Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam 10.32526/ennrj/21/20230086
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Abstract
The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km2 and 9.36 km2, respectively; a decrease in forest land of 55.61 km2, and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km2 while built-up and agricultural land will increase by 14.07 km2 and 43.61 km2, respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.
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References
Aburas MM, Ho YM, Pradhan B, Salleh AH, Alazaiza MY. Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model. Arabian Journal of Geosciences 2021;14(2):Article No. 131.
Aburas MM, Ho YM, Ramli MF, Ash’aari ZH. Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation 2017; 59:65-78.
Ai B, Xie D, Ma S, Jiang H. An EasyCA model with few steady variables and clone stamp strategy for simulation of urban growth in metropolitan areas. Ecological Modelling 2022;468: Article No.109950.
Al-Darwish Y, Ayad H, Taha D, Saadallah D. Predicting the future urban growth and it’s impacts on the surrounding environment using urban simulation models: Case study of Ibb City-Yemen. Alexandria Engineering Journal 2018;57(4):2887-95.
Alsharif AA, Pradhan B. Urban sprawl analysis of Tripoli Metropolitan City (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing 2014;42:149-63.
Arsanjani JJ, Helbich M, Kainz W, Boloorani AD. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation 2013; 21:265-75.
Arsanjani JJ, Kainz W, Mousivand AJ. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: The case of Tehran. International Journal of Image and Data Fusion 2011;2(4):329-45.
Azadi H, Ho P, Hasfiati L. Agricultural land conversion drivers: A comparison between less developed, developing and developed countries. Land Degradation and Development 2011;22(6):596-604.
Azizi P, Soltani A, Bagheri F, Sharifi S, Mikaeili M. An integrated modelling approach to urban growth and land use/cover change. Land 2022;11(10):Article No. 1715.
Castella JC, Verburg PH. Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecological Modelling 2007;202(3-4):410-20.
Cheng LL, Tian C, Yin TT. Identifying driving factors of urban land expansion using Google Earth Engine and machine-learning approaches in Mentougou District, China. Scientific Reports 2022;12(1):Article No. 16248.
Christensen M, Arsanjani JJ. Stimulating implementation of sustainable development goals and conservation action: Predicting future land use/cover change in Virunga National Park, Congo. Sustainability 2020;12(4):Article No. 1570.
Devendran AA, Gnanappazham L. Comparison of urban growth modeling using deep belief and neural network based cellular automata model: A case study of Chennai metropolitan area, Tamil Nadu, India. Journal of Geographic Information System 2019;11(1):1-6.
Ebrahimipour A, Saadat M, Farshchin A. Prediction of urban growth through cellular automata-Markov chain. Bulletin de la Société Royale des Sciences de Liège 2016;85:824-39.
Falah N, Karimi A, Harandi AT. Urban growth modeling using cellular automata model and AHP (Case study: Qazvin City). Modeling Earth Systems and Environment 2020;6:235-48.
Fan P, Ouyang Z, Nguyen DD, Nguyen TT, Park H, Chen J. Urbanization, economic development, environmental and social changes in transitional economies: Vietnam after Doimoi. Landscape and Urban Planning 2019;187:145-55.
Fathizad H, Rostami N, Faramarzi M. Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment 2015;187:Article No. 629.
General Statistics Office (GSO). Completed results of the 2019 Viet Nam population and housing census [Internet]. 2020 [cited 2023 Mar 5]. Available from: https://www.gso.gov.vn/en/data-and-statistics/2020/11/completed-results-of-the-2019-viet-nam-population-and-housing-census/ (in Vietnamese).
Hassan MI, Elhassan SM. Modelling of urban growth and planning: A critical review. Journal of Building Construction and Planning Research 2020;8(4):245-62.
Hu Z, Lo CP. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems 2007;31(6):667-88.
Huang B, Zhang L, Wu B. Spatiotemporal analysis of rural-urban land conversion. International Journal of Geographical Information Science 2009;23(3):379-98.
Huong HT, Pathirana A. Urbanization and climate change impacts on future urban flooding in Can Tho City, Vietnam. Hydrology and Earth System Sciences 2013;17(1):379-94.
Kamusoko C, Aniya M, Adi B, Manjoro M. Rural sustainability under threat in Zimbabwe-simulation of future land use/cover changes in the Bindura District based on the Markov-cellular automata model. Applied Geography 2009;29(3):435-47.
Lam Dong online. How to move the greenhouses out of Da Lat’s downtown? [Internet]. 2023 [cited 2023 Jun 15]. Available from: http://baolamdong.vn/toa-soan-ban-doc/202301/giai-phap-nao-dua-nha-kinh-ra-khoi-noi-o-da-lat-bb05c14/ (in Vietnamese).
Lam Dong Statistics Office (LDSO). Lam Dong Statistical Yearbook 2022. Ha Noi, Viet Nam: Statistical Publishing; 2023 (in Vietnamese).
Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, et al. The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change 2001;11(4):261-9.
Lin YP, Chu HJ, Wu CF, Verburg PH. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling-a case study. International Journal of Geographical Information Science 2011;25(1):65-87.
Mas JF, Kolb M, Paegelow M, Olmedo MT, Houet T. Modelling Land use/cover changes: A comparison of conceptual approaches and softwares. Environmental Modelling and Software 2014;51:94-111.
Mauro G. Rural-urban transition of Hanoi (Vietnam): Using Landsat imagery to map its recent peri-urbanization. ISPRS International Journal of Geo-Information 2020;9(11):Article No. 669.
Ministry of Agriculture and Rural Development of Vietnam (MARD). Agricultural Land-use Manual, Vol. 3. Land Resources in Vietnam: Land-use Current Status and Potential. Ha Noi, Vietnam: Scientific and Technical Publishing; 2015 (in Vietnamese).
Ministry of Construction (MC). Urban development in Vietnam - problems in the next period [Internet]. 2022 [cited 2023 Mar 6]. Available from: https://moc.gov.vn/tl/tin-tuc/74077/phat-trien-do-thi-viet-nam-nhung-van-de-dat-ra-trong-giai-doan-toi.aspx (in Vietnamese).
Ministry of Finance (MF). Agricultural land use and food security [Internet]. 2009 [cited 2023 Mar 6]. Available from: https://mof.gov.vn/webcenter/portal/btcvn/pages_r/l/tin-bo-tai-chinh?dDocName=BTC340505 (in Vietnamese).
Müller D, Zeller M. Land use dynamics in the central highlands of Vietnam: A spatial model combining village survey data with satellite imagery interpretation. Agricultural Economics 2002;27(3):333-54.
Musa SI, Hashim M, Reba MN. A review of geospatial-based urban growth models and modelling initiatives. Geocarto International 2017;32(8):813-33.
Mustak S, Baghmar NK, Singh SK, Srivastava PK. Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement. Geocarto International 2022;37(27):18275-303.
Nguyen TM, Lin TH, Chan HP. The environmental effects of urban development in Hanoi, Vietnam from satellite and meteorological observations from 1999-2016. Sustainability 2019;11(6):Article No. 1768.
Nong Y, Du Q. Urban growth pattern modeling using logistic regression. Geo-Spatial Information Science 2011;14(1):62-7.
Ntakirutimana A, Vansarochana C. Asessment and prediction of land use/land cover change in the National Capital of Burundi using multi-temporary landsat data and cellular automata-Markov chain model. Environment and Natural Resources Journal 2021;19(5):413-26.
Olmedo MT, Pontius Jr RG, Paegelow M, Mas JF. Comparison of simulation models in terms of quantity and allocation of land change. Environmental Modelling and Software 2015;69:214-21.
Park S, Jeon S, Kim S, Choi C. Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and Urban Planning 2011;99(2):104-14.
Perumal K, Bhaskaran R. Supervised classification performance of multispectral images. Journal of Computing 2010;2(2):124-9.
Pham HM, Yamaguchi Y. Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi, Vietnam. International Journal of Remote Sensing 2011;32(7):1901-15.
Pham TT, Hoang TL, Nguyen DT, Dao TL, Ngo HC, Pham VH. The Context of REDD+ in Vietnam: Drivers, Agents and Institutions. Bogor, Indonesia: Center for International Forestry Research; 2019.
Pham VC, Pham TT, Tong TH, Nguyen TT, Pham NH. The conversion of agricultural land in the peri-urban areas of Hanoi (Vietnam): Patterns in space and time. Journal of Land Use Science 2015;10(2):224-42.
Pontius Jr RG, Millones M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing 2011;32(15):4407-29.
Pontius RG. Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing 2000;66(8):1011-6.
Pontius RG. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing 2002;68(10):Article No. 1041.
Research Systems, Inc., (RSI). ENVI Tutorials. Portland, USA: StoneTablet; 2001.
Richards JA. Supervised classification techniques. In: Richards JA editor. Remote Sensing Digital Image Analysis. Switzerland: Springer; 2022. p. 263-367.
Sang L, Zhang C, Yang J, Zhu D, Yun W. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling 2011;54(3-4):938-43.
Shafizadeh-Moghadam H, Tayyebi A, Ahmadlou M, Delavar MR, Hasanlou M. Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. Computers, Environment and Urban Systems 2017;65:28-40.
Sohl TL, Claggett PR. Clarity versus complexity: Land-use modeling as a practical tool for decision-makers. Journal of Environmental Management 2013;129:235-43.
Son NT, Chen CF, Chen CR, Chang LY, Thanh BX. Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam. Journal of Applied Remote Sensing 2012;6(1):Article No. 063543.
Stephen JL. Dynamics of land cover and land use changes in the upper Ca river basin of Nghe An, Vietnam. Japanese Journal of Southeast Asian Studies 2009;47(3):287-308.
Tranh NH. Ancient Dalat. Ho Chi Minh City, Vietnam: HCMC General Publishing; 2001 (in Vietnamese).
Tro T. Dalat: Highland City. Ho Chi Minh City, Vietnam: HCMC General Publishing; 1993 (in Vietnamese).
Tsagkis P, Bakogiannis E, Nikitas A. Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities. Sustainable Cities and Society 2023;89:Article No. 104337.
Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SS. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management 2002; 30:391-405.
Wang M, Cai L, Xu H, Zhao S. Predicting land use changes in northern China using logistic regression, cellular automata, and a Markov model. Arabian Journal of Geosciences 2019;12:1-2.
Wang R, Murayama Y, Morimoto T. Scenario simulation studies of urban development using remote sensing and GIS. Remote Sensing Applications: Society and Environment 2021a;22: Article No. 100474.
Wang SW, Munkhnasan L, Lee WK. Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges 2021b;2:Article No. 100017.
World Bank (WB). Vietnam urbanization review: Technical assistance report [Internet]. 2011 [cited 2023 Mar 8]. Available from: https://openknowledge.worldbank.org/handle /10986/2826.
Xie C, Huang B, Claramunt C, Chandramouli C. Spatial logistic regression and GIS to model rural-urban land conversion. Proceedings of the PROCESSUS 2nd International Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation Models: Frameworks, Models and Applications; 2005 Jun 12; University of Toronto: Canada; 2005.
Yeh AGO, Li X, Xia C. Cellular automata modeling for urban and regional planning. In: Shi W, Goodchild MF, Batty M, Kwan MP, Zhang A, editors. Urban Informatics. The Urban Book Series. Singapore: Springer; 2021. p. 865-83.