gesjGEOGRAPHY, ENVIRONMENT, SUSTAINABILITYGEOGRAPHY, ENVIRONMENT, SUSTAINABILITY2071-93882542-1565Russian Geographical Society10.24057/2071-9388-2025-3684gesj-4142Research ArticleRESEARCH PAPERСтатьиModeling Future Carbon Stock Predictions Based on Land UseUtamiWesti

Jl. Teknika Utara, Yogyakarta, 55284 

Jl. Tata Bumi No. 5, Yogyakarta, 55293 

westiutami@mail.ugm.ac.idSugiyantoCatur

Jl. Sosio Humaniora, Yogyakarta, 55281 

RahardjoNoorhadi

Jl. Kaliurang, Yogyakarta, 55281 

Nurhadi.

Jl. Sosio Yusticia No.1, Yogyakarta, 55281 

Doctoral Program of Environmental Science Universitas Gadjah Mada ; Department of Land Management Sekolah Tinggi Pertanahan NasionalIndonesiaFaculty of Economics and Bussiness Universitas Gadjah MadaIndonesiaFaculty Geography Universitas Gadjah MadaIndonesiaFaculty of Social Science and Political Science, Universitas Gadjah MadaIndonesia202505072025182102113Copyright © Utami W., Sugiyanto C., Rahardjo N., Nurhadi .., 20252025Utami W., Sugiyanto C., Rahardjo N., Nurhadi ..Utami W., Sugiyanto C., Rahardjo N., Nurhadi ..Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ges.rgo.ru/jour/article/view/4142

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.

carbon stockclimate changeland usebuilt-up expansionmachine learningprediction modelingThe 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.ReferencesAbbass 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/s11356-022-19718-6Abbass 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/s11356-022-19718-6Achmad 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.190212Achmad 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.190212Alexandri 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.2023.108022Alexandri 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.2023.108022Almubaidin 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.2024.102114Almubaidin 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.2024.102114Amadou 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.2017.10.015Amadou 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.2017.10.015Aneva 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/plants9050602Aneva 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/plants9050602Araza 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/s41598-023-38935-8Araza 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/s41598-023-38935-8Bao 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.2021.107443Bao 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.2021.107443Cortés 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-yCortés 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-yDachary-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.4034Dachary-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.4034Dajam 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/10.1080/27658511.2024.2369382Dajam 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/10.1080/27658511.2024.2369382Dong 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/s41598-024-65113-1Dong 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/s41598-024-65113-1Dong 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/10.1029/2021EF001992Dong 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/10.1029/2021EF001992Feller 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-zFeller 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-zGao 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.2024.1374383Gao 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.2024.1374383Halim 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/10.24057/2071-9388-2022-2565Halim 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/10.24057/2071-9388-2022-2565Hamad 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/su10103421Hamad 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/su10103421Hu 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_22252240Hu 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_22252240Huang 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/10.1029/2023EF004254Huang 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/10.1029/2023EF004254International Council for Local Environmental Initiatives – Local Governments for Sustainability. (2022). https://iclei.org/activity/forestryprotocol-12/International Council for Local Environmental Initiatives – Local Governments for Sustainability. (2022). https://iclei.org/activity/forestryprotocol-12/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/s40808-023-01697-3Ismaili 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/s40808-023-01697-3Jakariya 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.2020.140255Jakariya 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.2020.140255James 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-B2-2020-675-2020James 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-B2-2020-675-2020Karamesouti 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.2015.04.010Karamesouti 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.2015.04.010Kim 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/s41467-024-50663-9Kim 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/s41467-024-50663-9Koroso 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.2023.106827Koroso 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.2023.106827Laino 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.2023.106709Laino 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.2023.106709Li 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.e18923Li 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.e18923Lin 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/su16062310Lin 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/su16062310Liu 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/f12111573Liu 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/f12111573Liu 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.2023.166204Liu 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.2023.166204Luo 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/su16041551Luo 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/su16041551Ma 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.2024.108430Ma 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.2024.108430Mason 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.16943Mason 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.16943Meliho 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/rs15102494Meliho 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/rs15102494Melis 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/rs13204087Melis 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/rs13204087Mirsanjari 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-2021-0029Mirsanjari 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-2021-0029Nakakaawa 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.2023.106615Nakakaawa 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.2023.106615Peng 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/land12101845Peng 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/land12101845Pu 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.2018.05.005Pu 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.2018.05.005Raqeeb 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/10.1590/1519-6984.253821Raqeeb 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/10.1590/1519-6984.253821Ren 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.2019.01.001Ren 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.2019.01.001Segura 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.2024.121573Segura 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.2024.121573Singh 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.2023.102408Singh 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.2023.102408Soni 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/10.1088/1755-1315/1032/1/012028Soni 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/10.1088/1755-1315/1032/1/012028Sutrisno 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=c92c223b8265f2d34af27943937fd3d8Sutrisno 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=c92c223b8265f2d34af27943937fd3d8Tariq 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.2022.103286Tariq 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.2022.103286Utami 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.pp2514-2523Utami 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.pp2514-2523Utami 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.2019.106576Utami 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.2019.106576Verma 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=0b7cf0d2ed25d0fa0d9c2c6bce1764b6Verma 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=0b7cf0d2ed25d0fa0d9c2c6bce1764b6Wei 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/s41598-024-60036-3Wei 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/s41598-024-60036-3Weindl 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.2017.10.002Weindl 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.2017.10.002Wu 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.2024.170348Wu 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.2024.170348Wu 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.2024.102485Wu 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.2024.102485Xu 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/land13030267Xu 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/land13030267Xu 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/land13010051Xu 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/land13010051Yan 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/su16135364Yan 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/su16135364Yusuf M. (2021). Mining and its effects on climate change. Aspects in Mining & Mineral Science, 7(3), 1-3, https://doi.org/10.31031/amms.2021.07.000662Yusuf M. (2021). Mining and its effects on climate change. Aspects in Mining & Mineral Science, 7(3), 1-3, https://doi.org/10.31031/amms.2021.07.000662Zhang 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/pnasZhang 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/pnasZhang 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/rs16101810Zhang 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/rs16101810Zhang 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/s41598-024-65068-3Zhang 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/s41598-024-65068-3Zhang 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/f14030483Zhang 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

The authors declare that there are no conflicts of interest present.