Prediksi perubahan tutupan lahan di DAS Wae Batu Merah, Kota Ambon menggunakan Cellular Automata Markov Chain
The Wae Batu Merah watershed is located in the center of Ambon City and has the potential to trigger land-use change which will have an impact on decreasing water quality, water pollution, flooding, and erosion which will increase in the future. The objective of this study was to analyze land-cover changes in the Wae Batu Merah watershed in 2012, 2017, and 2022 and predict land cover in 2031. The method used was Cellular Automata Markov Chain (CA-MC) with 5 factors driving land cover changes including slope, elevation, distance from the river, point of interest (POI), and distance from the road. The results showed that from 2012, 2017, 2022, and 2031 the residental and open land-cover continued to increase in area, in contrast to the land-cover of agricultural areas and non-agricultural areas which a decrease in area. The kappa accuracy value in the model reaches 91%. The results of the model year 2031 show that residential land cover types have an area of 392.09 ha, open land has an area of 35.31 ha, agricultural areas have an area of 104.59 ha, non-agricultural areas have an area of 118.35 and aquatic land cover types have an area of 4.69 ha.
Aquilué N, Cáceres M, Fortin MJ, Fall A and Brotons L. 2017. A spatial allocation procedure to model land-use/land-cover changes: Accounting for occurrence and spread processes. Ecological Modelling 344:73–86.
[BPS] Badan Pusat Statistik. 2021. Kota Ambon dalam angka 2021. Badan Pusat Statistik. Kota Ambon.
[BSN] Badan Standarisasi Nasional. 2010. SNI 7645-2010 tentang klasifikasi penutup lahan. Badan Standarisasi Nasional. Jakarta.
Bandjar A, Osok RM, Rachman G dan Sutapa IW. 2016. Strategi, mapping resiko, dan implementasi adaptasi perubahan iklim dan pengurangan risiko bencana untuk ketahanan di Kecamatan Sirimau Kota Madya Ambon. BIMAFIKA: Jurnal MIPA, Kependidikan dan Terapan 6(1).
Dutta DK, Rahman A, Paul S and Kundu A. 2019. Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi. Environmental Monitoring and Assessment 191(551).
Ghosh P, Mukhopadhyay A, Chanda A, Mondal P, Akhand A, Mukherjee S, Nayak SK, Ghosh S, Mitra D, Ghosh T and Hazra S. 2017. Application of cellular automata and Markov-chain model in geospatial environmental modeling- a review. Remote Sensing Applications: Society and Environment 5:64–77.
Girma R, Fürst C and Moges A. 2022. Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges 6:1-15.
Irawan IA, Supriatna S, Manessa MDM and Ristya Y. 2019. Prediction model of land cover changes using the cellular automata – markov chain affected by the BOCIMI Toll Road in Sukabumi Regency [Proceeding]. The 1st International Conference on Geodesy, Geomatics, and Land Administration 2019:247-256.
Kaihena M, Talakua CM, Pagaya J and Talakua SM. 2021. Analysis of water pollution in microbiology aspect of some watersheds at Ambon City, Maluku Province. IOP Conference Series Earth and Environmental Science 805(1):1-13.
Kapitza S, Golding N and Wintle BA. 2022. A fractional land use change model for ecological applications. Environmental Modelling & Software 147.
Lisanyoto L, Supriatna and Sumadio W. 2019. Spatial model of settlement expansion and its suitability to the landscapes in Singkawang City, West Kalimantan Province. IOP Conference Series Earth and Environmental Science 338: 12034.
Liu Q, Niu J, Wood JD and Kang S. 2022. Spatial optimization of cropping pattern in the upper-middle reaches of the Heihe River basin, Northwest China. Agricultural Water Management 264: 107479.
Mohamed A and Worku H. 2019. Quantification of the land use/land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone. Journal of Urban Management 8(1): 145–158.
Mustafa A, Ebaid A, Omrani H and McPhearson T. 2021. A multi-objective Markov Chain Monte Carlo cellular automata model: simulating multi-density urban expansion in NYC. Computers, Environment and Urban Systems 87: 101602.
Mwabumba M, Yadav BK, Rwiza MJ, Larbi I and Twisa S. 2022. Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model. Current Research in Environmental Sustainability 4: 100126.
Namara I, Hartono DM, Latief Y and Moersidik SS. 2022. Policy development of river water quality governance toward land use dynamics through a risk management approach. Journal of Ecological Engineering 23(2): 25–33.
Osok RM, Talakua SM dan Supriadi D. 2018. Penetapan kelas kemampuan lahan dan arahan rehabilitasi lahan Das Wai Batu Merah Kota Ambon Provinsi Maluku. Agrologia 7(1): 32-41.
Palmate SS, Wagner PD, Fohrer N and Pandey A. 2022. Assessment of uncertainties in modelling land use change with an integrated cellular automata–markov chain model. environmental Modeling & Assessment 27(2): 275–293.
Peter A, Zachariah B, Damuut LP and Abdulkadir S. 2021. Efficient traffic control system using fuzzy logic with priority [Proceeding]. International Conference on Information and Communication Technology and Applications 1350: 660–674.
Pratami M, Susiloningtyas D and Supriatna S. 2019. Modelling cellular automata for the development of settlement area Bengkulu City. IOP Conference Series Earth and Environmental Science 311(1):12073.
Ross ER and Randhir TO. 2022. Effects of climate and land use changes on water quantity and quality of coastal watersheds of Narragansett Bay. Science of The Total Environment 807(3):151082.
Shang C and Wu J. 2022. A legendary landscape in peril: Land use and land cover change and environmental impacts in the Wulagai River Basin, Inner Mongolia. Journal of Environmental Management 301:113816.
Supriatna S, Supriatna J, Koestoer RH and Takarina ND. 2016. Spatial dynamics model for sustainability landscape in Cimandiri Estuary, West Java, Indonesia [Proceeding]. Procedia - Social and Behavioral Sciences 227: 19–30.
Supriatna S, Fauzia S, Marko K, Manessa MDM and Ristya Y. 2020. Spatial dynamics of tsunami prone areas in Pariaman City, West Sumatera. Journal of Computational and Theoretical Nanoscience 17(2):1474–1491.
Tan S, Liu Q and Han S. 2022. Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China. Journal of Environmental Management 301:113778.
Tian G, Ma B, Xu X, Liu X, Xu L, Liu X, Xiao L and Kong L. 2016. Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—A case study of Tianjin metropolitan region, China. Ecological Indicators 70:439–450.
Wada CA, Pongkijvorasin S and Burnett KM. 2020. Mountain-to-sea ecological-resource management: Forested watersheds, coastal aquifers, and groundwater dependent ecosystems. Resource and Energy Economics 59(6):101146.
Xu D, Lyon SW, Mao J, Dai H and Jarsjö J. 2020. Impacts of multi-purpose reservoir construction, land-use change and climate change on runoff characteristics in the Poyang Lake basin, China. Journal of Hydrology: Regional Studies 29:100694.
Yu W, Zang S, Wu C, Liu W and Na X. 2011. Analyzing and modeling land use land cover change (LUCC) in the Daqing City, China. Applied Geography 31(2):600–608.
Zadeh LA. 1994. Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37(3):77–84.
Zhou Y, Wu T and Wang Y. 2022. Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: A case study of Hangzhou. Science of The Total Environment 807:150813.
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