IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8410-d427003.html
   My bibliography  Save this article

Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos

Author

Listed:
  • Cheechouyang Faichia

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China)

  • Zhijun Tong

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Jiquan Zhang

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Xingpeng Liu

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China
    Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China)

  • Emmanuel Kazuva

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China)

  • Kashif Ullah

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China
    Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Bazel Al-Shaibah

    (Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun 130024, China)

Abstract

Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions.

Suggested Citation

  • Cheechouyang Faichia & Zhijun Tong & Jiquan Zhang & Xingpeng Liu & Emmanuel Kazuva & Kashif Ullah & Bazel Al-Shaibah, 2020. "Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos," Sustainability, MDPI, vol. 12(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8410-:d:427003
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8410/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/20/8410/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    2. Christopher R. Schwalm & William R. L. Anderegg & Anna M. Michalak & Joshua B. Fisher & Franco Biondi & George Koch & Marcy Litvak & Kiona Ogle & John D. Shaw & Adam Wolf & Deborah N. Huntzinger & Kev, 2017. "Global patterns of drought recovery," Nature, Nature, vol. 548(7666), pages 202-205, August.
    3. Chen Liping & Sun Yujun & Sajjad Saeed, 2018. "Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-23, July.
    4. Jun Yang & Fei Chen & Jianchao Xi & Peng Xie & Chuang Li, 2014. "A Multitarget Land Use Change Simulation Model Based on Cellular Automata and Its Application," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-11, June.
    5. Rahel Hamad & Heiko Balzter & Kamal Kolo, 2018. "Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    6. Sangha, Kamaljit K. & Maynard, Simone & Pearson, Jasmine & Dobriyal, Pariva & Badola, Ruchi & Hussain, Syed Ainul, 2019. "Recognising the role of local and Indigenous communities in managing natural resources for the greater public benefit: Case studies from Asia and Oceania region," Ecosystem Services, Elsevier, vol. 39(C).
    7. Guan, DongJie & Li, HaiFeng & Inohae, Takuro & Su, Weici & Nagaie, Tadashi & Hokao, Kazunori, 2011. "Modeling urban land use change by the integration of cellular automaton and Markov model," Ecological Modelling, Elsevier, vol. 222(20), pages 3761-3772.
    8. Sonam Wangyel Wang & Belay Manjur Gebru & Munkhnasan Lamchin & Rijan Bhakta Kayastha & Woo-Kyun Lee, 2020. "Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu Zhang & Xiaoyu Niu & Yunfeng Hu & Huimin Yan & Lin Zhen, 2022. "Temporal and Spatial Evolution Characteristics and Its Driving Mechanism of Land Use/Land Cover Change in Laos from 2000 to 2020," Land, MDPI, vol. 11(8), pages 1-20, July.
    2. Chul-Min Song, 2021. "Analysis of the Effects of Local Regulations on the Preservation of Water Resources Using the CA-Markov Model," Sustainability, MDPI, vol. 13(10), pages 1-22, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    2. Zaheer Abbas & Guang Yang & Yuanjun Zhong & Yaolong Zhao, 2021. "Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China," Land, MDPI, vol. 10(6), pages 1-26, June.
    3. Auwalu Faisal Koko & Wu Yue & Ghali Abdullahi Abubakar & Roknisadeh Hamed & Akram Ahmed Noman Alabsi, 2020. "Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    4. Michel Opelele Omeno & Ying Yu & Wenyi Fan & Tolerant Lubalega & Chen Chen & Claude Kachaka Sudi Kaiko, 2021. "Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    5. Han, Yu & Jia, Haifeng, 2017. "Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China," Ecological Modelling, Elsevier, vol. 353(C), pages 107-116.
    6. Harik, G. & Alameddine, I. & Zurayk, R. & El-Fadel, M., 2023. "Uncertainty in forecasting land cover land use at a watershed scale: Towards enhanced sustainable land management," Ecological Modelling, Elsevier, vol. 486(C).
    7. Courage Kamusoko & Yukio Wada & Toru Furuya & Shunsuke Tomimura & Mitsuru Nasu & Khamma Homsysavath, 2013. "Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management," Land, MDPI, vol. 2(1), pages 1-19, January.
    8. Zimu Jia & Bingran Ma & Jing Zhang & Weihua Zeng, 2018. "Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    9. Ehab Hendawy & A. A. Belal & E. S. Mohamed & Abdelaziz Elfadaly & Beniamino Murgante & Ali A. Aldosari & Rosa Lasaponara, 2019. "The Prediction and Assessment of the Impacts of Soil Sealing on Agricultural Land in the North Nile Delta (Egypt) Using Satellite Data and GIS Modeling," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    10. Markos Mathewos & Semaria Moga Lencha & Misgena Tsegaye, 2022. "Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation," Land, MDPI, vol. 11(10), pages 1-28, September.
    11. Camila Orellana Pereira & Rossana Escanilla-Minchel & Alejandra Cortés González & Hernán Alcayaga & Mauricio Aguayo & Miguel Aguayo Arias & Alejandro N. Flores, 2022. "Assessment of Future Land Use/Land Cover Scenarios on the Hydrology of a Coastal Basin in South-Central Chile," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    12. Mansour, Shawky & Al-Belushi, Mohammed & Al-Awadhi, Talal, 2020. "Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques," Land Use Policy, Elsevier, vol. 91(C).
    13. Nguyen Hong Giang & Yu-Ren Wang & Tran Dinh Hieu & Nguyen Huu Ngu & Thanh-Tuan Dang, 2022. "Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    14. Shuqing Wang & Xinqi Zheng, 2023. "Dominant transition probability: combining CA-Markov model to simulate land use change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6829-6847, July.
    15. Pakawan Chotchaiwong & Saowanee Wijitkosum, 2019. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios," Land, MDPI, vol. 8(9), pages 1-16, September.
    16. Ruci Wang & Hao Hou & Yuji Murayama, 2018. "Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    17. Shufang Wang & Xiyun Jiao & Liping Wang & Aimin Gong & Honghui Sang & Mohamed Khaled Salahou & Liudong Zhang, 2020. "Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis," Sustainability, MDPI, vol. 12(4), pages 1-13, February.
    18. Sonam Wangyel Wang & Belay Manjur Gebru & Munkhnasan Lamchin & Rijan Bhakta Kayastha & Woo-Kyun Lee, 2020. "Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS," Sustainability, MDPI, vol. 12(9), pages 1-18, May.
    19. Miao Guan & Changsheng Xiong, 2022. "The Net Spatio-Temporal Impact of the International Tourism Is-Land Strategy on the Ecosystem Service Value of Hainan Island: A Counterfactual Analysis," Land, MDPI, vol. 11(10), pages 1-15, September.
    20. Wang, Han & Tian, Fuan & Wu, Jianxian & Nie, Xin, 2023. "Is China forest landscape restoration (FLR) worth it? A cost-benefit analysis and non-equilibrium ecological view," World Development, Elsevier, vol. 161(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8410-:d:427003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.