IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3640-d1636986.html
   My bibliography  Save this article

Evaluation and Prediction of Ecological Quality Based on Remote Sensing Environmental Index and Cellular Automata-Markov

Author

Listed:
  • Weirong Qin

    (College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
    Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
    These authors contributed equally to this work.)

  • Mohd Hasmadi Ismail

    (Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
    These authors contributed equally to this work.)

  • Mohammad Firuz Ramli

    (Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia)

  • Junlin Deng

    (Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China)

  • Ning Wu

    (Key Laboratory of Beibu Gulf Offshore Engineering Equipment and Technology, Beibu Gulf University, Qinzhou 535011, China)

Abstract

The evaluation and prediction of ecological environmental quality are essential for sustainable development and environmental management, particularly in regions experiencing rapid urbanization and industrial growth like Johor in southern Peninsular Malaysia. This study evaluates the temporal and spatial changes in ecological environmental quality in Johor from 1990 to 2020 using the Remote Sensing Environmental Index (RSEI) and Cellular Automata-Markov (CA-Markov). A CA-Markov model was employed to predict ecological environmental quality for the next 12 months based on historical data. The results reveal significant changes over the 30 years, highlighting the dynamic nature of ecological conditions. The prediction results indicate that areas with excellent ecological quality are primarily focused in the central and northern regions, while the southern and eastern edges show mixed ecological conditions. The western region, characterized by intensive land use, shows significant environmental degradation. The poorest ecological points are mainly distributed in urban and semiurban areas with frequent human activities, such as cities, ports, and villages. These findings highlight the need for targeted environmental policies and management strategies to mitigate ecological degradation and promote sustainable development in Johor State of Peninsular Malaysia.

Suggested Citation

  • Weirong Qin & Mohd Hasmadi Ismail & Mohammad Firuz Ramli & Junlin Deng & Ning Wu, 2025. "Evaluation and Prediction of Ecological Quality Based on Remote Sensing Environmental Index and Cellular Automata-Markov," Sustainability, MDPI, vol. 17(8), pages 1-26, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3640-:d:1636986
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3640/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/8/3640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    2. Pengwen Gao & Alimujiang Kasimu & Yongyu Zhao & Bing Lin & Jinpeng Chai & Tuersunayi Ruzi & Hemiao Zhao, 2020. "Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    3. Huan Tang & Jiawei Fang & Ruijie Xie & Xiuli Ji & Dayong Li & Jing Yuan, 2022. "Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI)," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    4. 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.
    5. Meng Luo & Shengwei Zhang & Lei Huang & Zhiqiang Liu & Lin Yang & Ruishen Li & Xi Lin, 2022. "Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    6. Gebdang B. Ruben & Ke Zhang & Zengchuan Dong & Jun Xia, 2020. "Analysis and Projection of Land-Use/Land-Cover Dynamics through Scenario-Based Simulations Using the CA-Markov Model: A Case Study in Guanting Reservoir Basin, China," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    7. 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.
    8. Shengtang Wang & Yingchun Ge, 2022. "Ecological Quality Response to Multi-Scenario Land-Use Changes in the Heihe River Basin," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    9. Camille Parmesan & Gary Yohe, 2003. "A globally coherent fingerprint of climate change impacts across natural systems," Nature, Nature, vol. 421(6918), pages 37-42, January.
    Full references (including those not matched with items on IDEAS)

    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. Jing Liu & Chunchun Hu & Xionghua Kang & Fei Chen, 2023. "A Loosely Coupled Model for Simulating and Predicting Land Use Changes," Land, MDPI, vol. 12(1), pages 1-19, January.
    2. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Richard Tol, 2011. "Regulating knowledge monopolies: the case of the IPCC," Climatic Change, Springer, vol. 108(4), pages 827-839, October.
    4. Ding, Yimin & Wang, Weiguang & Song, Ruiming & Shao, Quanxi & Jiao, Xiyun & Xing, Wanqiu, 2017. "Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China," Agricultural Water Management, Elsevier, vol. 193(C), pages 89-101.
    5. Anne Goodenough & Adam Hart, 2013. "Correlates of vulnerability to climate-induced distribution changes in European avifauna: habitat, migration and endemism," Climatic Change, Springer, vol. 118(3), pages 659-669, June.
    6. Francesca Pilotto & Ingolf Kühn & Rita Adrian & Renate Alber & Audrey Alignier & Christopher Andrews & Jaana Bäck & Luc Barbaro & Deborah Beaumont & Natalie Beenaerts & Sue Benham & David S. Boukal & , 2020. "Meta-analysis of multidecadal biodiversity trends in Europe," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    7. Wesley R. Brooks & Stephen C. Newbold, 2013. "Ecosystem damages in integrated assessment models of climate change," NCEE Working Paper Series 201302, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2013.
    8. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    9. Qifei Zhang & Congjian Sun & Yaning Chen & Wei Chen & Yanyun Xiang & Jiao Li & Yuting Liu, 2022. "Recent Oasis Dynamics and Ecological Security in the Tarim River Basin, Central Asia," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    10. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    11. repec:plo:pone00:0184193 is not listed on IDEAS
    12. Sri Murniani Angelina Letsoin & David Herak & Fajar Rahmawan & Ratna Chrismiari Purwestri, 2020. "Land Cover Changes from 1990 to 2019 in Papua, Indonesia: Results of the Remote Sensing Imagery," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    13. Hao Wang & Guohua Liu & Zongshan Li & Xin Ye & Bojie Fu & Yihe Lü, 2017. "Analysis of the Driving Forces in Vegetation Variation in the Grain for Green Program Region, China," Sustainability, MDPI, vol. 9(10), pages 1-14, October.
    14. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    15. Peng Qin & Manying Bai, 2022. "Does oil price uncertainty matter in stock market volatility forecasting?," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-21, December.
    16. Fabina, Nicholas S. & Abbott, Karen C. & Gilman, R.Tucker, 2010. "Sensitivity of plant–pollinator–herbivore communities to changes in phenology," Ecological Modelling, Elsevier, vol. 221(3), pages 453-458.
    17. Xiumei Wang & Jianjun Dong & Taogetao Baoyin & Yuhai Bao, 2019. "Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    18. Christina Kassara & Christos Barboutis & Anastasios Bounas, 2025. "Favorable stopover sites and fuel load dynamics of spring bird migrants under a changing climate," Climatic Change, Springer, vol. 178(1), pages 1-19, January.
    19. Anna Yusa & Peter Berry & June J.Cheng & Nicholas Ogden & Barrie Bonsal & Ronald Stewart & Ruth Waldick, 2015. "Climate Change, Drought and Human Health in Canada," IJERPH, MDPI, vol. 12(7), pages 1-54, July.
    20. Portalier, S.M.J. & Candau, J.-N. & Lutscher, F., 2024. "Larval mortality from phenological mismatch can affect outbreak frequency and severity of a boreal forest defoliator," Ecological Modelling, Elsevier, vol. 493(C).
    21. A. Ogden & J. Innes, 2008. "Climate change adaptation and regional forest planning in southern Yukon, Canada," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 13(8), pages 833-861, October.

    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:17:y:2025:i:8:p:3640-:d:1636986. 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.