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Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata

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  • Griselda Vázquez-Quintero

    (Doctorado Institucional en Ciencias Agropecuarias y Forestales, Universidad Juárez del Estado de Durango, Boulevard del Guadiana #501, Ciudad Universitaria, Durango C.P. 34120, Mexico)

  • Raúl Solís-Moreno

    (Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Boulevard Durango #501, Valle del Sur, Durango C.P. 34120, Mexico)

  • Marín Pompa-García

    (Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Boulevard Durango #501, Valle del Sur, Durango C.P. 34120, Mexico)

  • Federico Villarreal-Guerrero

    (Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Periférico Fco. R. Almada Km 1, Chihuahua C.P. 31453, Mexico)

  • Carmelo Pinedo-Alvarez

    (Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Periférico Fco. R. Almada Km 1, Chihuahua C.P. 31453, Mexico)

  • Alfredo Pinedo-Alvarez

    (Facultad de Zootecnia y Ecología, Universidad Autónoma de Chihuahua, Periférico Fco. R. Almada Km 1, Chihuahua C.P. 31453, Mexico)

Abstract

The spatio-temporal analysis of land use changes could provide basic information for managing the protection, conservation and production of forestlands, which promotes a sustainable resource use of temperate ecosystems. In this study we modeled and analyzed the spatial and temporal dynamics of land use of a temperate forests in the region of Pueblo Nuevo, Durango, Mexico. Data from the Landsat images Multispectral Scanner (MSS) 1973, Thematic Mapper (TM) 1990, and Operational Land Imager (OLI) 2014 were used. Supervised classification methods were then applied to generate the land use for these years. To validate the land use classifications on the images, the Kappa coefficient was used. The resulting Kappa coefficients were 91%, 92% and 90% for 1973, 1990 and 2014, respectively. The analysis of the change dynamics was assessed with Markov Chains and Cellular Automata (CA), which are based on probabilistic modeling techniques. The Markov Chains and CA show constant changes in land use. The class most affected by these changes is the pine forest. Changes in the extent of temperate forest of the study area were further projected until 2028, indicating that the area of pine forest could be continuously reduced. The results of this study could provide quantitative information, which represents a base for assessing the sustainability in the management of these temperate forest ecosystems and for taking actions to mitigate their degradation.

Suggested Citation

  • Griselda Vázquez-Quintero & Raúl Solís-Moreno & Marín Pompa-García & Federico Villarreal-Guerrero & Carmelo Pinedo-Alvarez & Alfredo Pinedo-Alvarez, 2016. "Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata," Sustainability, MDPI, vol. 8(3), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:3:p:236-:d:64920
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    References listed on IDEAS

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    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.
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    2. Md Shihab Uddin & Badal Mahalder & Debabrata Mahalder, 2023. "Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    3. T. V. Ramachandra & Bharath Setturu, 2019. "Sustainable Management of Bannerghatta National Park, India, with the Insights in Land Cover Dynamics," FIIB Business Review, , vol. 8(2), pages 118-131, June.
    4. Wafaa Majeed Mutashar Al-Hameedi & Jie Chen & Cheechouyang Faichia & Biswajit Nath & Bazel Al-Shaibah & Ali Al-Aizari, 2022. "Geospatial Analysis of Land Use/Cover Change and Land Surface Temperature for Landscape Risk Pattern Change Evaluation of Baghdad City, Iraq, Using CA–Markov and ANN Models," Sustainability, MDPI, vol. 14(14), pages 1-31, July.

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