Modeling Future Carbon Stock Predictions Based on Land Use
https://doi.org/10.24057/2071-9388-2025-3684
Abstract
The considerable influence of extensive land use change on the increasing levels of carbon emissions has significant implications for the occurrence of a multitude of disasters. The objective of this research is to develop a predictive model of future carbon stocks based on land use type. The data set includes land use maps from 2014, 2018, and 2022, obtained through visual interpretation of Pleiades data and associated driving variables, including socio-economic, locational, physical, land, and spatial planning factors. To predict land use in relation to future carbon stock values, the Multilayer Perceptron Neural Network Markov Chain (MLPNN-MC) algorithm was employed. Research related to this modeling is capable of producing an accuracy rate of 98%. The results of the prediction demonstrate that by 2034, there will be a reduction in the area of land used with high to low carbon stock, with a decrease of 153.2 ha, which equates to a reduction in carbon stock of 9,050 tonnes C/ha. To reduce carbon emissions, it is essential to implement policies that regulate land use change, optimize forest management, and conserve mangrove ecosystems. The monitoring and prediction of future carbon stocks plays a pivotal role in climate change mitigation, enabling more targeted and measurable actions to be taken.
Keywords
Supporting agencies: The author wishes to express gratitude to the fieldwork participants from the Faculty of Geography, Universitas Gadjah Mada. In addition, the author would like to thank the government, the Kulon Progo Regency Land Office, and the Geospatial Information Agency for providing secondary data.
About the Authors
Westi Utami
Doctoral Program of Environmental Science Universitas Gadjah Mada ; Department of Land Management Sekolah Tinggi Pertanahan Nasional
Indonesia
Indonesia
Jl. Teknika Utara, Yogyakarta, 55284
Jl. Tata Bumi No. 5, Yogyakarta, 55293
Catur Sugiyanto
Faculty of Economics and Bussiness Universitas Gadjah Mada
Indonesia
Indonesia
Jl. Sosio Humaniora, Yogyakarta, 55281
Noorhadi Rahardjo
Faculty Geography Universitas Gadjah Mada
Indonesia
Indonesia
Jl. Kaliurang, Yogyakarta, 55281
Nurhadi .
Faculty of Social Science and Political Science, Universitas Gadjah Mada
Indonesia
Indonesia
Jl. Sosio Yusticia No.1, Yogyakarta, 55281
References
1. Abbass K., Qasim M.Z., Song H., Murshed M., Mahmood H., & Younis I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. In Environmental Science and Pollution Research 29(28), 42539–42559, Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s11356-022-19718-6
2. Achmad A., Ramli I., Sugiarto S., Irzaidi I., & Izzaty A. (2024). Assessing and forecasting carbon stock variations in response to land use and land cover changes in Central Aceh, Indonesia. International Journal of Design and Nature and Ecodynamics, 19(2), 465–475. https://doi.org/10.18280/ijdne.190212
3. Alexandri E., Antón J.I., & Lewney R. (2024). The impact of climate change mitigation policies on European labour markets. Ecological Economics, 216, 1-14, https://doi.org/10.1016/j.ecolecon.2023.108022
4. Almubaidin M.A., Ismail N.S., Latif S.D., Ahmed A.N., Dullah H., El-Shafie A., & Sonne C. (2024). Machine learning predictions for carbon monoxide levels in urban environments. Results in Engineering, 22, 1-8, https://doi.org/10.1016/j.rineng.2024.102114
5. Amadou M.L., Villamor G.B., & Kyei-Baffour N. (2018). Simulating agricultural land-use adaptation decisions to climate change: An empirical agent-based modelling in northern Ghana. Agricultural Systems, 166, 196–209. https://doi.org/10.1016/j.agsy.2017.10.015
6. Aneva I., Zhelev P., Lukanov S., Peneva M., Vassilev K., & Zheljazkov V.D. (2020). Influence of the land use type on the wild plant diversity. Plants, 9(5), 602, https://doi.org/10.3390/plants9050602
7. Araza A., de Bruin S., Hein L., & Herold M. (2023). Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting. Scientific Reports, 13(1), 1-15, https://doi.org/10.1038/s41598-023-38935-8
8. Bao Z., Zhang J., Wang G., Guan T., Jin J., Liu Y., Li M., & Ma T. (2021). The sensitivity of vegetation cover to climate change in multiple climatic zones using machine learning algorithms. Ecological Indicators, 124, 11-18, https://doi.org/10.1016/j.ecolind.2021.107443
9. Cortés Arbués I., Chatzivasileiadis T., Ivanova O., Storm S., Bosello F., & Filatova T. (2024). Distribution of economic damages due to climate-driven sea-level rise across European regions and sectors. Scientific Reports, 14(1), 1-15, https://doi.org/10.1038/s41598-023-48136-y
10. Dachary-Bernard J., Gaschet F., Hautdidier B., Lemarié-Boutry M., & Pouyanne G. (2018). Between environmental protection and urban development: How changes in land use planning affect coastal land markets in Aquitaine, France. In Sud-Ouest Europeen, 45, 71–88. Presses Universitaires du Mirail. https://doi.org/10.4000/soe.4034
11. Dajam A.S., & Eid E.M. (2024). Modelling the distribution pattern of soil organic carbon in protected and grazed areas of mangrove forests (Avicennia Marina). Sustainable Environment, 10(1), 1-14, https://doi.org/10.1080/27658511.2024.2369382
12. Dong L. (2024). Spatio-temporal evolution and prediction of carbon balance in the Yellow River Basin and zoning for low-carbon economic development. Scientific Reports, 14(1), 1-19, https://doi.org/10.1038/s41598-024-65113-1
13. Dong Z., Wang L., Sun Y., Hu T., Limsakul A., Singhruck P., & Pimonsree S. (2021). Heatwaves in Southeast Asia and Their Changes in a Warmer World. Earth’s Future, 9(7), 1-13, https://doi.org/10.1029/2021EF001992
14. Feller I.C., Friess D.A., Krauss K.W., & Lewis R.R. (2017). The state of the world’s mangroves in the 21st century under climate change. Hydrobiologia, 803(1), 1–12. https://doi.org/10.1007/s10750-017-3331-z
15. Gao X., Zhao M., Zhang M., Guo Z., Liu X., & Yuan Z. (2024). Carbon conduction effect and multi-scenario carbon emission responses of land use patterns transfer: a case study of the Baiyangdian basin in China. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1374383
16. Halim M.A.R., Soeprobowati T.R., Hadiyanto H. (2023). Ecological assessment of the role of mangrove trees in carbon sequestration and biodiversity in Karimunjawa National Park Indonesia. Geography, Environment, Sustainability, 16(3), 1-13, https://doi.org/10.24057/2071-9388-2022-2565
17. Hamad R., Balzter H., & Kolo K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability (Switzerland), 10(10), 1–23. https://doi.org/10.3390/su10103421
18. Hu Y., Sun Z.H., Zhang R.Q., & Li G. (2024). Spatial servive scope of carbon neutrality and its simulation prediction in the Loess Plateau of China. Applied Ecology and Environmental Research, 22(3), 2225–2240. https://doi.org/10.15666/aeer/2203_22252240
19. Huang X., Ibrahim M.M., Luo Y., Jiang L., Chen J., & Hou E. (2024). Land use change alters soil organic carbon: constrained global patterns and predictors. Earth’s Future, 12(5), 1-16, https://doi.org/10.1029/2023EF004254
20. International Council for Local Environmental Initiatives – Local Governments for Sustainability. (2022). https://iclei.org/activity/forestryprotocol-12/
21. Ismaili A.H., Chemchaoui A., El Asri B., Ghazi S., Brhadda N., & Ziri R. (2023). Modeling predictive changes of carbon storage using invest model in the Beht Watershed (Morocco). Modeling Earth Systems and Environment, 9(4), 4313–4322. https://doi.org/10.1007/s40808-023-01697-3
22. Jakariya M., Alam M.S., Rahman M.A., Ahmed S., Elahi M.M.L., Khan A.M.S., Saad S., Tamim H.M., Ishtiak T., Sayem S.M., Ali M.S., & Akter D. (2020). Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques. Science of the Total Environment, 742, 1-11, https://doi.org/10.1016/j.scitotenv.2020.140255
23. James D., Collin A., Mury A., & Costa S. (2020). Very high resolution land use and land cover mapping using Pleiades-1 stereo imagery and machine learning. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B2), 675–682. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-675-2020
24. Karamesouti M., Detsis V., Kounalaki A., Vasiliou P., Salvati L., & Kosmas C. (2015). Land-use and land degradation processes affecting soil resources: Evidence from a traditional Mediterranean Cropland (Greece). Catena, 132, 45–55. https://doi.org/10.1016/j.catena.2015.04.010
25. Kim G.Il, Oh J. H., Shin N.Y., An S.Il, Yeh S.W., Shin J., & Kug J.S. (2024). Deep ocean warming-induced El Niño changes. Nature Communications, 15(1), 1-8, https://doi.org/10.1038/s41467-024-50663-9
26. Koroso N.H. (2023). Urban land policy and urban land use efficiency: An analysis based on remote sensing and institutional credibility thesis. Land Use Policy, 132, 1-18, https://doi.org/10.1016/j.landusepol.2023.106827
27. Laino E., & Iglesias G. (2023). Scientometric review of climate-change extreme impacts on coastal cities. In Ocean and Coastal Management, 242, 1-13, Elsevier Ltd. https://doi.org/10.1016/j.ocecoaman.2023.106709
28. Li Q., Pu Y., & Gao W. (2023). Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three Northeastern Provinces, China. Heliyon, 9(8), 1-16, https://doi.org/10.1016/j.heliyon.2023.e18923
29. Lin Y., Chen L., Ma Y., & Yang T. (2024). Analysis and simulation of land use changes and their impact on carbon stocks in the Haihe River Basin by Combining LSTM with the InVEST Model. Sustainability (Switzerland), 16(6), 1-16, https://doi.org/10.3390/su16062310
30. Liu C., Liu Z., Xie B., Liang Y., Li, X., & Zhou K. (2021). Decoupling the effect of climate and land-use changes on carbon sequestration of vegetation in Mideast Hunan Province, China. Forests, 12(11), 1-14, https://doi.org/10.3390/f12111573
31. Liu J., Yan Q., & Zhang M. (2023). Ecosystem carbon storage considering combined environmental and land-use changes in the future and pathways to carbon neutrality in developed regions. Science of the Total Environment, 903, 1-16, https://doi.org/10.1016/j.scitotenv.2023.166204
32. Luo X., Luo Y., Le F., Zhang Y., Zhang H., & Zhai J. (2024). Spatiotemporal variation and prediction analysis of land use/land cover and ecosystem service changes in Gannan, China. Sustainability (Switzerland), 16(4), 1-22, https://doi.org/10.3390/su16041551
33. Ma X., Zou B., Deng J., Gao J., Longley I., Xiao S., Guo B., Wu Y., Xu T., Xu X., Yang X., Wang X., Tan Z., Wang Y., Morawska L., & Salmond J. (2024). A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023. In Environment International, 183. Elsevier Ltd. https://doi.org/10.1016/j.envint.2024.108430
34. Mason V.G., Burden A., Epstein G., Jupe L.L., Wood K.A., & Skov M.W. (2023). Blue carbon benefits from global saltmarsh restoration. Global Change Biology, 29(23), 6517–6545. https://doi.org/10.1111/gcb.16943
35. Meliho M., Boulmane M., Khattabi A., Dansou C.E., Orlando C.A., Mhammdi N., & Noumonvi K.D. (2023). Spatial prediction of soil organic carbon stock in the Moroccan High Atlas using machine learning. Remote Sensing, 15(10), 1-29, https://doi.org/10.3390/rs15102494
36. Melis M.T., Pisani L., De Waele J. (2021). On the use of tri-stereo pleiades images for the morphometric measurement of dolines in the Basaltic Plateau of Azrou (Middle Atlas, Morocco). Remote Sens (Basel), 13, 4087, 1-17, doi:10.3390/rs13204087
37. Mirsanjari M.M., Visockiene J.S., Mohammadyari F., & Zarandian A. (2021). Modelling of expansion changes of vilnius city area and impacts on landscape patterns using an artificial neural network. Ecological Chemistry and Engineering S, 28(3), 429–447. https://doi.org/10.2478/eces-2021-0029
38. Nakakaawa C.A., Vedeld P.O., Aune J.B. (2011). Spatial and temporal land use and carbon stock changes in Uganda: Implications for a future REDD strategy. Mitigation and Adaptation Strategies for Global Change, 16: 25-62 O’Driscoll C., Crowley F., Doran J., & McCarthy N. (2023). Land-use mixing in Irish cities: Implications for sustainable development. Land Use Policy, 128, 1-7, https://doi.org/10.1016/j.landusepol.2023.106615
39. Peng B., Yang J., Li Y., & Zhang S. (2023). Land use/land cover changes in Baicheng District, China during the Period 1954–2020 and their driving forces. Land, 12(10), 1-20, https://doi.org/10.3390/land12101845
40. Pu R., Landry S., & Yu Q. (2018). Assessing the potential of multi-seasonal high resolution Pléiades satellite imagery for mapping urban tree species. In International Journal of Applied Earth Observation and Geoinformation, 71, 144–158. https://doi.org/10.1016/j.jag.2018.05.005
41. Raqeeb A., Saleem A., Ansari L., Nazami S.M., Muhammad M.W., Malik M., Naqash M., & Khalid F. (2024). Assessment of land use cover changes, carbon sequestration and carbon stock in dry temperate forests of Chilas watershed, Gilgit-Baltistan. Brazilian Journal of Biology, 84, 1-16, https://doi.org/10.1590/1519-6984.253821
42. Ren Y., Lü Y., Comber A., Fu B., Harris P., & Wu L. (2019). Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects. In Earth-Science Reviews, 190, 398–415. https://doi.org/10.1016/j.earscirev.2019.01.001
43. Segura C., Neal A.L., Castro-Sardiňa L., Harris P., Rivero M.J., Cardenas L.M., & Irisarri J.G.N. (2024). Comparison of direct and indirect soil organic carbon prediction at farm field scale. Journal of Environmental Management, 365, 1-12, https://doi.org/10.1016/j.jenvman.2024.121573
44. Singh R.K., Biradar C.M., Behera M.D., Prakash A.J., Das P., Mohanta M.R., Krishna G., Dogra A., Dhyani S.K., & Rizvi J. (2024). Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning. Ecological Informatics, 79, 1-20, https://doi.org/10.1016/j.ecoinf.2023.102408
45. Soni S., Singh H., Qurashi J., Shuja M., Pandey M., & Arora A. (2022). Landuse/landcover monitoring and spatiotemporal modelling using multilayer perceptron and ’multilayer perceptron’-Markov Chain ensemble models: A case study of Dausa City, Rajasthan. IOP Conference Series: Earth and Environmental Science, 1032(1), 1-19, https://doi.org/10.1088/1755-1315/1032/1/012028
46. Sutrisno D., Darmawan M., & Helmi M. (2021). Climate change, land use cover change and its impact on Coastal Area. In 42nd Asian Conference on Remote Sensing, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127425731&partnerID=40&md5=c92c223b8265f2d34af27943937fd3d8
47. Tariq A., Yan J., & Mumtaz F. (2022). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. In Physics and Chemistry of the Earth, 128. https://doi.org/10.1016/j.pce.2022.103286
48. Utami W., Sugiyanto C., & Rahardjo N. (2024a). Artificial intelligence in land use prediction modeling: a review. IAES International Journal of Artificial Intelligence (IJ-AI), 13(3), 2514. https://doi.org/10.11591/ijai.v13.i3.pp2514-2523
49. Utami W., Sugiyanto C., & Rahardjo N. (2024b). Mangrove area degradation and management strategies in Indonesia: A review. In Journal of Degraded and Mining Lands Management, 11, 3, 6037–6047. https://doi.org/10.15243/jdmlm.2024.113.6037 van Bijsterveldt C.E.J., van Wesenbeeck B.K., van der Wal D., Afiati N., Pribadi R., Brown B., & Bouma T.J. (2020). How to restore mangroves for greenbelt creation along eroding coasts with abandoned aquaculture ponds. Estuarine, Coastal and Shelf Science, 235, 106576. https://doi.org/10.1016/j.ecss.2019.106576
50. Verma D., Singh V., Bhattacharya P., & Kishwan J. (2020). Development, environmental impact and green growth: India. Ecology, Environment and Conservation, 26, S238–S244. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099239876&partnerID=40&md5=0b7cf0d2ed25d0fa0d9c2c6bce1764b6
51. Wei T., Yang B., Wang G., & Yang K. (2024). County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China. Scientific Reports, 14(1), 1-17, https://doi.org/10.1038/s41598-024-60036-3
52. Weindl I., Popp A., Bodirsky B.L., Rolinski S., Lotze-Campen H., Biewald A., Humpenöder F., Dietrich J. P., & Stevanović M. (2017). Livestock and human use of land: Productivity trends and dietary choices as drivers of future land and carbon dynamics. Global and Planetary Change, 159, 1–10. https://doi.org/10.1016/j.gloplacha.2017.10.002
53. Wu W., Huang Z., Sun Z., Zhang J., Wang S., Fang M., Yang H., Lu H., Guo G., & Liu W. (2024). Simulation and attribution analysis of terrestrial ecosystem carbon storage of Hainan Island from 2015 to 2050. Science of the Total Environment, 917. https://doi.org/10.1016/j.scitotenv.2024.170348
54. Wu X., Shen C., Shi L., Wan Y., Ding J., & Wen Q. (2024). Spatio-temporal evolution characteristics and simulation prediction of carbon storage: A case study in Sanjiangyuan Area, China. Ecological Informatics, 80. https://doi.org/10.1016/j.ecoinf.2024.102485
55. Xu D., Yu C., Lin W., Yao J., & Zhou W. (2024). Spatiotemporal Evolution and Prediction of Land Use and Carbon Stock in Shanghai. Land, 13(3), 1-19, https://doi.org/10.3390/land13030267
56. Xu F., Chi G., & Wang H. (2024). Scenario Analysis of Carbon Emission Changes Resulting from a Rural Residential Land Decrement Strategy: A Case Study in China. Land, 13(1). https://doi.org/10.3390/land13010051
57. Yan X., Huang M., Tang Y., Guo Q., Wu X., & Zhang G. (2024). Study on the dynamic change of land use in Megacities and its impact on ecosystem services and modeling prediction. Sustainability (Switzerland), 16(13), 1-28, https://doi.org/10.3390/su16135364
58. Yusuf M. (2021). Mining and its effects on climate change. Aspects in Mining & Mineral Science, 7(3), 1-3, https://doi.org/10.31031/amms.2021.07.000662
59. Zhang D., Zhu Z. I., Chen S., Zhang C.I., Lu X., Zhang X., Zhang X., & Davidson M.R. (2024). Spatially resolved land and grid model of carbon neutrality in China, Sustainability Science Environmental Sciences, 121 (10), 1-12, https://doi.org/10.1073/pnas
60. Zhang J., Yang J., Liu P., Liu Y., Zheng Y., Shen X., Li B., Song H., & Liang Z. (2024). Effects of land use/cover change on terrestrial carbon stocks in the Yellow River Basin of China from 2000 to 2030. Remote Sensing, 16(10), 1-16, https://doi.org/10.3390/rs16101810
61. Zhang W., Yuan W., Xuan W., Lu Y., & Huang Z. (2024). Leveraging AI techniques for predicting spatial distribution and determinants of carbon emission in China’s Yangtze River Delta. Scientific Reports, 14(1), 1-16, https://doi.org/10.1038/s41598-024-65068-3
62. Zhang X., Jia W., Sun Y., Wang F., & Miu Y. (2023). Simulation of spatial and temporal distribution of forest carbon stocks in long time series—based on remote sensing and deep learning. Forests, 14(3), 1-21, https://doi.org/10.3390/f14030483
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For citations:
Utami W., Sugiyanto C., Rahardjo N., . N. Modeling Future Carbon Stock Predictions Based on Land Use. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(2):102-113. https://doi.org/10.24057/2071-9388-2025-3684
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