model the LULCC. Furthermore, by the current availability of global multi-temporal and multi-sensor remote sensing data, the LULCC modeling study can be limitless

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Section: Lulcc Modeling Methods Developmentmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Remote sensing and geographic information systems technics for spatial-based development planning and policy

Arimjaya

Dimyati

2022

IJECE

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“…Previously, most of the research objects were selected on a large scale. For example, (Jana et al, 2022), (Sma et al, 2019), (Aksoy and Kaptan, 2021), Etemadi et al (2018) used the CA-Markov model to simulate and predict the large-scale land use evolution of the Mahi River in India, mountainous cities in Oman, Ulus district in northern Turkey, mangroves along the coast of Iran. Meanwhile, (Fu et al, 2022) simulated and predicted the landscape pattern of county land use in Mianzhu City.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolution and prediction of land use around metro stations

Jia

Dan

et al. 2023

Front. Environ. Sci.

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Metro stations are considered high-quality resources for promoting urban development, which have great influences on the surrounding land use changes. The simulation and prediction of land use change can provide a scientific basis for urban land planning. In this work, the cellular automata (CA)-Markov model was adopted by taking into account point of interest (POI) kernel density and station accessibility as driving factors to predict the land use change of station surrounding areas. Then, the land type compositions of different years, temporal and spatial evolution of landscape patterns, and strategies of different metro stations were explored. The results show that the Kappa coefficients of the Zoo Station and the Lu Xiao Station are 87% and 79%, respectively, indicating that the improved CA-Markov model can predict land use changes more accurately by considering POI kernel density and station accessibility. Finally, different optimized strategies based on systematic predictions of land use landscape patterns according to the spatial and temporal distribution of metro stations were proposed. The work provides important references for predicting the impact of new metro stations on land use in the future and guides the adjustment and optimization of land use policy planning.

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“…To produce spatially distributed continuous weighing factors, a kernel size of 5 × 5 that accounts for the neighborhood pixels was chosen, as shown in Figure 3. Pixels further away from the existing LU/LC class were deemed less suitable than pixels closer to the existing LU/LC class [57,58]. The present study used Markov chain and CA-Markov models available in Terrset.…”

Section: Hybrid Ca-markov Modelingmentioning

confidence: 99%

Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

Theres

Radhakrishnan

Rahaman

2023

Earth

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Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

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Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: a study in Northern Turkey

Cited by 28 publications

References 68 publications

Remote sensing and geographic information systems technics for spatial-based development planning and policy

Remote sensing and geographic information systems technics for spatial-based development planning and policy

Evolution and prediction of land use around metro stations

Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

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