Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Dự đoán tăng trưởng đô thị cho quản lý đô thị bền vững bằng mô hình chuỗi Markov: Nghiên cứu về đô thị Purulia, Tây Bengal, Ấn Độ
Tóm tắt
Sự tập trung dân số nhanh chóng, sự phát triển xây dựng chưa được tổ chức, sự thiếu thông tin về sự mở rộng đô thị và biến đổi sử dụng đất là những thách thức trong quy hoạch không gian đô thị tại các thị trấn vùng sâu vùng xa như huyện Purulia, Tây Bengal. Để đảm bảo quản lý đô thị bền vững cho đô thị Purulia, nghiên cứu hiện tại được thực hiện nhằm phân tích mô hình tăng trưởng đô thị và dự đoán xác suất mở rộng xây dựng trong tương lai cho các chiến lược quản lý đô thị bền vững cũng như vì phúc lợi của môi trường vật lý và con người trong tương lai. Thị trấn Purulia là thị trấn duy nhất chiếm ưu thế tại huyện này về dân số và cơ sở hạ tầng đô thị. Hình ảnh từ vệ tinh được sử dụng để tạo ra bản đồ LULC cho các năm 1990, 2000, 2010 và 2020. Một mô hình dự đoán tăng trưởng đô thị kết hợp giữa Markov và tự động hóa tế bào (CA) đã được sử dụng để dự báo mở rộng xây dựng của thị trấn vào năm 2030. Để xem xét các loại LULC khác, mô hình MC-CA đã được tích hợp vào ma trận xác suất chuyển tiếp để xác định xác suất chuyển đổi đất xây dựng từ các loại LULC khác. Kết quả của sự chuyển đổi LULC cho thấy xu hướng diện tích xây dựng tăng nhanh từ năm 1990 đến 2020 và xác suất cao nhất là từ đất nông nghiệp, tiếp theo là sự phủ cây xanh và mặt nước. Nghiên cứu chỉ ra rằng mô hình tăng trưởng đô thị sẽ lấn chiếm vào các vùng ngoại ô gần gũi của đô thị trong những thập kỷ tới. Đối với mục đích quy hoạch phát triển đô thị tại các khu vực thiếu thốn về kinh tế-xã hội như huyện Purulia, các chiến lược quy hoạch đã đề xuất có thể được sử dụng tốt nhất bởi các nhà quy hoạch và quản lý.
Từ khóa
Tài liệu tham khảo
Agapiou, A., Alexakis, D. D., Lysandrou, V., Sarris, A., Cuca, B., Themistocleous, K., & Hadjimitsis, D. G. (2015). Impact of urban sprawl on cultural heritage monuments: The case study of Paphos area in Cyprus. Journal of Cultural Heritage, 16, 671–680.
Ahmed, B., Kamruzzaman, Md., & Zhu, X. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka Bangladesh. Remote Sensing, 5, 5969–5998. https://doi.org/10.3390/rs5115969
Aliani, H., Malmir, M., Sourodi, M., & Kafaky, S. (2019). Change detection and prediction of urban land use changes by CA–Markov model (case study: Talesh County). Environmental Earth Sciences., 78(546), 1–12.
Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S., Al-Sharif, A. (2012) Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences, 1–13.
Al-sharif, A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7, 4291–4301. https://doi.org/10.1007/s12517-013-1119-7
Araya, Y. H., & Cabral, P. (2010). Analysis and modelling of urban land cover change in Setubal and Sesimbra Portugal. Remote Sensing, 2, 1549–1563.
Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275.
Basse, R. M., Omrani, H., Charif, O., Gerber, P., & Bodis, K. (2014). Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 5, 160–171.
Berberoglu, S., Akın, A., & Clarke, K. (2016). Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: A comparative approach. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2016.04.017
Bharath, H. A., Chandan, M. C., Vinay, S., Ramachandra, T., & V. (2017). Modelling the growth of two rapidly urbanizing Indian cities. Journal of Geomatics., 11(12), 149–166.
Biswas, M., Banerji, S., & Mitra, D. (2020). Land-use–land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata. Environment, Development and Sustainability., 22, 4341–4360. https://doi.org/10.1007/s10668-019-00387-4
Bozkaya, A., Balcik, F., Goksel, C., & Esbah, H. (2015). Forecasting land-cover growth using remotely sensed data: A case study of the Igneada protection area in Turkey. Environmental Monitoring and Assessment, 187(59), 1–18. https://doi.org/10.1007/s10661-015-4322-z
Chang, F., Ko, C., Yang, P., & Chen, K. (2017). Carbon sequestration and substation potential of subtropical mountain Sugi plantation forests in Central Taiwan. Journal of Cleaner Production, 167, 1099–1105.
District census handbook, Purulia. 2011. Village & town directory, Directorate of census operations, West Bengal, Census of India, 2011. Series-20. Part XII-A.
Etemadi, H., Smoak, J., & Karami, J. (2018). Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change. Environmental Earth Sciences., 77(208), 1–13. https://doi.org/10.1007/s12665-018-7392-8
Fan, F., Wang, Y., & Wang, Z. (2008). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental Monitoring and Assessment, 137, 127–147. https://doi.org/10.1007/s10661-007-9734-y
Fathizad, H., Rostami, N., & Faramarzi, M. (2015). Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment, 187(629), 1–12. https://doi.org/10.1007/s10661-015-4805-y
Fei, L., Shuwen, Z., Kun, V., Jiuchun, Y., Qing, W., & Liping, C. (2015). The relationships between land use change and demographic dynamics in western Jilin province. Journal of Geographical Sciences, 25(5), 617–636. https://doi.org/10.1007/s11442-015-1191-x
Fu, X., Wang, X., & Yang, Y. J. (2018). Deriving suitability factors for CA-Markov land use simulation model based on local historical data. Journal of Environmental Management, 206, 10–19.
Gashaw, T., Tulu, T., Argaw, M., & Worqlul, A. (2017). Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Environmental System Research. https://doi.org/10.1186/s40068-017-0094-5
Ghosh, D., Karmakar, M., Banerjee, M., & Mandal, M. (2020). Evaluating the rate of change and predicting the future scenario of spatial pattern using Markov chain model: a study from Baghmundi C.D. Block of Purulia district, West Bengal. Applied Geomatics. https://doi.org/10.1007/s12518-020-00345-0
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modelling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222, 3761–3772.
Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112.
Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Environmental Earth Sciences., 79(60), 1–12.
Karimi, H., Jafarnezhad, J., Khaledi, J., & Ahmadi, P. (2018). Monitoring and prediction of land use/land cover changes using CA-Markov model: A case study of Ravansar County in Iran. Arabian Journal of Geosciences., 11(592), 1–19. https://doi.org/10.1007/s12517-018-3940-5
Keshtkar, H., & Voigt, W. (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2(10), 1–13.
Kumar, A., & Pandey, A. (2016). Geoinformatics based Groundwater Potential Assessment in Hard Rock Terrain of Ranchi Urban Agglomeration, Jharkhand (India) using MCDM-AHP Techniques. Groundwater Sustainable Development, 27(41), 2–3.
Kumar, S., Radhakrishnan, N., & Mathew, S. (2014). Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk., 5, 145–156. https://doi.org/10.1080/19475705.2013.795502
Lal, K., Kumar, D., & Kumar, A. (2017). Spatio-temporal landscape modelling of urban growth patterns in Dhanbad Urban Agglomeration, India using geoinformatics techniques. The Egyptian Journal of Remote Sensing and Space Sciences. https://doi.org/10.1016/j.ejrs.2017.01.003
Liu, Y., Liu, Y., Chen, Y., & Long, H. (2010). The process and driving forces of rural hollowing in China under rapid urbanization. Journal of Geographical Sciences, 20, 876–888.
Liu, Y., Fan, P., Yue, W., & Song, Y. (2018). Impacts of land finance on urban sprawl in China: The case of Chongqing. Land Use Policy. 420–432.
Long, B., & Giri, C. (2011). Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors. 29–72
Lu, Q., Chang, N., Joyce, J., Chen, A., Savic, D., & Djordjevic, S. (2017). Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model. Computers, Environment and Urban Systems. https://doi.org/10.1016/j.compenvurbsys.2017.11.006
Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model. Environmental Monitoring and Assessment, 191(68), 1–19.
Maithani, S. (2010b). Cellular automata based model of urban spatial growth. Journal of the Indian Society of Remote Sensing, 38(4), 604–610.
Maithani, S., 2010a. Application of cellular automata and GIS techniques in urban growth modelling: A new perspective. Institute of Town Planners, India Journal. 36–49.
Maithani, S. (2017). Calibration of a multi-criteria evaluation based cellular automata model for Indian cities having varied growth patterns. Journal of the Indian Society of Remote Sensing. 1–12.
Maity, B., Mallick, S. K., & Rudra, S. (2021). Integration of urban expansion with hybrid road transport network development within Haldia Municipality West Bengal, Egyptian. Journal of Remote Sensing and Space Science. https://doi.org/10.1016/j.ejrs.2020.10.005
Maity, B., Mallick, S.K., Das, P., Rudra, S. (2022) Comparative analysis of groundwater potentiality zone using Fuzzy-AHP, frequency ratio and Bayesian weights of evidence methods. Applied Water Science, 12, 63. https://doi.org/10.1007/s13201-022-01591-w
Maity, B., Mallick, S.K., & Rudra, S. (2020) Spatiotemporal dynamics of urban landscape in Asansol Municipal Corporation, West Bengal, India: A geospatial analysis. Geojournal, https://doi.org/10.1007/s10708-020-10315-z
Mishra, V., & Rai, P. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar) India. Arabian Journal of Geosciences, 9(249), 1–18.
Mishra, V. N., Rai, P. K., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar) India. Journal of the Geographical Institute" Jovan Cvijic., 64, 111–127.
Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99, 141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001
Moghadam, H., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geographysics. https://doi.org/10.1016/j.apgeog.2013.01.009
Mondal, B., Das, D., & Bhatta, B. (2017). Integrating cellular automata and Markov techniques to generate urban development potential surface: A study on Kolkata agglomeration. Geocarto International., 32(4), 401–419. https://doi.org/10.1080/10106049.2016.1155656
Mondal., B, Das., DN, Dolui., G. (2015). Modeling spatial variation of explanatory factors of urban expansion of Kolkata: A geographically weighted regression approach. Model Earth System Environment, 1(4), 29. https://doi.org/10.1007/s40808-015-0026-1
Moradi, F., Kaboli, H., & Lashkarara, B. (2020). Projection of future land use/cover change in the Izeh-Pyon Plain of Iran using CA-Markov model. Arabian Journal of Geosciences., 13(998), 1–17.
Mosammam, H., Nia, J., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms the case of Qom City. The Egyptian Journal of Remote Sensing and Space Sciences. 103–116. http://creativecommons.org/licenses/by-nc-nd/4.0/.
Mugagga, F., Kakembo, V., & Buyinza, M. (2012). Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. CATENA, 90, 39–46.
Mumtaz, F., Tao, Y., Leeuw, G., Zhao, L., Fan, C., & Elnashar, A. (2020). Modeling Spatio-temporal land transformation and its associated impacts on land surface temperature (LST). Remote Sensing. https://doi.org/10.3390/rs12182987
Ozturk, D. (2017). Assessment of urban sprawl using Shannon’s entropy and fractal analysis: A case study of Atakum, Ilkadim and Canik (Samsun, Turkey). Journal of Environmental Engineering and Landscape Management., 25(03), 264–276.
Sarkar, A., & Chouhan, P. (2019). Dynamic simulation of urban expansion based on Cellular Automata and Markov Chain Model: A case study in Siliguri Metropolitan Area, West Bengal. Modelling Earth Systems and Environment. https://doi.org/10.1007/s40808-019-00626-7
Shaar, W., Gérard, J., Nehme, N., Lakiss, H., & Barakat, L. (2020). Application of modified cellular automata Markov chain model: Forecasting land use pattern in Lebanon. Modeling Earth Systems and Environment. 1–15.
Sharifi, A., & Hosseingholizadeh, M. (2019). The effect of rapid population growth on urban expansion and destruction of green space in Tehran from 1972 to 2017. Journal of Indian Society of Remote Sensing, 2019(47), 1063–1071.
Shikary, C., & Rudra, S. (2020). Measuring urban land use change and sprawl using geospatial techniques: A study on Purulia Municipality, West Bengal, India. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-020-01212-6
Siddiqui, A., Siddiqui, A., Maithani, S., Jha, A., Kumar, P., Srivastav, S. (2018). Urban growth dynamics of an Indian metropolitan using CA Markov and logistic regression. The Egyptian Journal of Remote Sensing and Space Sciences. 229–236.
Singh, S., Mustak, S., Srivastava, P., Szabo, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular Automata Markov chain models using earth observation datasets and geo-information. Environmental Processing, 2, 61–78.
Srivastava, A., Kumari, N., & Maza, M. (2020). Hydrological response to agricultural land use heterogeneity using variable infiltration capacity model. Water Resources Management, 34, 3779–3794. https://doi.org/10.1007/s11269-020-02630-4
Sun, H., Forsythe, W., & Waters, N. (2007). Modelling urban land use change and urban sprawl: Calgary, Alberta, Canada. Springer Science. 353–376.
Thapa, R., & Murayama, Y. (2009). Examining spatiotemporal urbanization patterns in Kathmandu Valley, Nepal: remote sensing and spatial metrics approaches. Remote Sensing, 1(3), 534–556. https://doi.org/10.3390/rs1030534
Thapa, R. B., & Murayama, Y. (2010). Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy processes. Applied Geography, 30(1), 70–83.
Triantakonstantis, D., & Mountrakis, G. (2012). Urban growth prediction: A review of computational models and human perceptions. Journal of Geographic Information System, 4, 555–587.
Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245. https://doi.org/10.1016/j.proenv.2012.01.117
Weinhold, D., & Reis, E. (2001). Model evaluation and causality testing in short panels: The case of infrastructure provision and population growth in the Brazilian Amazon. Journal of Regional Science, 41, 639–657.
Weng, Q. H. (2010). Remote sensing and GIS integration. McGraw-Hill.
Yang, X., Zheng, X.-Q., & Lv, L.-N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19. https://doi.org/10.1016/j.ecolmodel.2012.03.011
Zhang, Y., & Xie, H. (2019). Interactive relationship among urban expansion, economic development, and population growth since the reform and opening up in China: An analysis based on a vector error correction model. Land. https://doi.org/10.3390/land8100153
Zhang, Q., Ban, Y., Liu, J., & Hu, Y. (2011). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area China. Computing Environmental Urban System, 35, 126–139. https://doi.org/10.1016/j.compenvurbsys.2010.12.002