IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i10p4332-d362766.html
   My bibliography  Save this article

Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method

Author

Listed:
  • Cláudia M. Viana

    (Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal)

  • Jorge Rocha

    (Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal)

Abstract

The present study used the official Portuguese land use/land cover (LULC) maps ( Carta de Uso e Ocupação do Solo , COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly different between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (−3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.

Suggested Citation

  • Cláudia M. Viana & Jorge Rocha, 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4332-:d:362766
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/10/4332/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/10/4332/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Deng, Xiangzheng & Huang, Jikun & Rozelle, Scott & Uchida, Emi, 2008. "Growth, population and industrialization, and urban land expansion of China," Journal of Urban Economics, Elsevier, vol. 63(1), pages 96-115, January.
    2. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    3. 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.
    4. Yan Liu & Yongjiu Feng, 2016. "Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    5. Bruno M. Meneses & Eusébio Reis & Susana Pereira & Maria J. Vale & Rui Reis, 2017. "Understanding Driving Forces and Implications Associated with the Land Use and Land Cover Changes in Portugal," Sustainability, MDPI, vol. 9(3), pages 1-20, February.
    6. Brigite Botequim & Manuel Arias-Rodil & Jordi Garcia-Gonzalo & Andreia Silva & Susete Marques & José G. Borges & Maria Manuela Oliveira & Margarida Tomé, 2017. "Modeling Post-Fire Mortality in Pure and Mixed Forest Stands in Portugal—A Forest Planning-Oriented Model," Sustainability, MDPI, vol. 9(3), pages 1-23, March.
    7. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    8. Iman Rousta & Md Omar Sarif & Rajan Dev Gupta & Haraldur Olafsson & Manjula Ranagalage & Yuji Murayama & Hao Zhang & Terence Darlington Mushore, 2018. "Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)," Sustainability, MDPI, vol. 10(12), pages 1-25, November.
    9. Gomes, Eduardo & Abrantes, Patrícia & Banos, Arnaud & Rocha, Jorge, 2019. "Modelling future land use scenarios based on farmers’ intentions and a cellular automata approach," Land Use Policy, Elsevier, vol. 85(C), pages 142-154.
    10. Kabir Uddin & Mir Abdul Matin & Sajana Maharjan, 2018. "Assessment of Land Cover Change and Its Impact on Changes in Soil Erosion Risk in Nepal," Sustainability, MDPI, vol. 10(12), pages 1-20, December.
    11. Mertens, Benoit & Sunderlin, William D. & Ndoye, Ousseynou & Lambin, Eric F., 2000. "Impact of Macroeconomic Change on Deforestation in South Cameroon: Integration of Household Survey and Remotely-Sensed Data," World Development, Elsevier, vol. 28(6), pages 983-999, June.
    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. Martina Venturi & Francesco Piras & Federica Corrieri & Beatrice Fiore & Antonio Santoro & Mauro Agnoletti, 2021. "Assessment of Tuscany Landscape Structure According to the Regional Landscape Plan Partition," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Eduardo Gomes, 2020. "Sustainable Population Growth in Low-Density Areas in a New Technological Era: Prospective Thinking on How to Support Planning Policies Using Complex Spatial Models," Land, MDPI, vol. 9(7), pages 1-14, July.
    3. Juliana Mio de Souza & Paulo Morgado & Eduarda Marques da Costa & Luiz Fernando de Novaes Vianna, 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    4. Francesco Piras & Beatrice Fiore & Antonio Santoro, 2022. "Small Cultural Forests: Landscape Role and Ecosystem Services in a Japanese Cultural Landscape," Land, MDPI, vol. 11(9), pages 1-20, September.

    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. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    2. Yongjiu Feng & Jiafeng Wang & Xiaohua Tong & Yang Liu & Zhenkun Lei & Chen Gao & Shurui Chen, 2018. "The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    3. Luoman Pu & Jiuchun Yang & Lingxue Yu & Changsheng Xiong & Fengqin Yan & Yubo Zhang & Shuwen Zhang, 2021. "Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050," IJERPH, MDPI, vol. 18(3), pages 1-21, January.
    4. Youjung Kim & Galen Newman & Burak Güneralp, 2020. "A Review of Driving Factors, Scenarios, and Topics in Urban Land Change Models," Land, MDPI, vol. 9(8), pages 1-22, July.
    5. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    6. 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.
    7. Nij Tontisirin & Sutee Anantsuksomsri, 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    8. Ivan Marić & Lovre Panđa & Josip Faričić & Ante Šiljeg & Fran Domazetović & Tome Marelić, 2022. "Long-Term Assessment of Spatio-Temporal Landuse/Landcover Changes (LUCCs) of Ošljak Island (Croatia) Using Multi-Temporal Data—Invasion of Aleppo Pine," Land, MDPI, vol. 11(5), pages 1-38, April.
    9. Silvia Tobias & Bronwyn Price, 2020. "How Effective Is Spatial Planning for Cropland Protection? An Assessment Based on Land-Use Scenarios," Land, MDPI, vol. 9(2), pages 1-20, February.
    10. Jinyao Lin & Tongli Chen & Qiazi Han, 2018. "Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    11. Juliana Mio de Souza & Paulo Morgado & Eduarda Marques da Costa & Luiz Fernando de Novaes Vianna, 2022. "Modeling of Land Use and Land Cover (LULC) Change Based on Artificial Neural Networks for the Chapecó River Ecological Corridor, Santa Catarina/Brazil," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    12. Lü, Da & Gao, Guangyao & Lü, Yihe & Xiao, Feiyan & Fu, Bojie, 2020. "Detailed land use transition quantification matters for smart land management in drylands: An in-depth analysis in Northwest China," Land Use Policy, Elsevier, vol. 90(C).
    13. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    14. Haiwen Zhou, 2013. "The Choice of Technology and Rural-Urban Migration in Economic Development," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 8(3), pages 337-361, September.
    15. Bin Huang & Zaijian Yuan & Mingguo Zheng & Yishan Liao & Kim Loi Nguyen & Thi Hong Nguyen & Samran Sombatpanit & Dingqiang Li, 2022. "Soil and Water Conservation Techniques in Tropical and Subtropical Asia: A Review," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
    16. Ortuño-Padilla, Armando & Fernández-Aracil, Patricia, 2013. "Impact of fuel price on the development of the urban sprawl in Spain," Journal of Transport Geography, Elsevier, vol. 33(C), pages 180-187.
    17. 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.
    18. Dai, Jiangyu & Wu, Shiqiang & Han, Guoyi & Weinberg, Josh & Xie, Xinghua & Wu, Xiufeng & Song, Xingqiang & Jia, Benyou & Xue, Wanyun & Yang, Qianqian, 2018. "Water-energy nexus: A review of methods and tools for macro-assessment," Applied Energy, Elsevier, vol. 210(C), pages 393-408.
    19. Xu, Tingting & Gao, Jay & Li, Yuhua, 2019. "Machine learning-assisted evaluation of land use policies and plans in a rapidly urbanizing district in Chongqing, China," Land Use Policy, Elsevier, vol. 87(C).
    20. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.

    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:10:p:4332-:d:362766. 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.