Prediction of land use/land cover change in Indonesia using the open source land cover dataset: a review
Abstract
Indonesia, as a promising developing country, faced with the fact that the development is not evenly distributed. Moreover, the number of people living in urban area is more and increasing at least 2.1% per year according to Central Statistics Agency (BPS). Hence, urban area has better transportation access and public facilites. However, high number of people living in urban area leads to spatial confilcts if spatial planning is not carried out based on sustainable development. For this reason, it is necessary to carry out long-term spatial planning using predictions of changes in land use/land cover in Indonesia. The purpose of this literature review is to get an overview of research development trends related to predictions of land use/land cover in Indonesia. Based on bibliometric analysis, the research trend related to this topic is that most research locations are in urban areas using satellite imagery input data and the Cellular Automata-Marcov Chain (CA-MC) method for making predictive models. Meanwhile, open source land cover datasets have not been widely used in land use/land cover prediction research in Indonesia. This can be used as input for updating further research.
Keyword : prediction, land use/land cover change, spatial planning, open source dataset
How to Cite
Astuty, Y. I., & Dimyati, M. (2024). Prediction of land use/land cover change in Indonesia using the open source land cover dataset: a review. Geodesy and Cartography, 50(2), 67–75. https://doi.org/10.3846/gac.2024.19285
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hakim, A. M. Y., Matsuoka, M., Baja, S., Rampisela, D. A., & Arif, S. (2020). Predicting land cover change in the Mamminasata area, Indonesia, to evaluate the spatial plan. ISPRS International Journal of Geo-Information, 9(8), Article 481. https://doi.org/10.3390/ijgi9080481
Hasannudin, D. A. L., Nurrochmat, D. R., & Ekayani, M. (2022). Agroforestry management systems through landscape-life scape integration: A case study in Gowa, Indonesia. Biodiversitas, 23(4), 1864–1874. https://doi.org/10.13057/biodiv/d230420
Helena Agustina, I., Risang Aji, R., Fardani, I., Puspitasari Rochman, G., Mutia Ekasari, A., & Alain Jauzi Mohmed, F. (2022). Cellular automata for cirebon city land cover and development prediction. Journal of the Malaysian Institute of Planners, 20, 77–88. https://doi.org/10.21837/pm.v20i20.1080
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Lihawa, F., Ismail, M., Yusuf, D., & Lahay, R. J. (2022). Spatial dynamic analysis of changes in land use applying markov chain and cellular automata. Environment and Ecology Research, 10(6), 688–700. https://doi.org/10.13189/eer.2022.100606
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Prayitno, G., Sari, N., Hasyim, A. W., & Nyoman Widhi, S. W. (2020). Land-use prediction in Pandaan District Pasuruan regency. International Journal of GEOMATE, 18(65), 64–71. https://doi.org/10.21660/2020.65.41738
Putra, A. N., Nita, I., Jauhary, M. R. Al, Nurhutami, S. R., & Ismail, M. H. (2021). Landslide risk analysis on agriculture area in pacitan regency in East Java Indonesia using geospatial techniques. Environment and Natural Resources Journal, 19(2), 141–152. https://doi.org/10.32526/ennrj/19/2020167
Ramadan, G. F., & Hidayati, I. N. (2022). Prediction and simulation of land use and land cover changes using open source QGIS. A case study of Purwokerto, Central Java, Indonesia. Indonesian Journal of Geography, 54(3), 344–351. https://doi.org/10.22146/IJG.68702
Santosa, B. H., Martono, D. N., Purwana, R., & Koestoer, R. H. (2022). Flood vulnerability evaluation and prediction using multi-temporal data: A case in Tangerang, Indonesia. International Journal on Advanced Science, Engineering and Information Technology, 12(6), 2156–2164. https://doi.org/10.18517/ijaseit.12.6.16903
Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability, 11(11), Article 3024. https://doi.org/10.3390/su11113024
Sejati, A. W., Buchori, I., & Rudiarto, I. (2019). The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region. Sustainable Cities and Society, 46. https://doi.org/10.1016/j.scs.2019.101432
Sterling, S. M., Ducharne, A., & Polcher, J. (2013). The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3(4), 385–390. https://doi.org/10.1038/nclimate1690
Struebig, M. J., Fischer, M., Gaveau, D. L. A., Meijaard, E., Wich, S. A., Gonner, C., Sykes, R., Wilting, A., & Kramer-Schadt, S. (2015). Anticipated climate and land-cover changes reveal refuge areas for Borneo’s orang-utans. Global Change Biology, 21(8), 2891–2904. https://doi.org/10.1111/gcb.12814
Supriatna, Mukhtar, M. K., Wardani, K. K., Hashilah, F., & Manessa, M. D. M. (2022). CA-Markov chain model-based predictions of land cover: A case study of Banjarmasin city. Indonesian Journal of Geography, 54(3), 365–372. https://doi.org/10.22146/IJG.71721
Suriadikusumah, A., Mulyono, A., Hilda, M., & Maulana, R. (2022). Prediction of Bandung district land use change using markov chain modeling. International Journal on Advance Science Engineering Information Technology, 12(1).
Tupan, T., Rahayu, R. N., Rachmawati, R., & Rahayu, E. S. R. (2018). Analisis Bibliometrik Perkembangan Penelitian Bidang Ilmu Instrumentasi. BACA: Jurnal dokumentasi dan informasi, 39(2), 135. https://doi.org/10.14203/j.baca.v39i2.413
Umarhadi, D. A., Widyatmanti, W., Kumar, P., Yunus, A. P., Khedher, K. M., Kharrazi, A., & Avtar, R. (2022). Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis. Science of the Total Environment, 816, Article 151561. https://doi.org/10.1016/j.scitotenv.2021.151561
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and esri land cover. Remote Sensing, 14(16), Article 4101. https://doi.org/10.3390/rs14164101
Wayan Gede Krisna Arimjaya, I., & Dimyati, M. (2022). Remote sensing and geographic information systems technics for spatial-based development planning and policy. International Journal of Electrical and Computer Engineering, 12(5), 5073–5083. https://doi.org/10.11591/ijece.v12i5.pp5073-5083
Yulianto, F., Maulana, T., & Khomarudin, M. R. (2019). Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia. International Journal of Digital Earth, 12(10), 1151–1176. https://doi.org/10.1080/17538947.2018.1497098
Zhang, X., Xiong, Z., Zhang, X., Shi, Y., Liu, J., Shao, Q., & Yan, X. (2016). Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: a case study over northeast China. Climate Dynamics, 46(3–4), 765–778. https://doi.org/10.1007/s00382-015-2611-4
Artikanur, S. D., Widiatmaka, W., Setiawan, Y., & Marimin, M. (2022). Predicting sugar balance as the impact of land-use/land-cover change dynamics in a sugarcane producing regency in East Java, Indonesia. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.787207
Avtar, R., Rinamalo, A. V., Umarhadi, D. A., Gupta, A., Khedher, K. M., Yunus, A. P., Singh, B. P., Kumar, P., Sahu, N., & Sakti, A. D. (2022). Land use change and prediction for valuating carbon sequestration in Viti Levu Island, Fiji. Land, 11(8). https://doi.org/10.3390/land11081274
Farid, M., Pratama, M. I., Kuntoro, A. A., Adityawan, M. B., Rohmat, F. I. W., & Moe, I. R. (2022). Flood prediction due to land cover change in the Ciliwung River Basin. International Journal of Technology, 13(2), 356–366. https://doi.org/10.14716/ijtech.v13i2.4662
Feizizadeh, B., Darabi, S., Blaschke, T., & Lakes, T. (2022). QADI as a new method and alternative to kappa for accuracy assessment of remote sensing-based image classification. Sensors, 22(12), Article 4506. https://doi.org/10.3390/s22124506
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20–22), 3761–3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009
Hakim, A. M. Y., Baja, S., Rampisela, D. A., & Arif, S. (2021). Modelling land use/land cover changes prediction using multi-layer perceptron neural network (MLPNN): a case study in Makassar City, Indonesia. International Journal of Environmental Studies, 78(2), 301–318. https://doi.org/10.1080/00207233.2020.1804730
Hakim, A. M. Y., Matsuoka, M., Baja, S., Rampisela, D. A., & Arif, S. (2020). Predicting land cover change in the Mamminasata area, Indonesia, to evaluate the spatial plan. ISPRS International Journal of Geo-Information, 9(8), Article 481. https://doi.org/10.3390/ijgi9080481
Hasannudin, D. A. L., Nurrochmat, D. R., & Ekayani, M. (2022). Agroforestry management systems through landscape-life scape integration: A case study in Gowa, Indonesia. Biodiversitas, 23(4), 1864–1874. https://doi.org/10.13057/biodiv/d230420
Helena Agustina, I., Risang Aji, R., Fardani, I., Puspitasari Rochman, G., Mutia Ekasari, A., & Alain Jauzi Mohmed, F. (2022). Cellular automata for cirebon city land cover and development prediction. Journal of the Malaysian Institute of Planners, 20, 77–88. https://doi.org/10.21837/pm.v20i20.1080
Keputusan Menteri Dalam Negeri Nomor 050-145 Tahun 2022 tentang Pemberian dan Pemutakhiran Kode, Data Wilayah Administrasi Pemerintahan, dan Pulau Tahun 2021, Jakarta. (2022).
Li, D., Tian, P., Luo, H., Hu, T., Dong, B., Cui, Y., Khan, S., & Luo, Y. (2020). Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau. Science of The Total Environment, 742, Article 140570. https://doi.org/10.1016/j.scitotenv.2020.140570
Lihawa, F., Ismail, M., Yusuf, D., & Lahay, R. J. (2022). Spatial dynamic analysis of changes in land use applying markov chain and cellular automata. Environment and Ecology Research, 10(6), 688–700. https://doi.org/10.13189/eer.2022.100606
McCarl, B., Attavanich, W., Musumba, M., Mu, J. E., & Aisabokhae, R. A. (2014). Land use and climate change. Science, 310, 1625–1626. https://doi.org/10.1126/science.1120529
Prayitno, G., Sari, N., Hasyim, A. W., & Nyoman Widhi, S. W. (2020). Land-use prediction in Pandaan District Pasuruan regency. International Journal of GEOMATE, 18(65), 64–71. https://doi.org/10.21660/2020.65.41738
Putra, A. N., Nita, I., Jauhary, M. R. Al, Nurhutami, S. R., & Ismail, M. H. (2021). Landslide risk analysis on agriculture area in pacitan regency in East Java Indonesia using geospatial techniques. Environment and Natural Resources Journal, 19(2), 141–152. https://doi.org/10.32526/ennrj/19/2020167
Ramadan, G. F., & Hidayati, I. N. (2022). Prediction and simulation of land use and land cover changes using open source QGIS. A case study of Purwokerto, Central Java, Indonesia. Indonesian Journal of Geography, 54(3), 344–351. https://doi.org/10.22146/IJG.68702
Santosa, B. H., Martono, D. N., Purwana, R., & Koestoer, R. H. (2022). Flood vulnerability evaluation and prediction using multi-temporal data: A case in Tangerang, Indonesia. International Journal on Advanced Science, Engineering and Information Technology, 12(6), 2156–2164. https://doi.org/10.18517/ijaseit.12.6.16903
Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability, 11(11), Article 3024. https://doi.org/10.3390/su11113024
Sejati, A. W., Buchori, I., & Rudiarto, I. (2019). The spatio-temporal trends of urban growth and surface urban heat islands over two decades in the Semarang Metropolitan Region. Sustainable Cities and Society, 46. https://doi.org/10.1016/j.scs.2019.101432
Sterling, S. M., Ducharne, A., & Polcher, J. (2013). The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3(4), 385–390. https://doi.org/10.1038/nclimate1690
Struebig, M. J., Fischer, M., Gaveau, D. L. A., Meijaard, E., Wich, S. A., Gonner, C., Sykes, R., Wilting, A., & Kramer-Schadt, S. (2015). Anticipated climate and land-cover changes reveal refuge areas for Borneo’s orang-utans. Global Change Biology, 21(8), 2891–2904. https://doi.org/10.1111/gcb.12814
Supriatna, Mukhtar, M. K., Wardani, K. K., Hashilah, F., & Manessa, M. D. M. (2022). CA-Markov chain model-based predictions of land cover: A case study of Banjarmasin city. Indonesian Journal of Geography, 54(3), 365–372. https://doi.org/10.22146/IJG.71721
Suriadikusumah, A., Mulyono, A., Hilda, M., & Maulana, R. (2022). Prediction of Bandung district land use change using markov chain modeling. International Journal on Advance Science Engineering Information Technology, 12(1).
Tupan, T., Rahayu, R. N., Rachmawati, R., & Rahayu, E. S. R. (2018). Analisis Bibliometrik Perkembangan Penelitian Bidang Ilmu Instrumentasi. BACA: Jurnal dokumentasi dan informasi, 39(2), 135. https://doi.org/10.14203/j.baca.v39i2.413
Umarhadi, D. A., Widyatmanti, W., Kumar, P., Yunus, A. P., Khedher, K. M., Kharrazi, A., & Avtar, R. (2022). Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis. Science of the Total Environment, 816, Article 151561. https://doi.org/10.1016/j.scitotenv.2021.151561
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022). Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and esri land cover. Remote Sensing, 14(16), Article 4101. https://doi.org/10.3390/rs14164101
Wayan Gede Krisna Arimjaya, I., & Dimyati, M. (2022). Remote sensing and geographic information systems technics for spatial-based development planning and policy. International Journal of Electrical and Computer Engineering, 12(5), 5073–5083. https://doi.org/10.11591/ijece.v12i5.pp5073-5083
Yulianto, F., Maulana, T., & Khomarudin, M. R. (2019). Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia. International Journal of Digital Earth, 12(10), 1151–1176. https://doi.org/10.1080/17538947.2018.1497098
Zhang, X., Xiong, Z., Zhang, X., Shi, Y., Liu, J., Shao, Q., & Yan, X. (2016). Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: a case study over northeast China. Climate Dynamics, 46(3–4), 765–778. https://doi.org/10.1007/s00382-015-2611-4