DOI: https://doi.org/10.14710/geoplanning.12.1.57-68

Land Cover Classification of Indonesian Archipelago Using Digital Spectroscopy to Support Spatial Planning in Indonesia

*Guntur Bagus Pamungkas orcid scopus publons  -  Study Program of Urban and Regional Planning, Faculty of Science and Technology, Universitas Terbuka, Indonesia
Muhammad Reffi Firmansyah  -  Study Program of Urban and Regional Planning, Faculty of Science and Technology, Universitas Terbuka, Indonesia
Ratna Sari  -  Study Program of Urban and Regional Planning, Faculty of Science and Technology, Universitas Terbuka, Indonesia
Anindya Putri Tamara  -  Study Program of Urban and Regional Planning, Faculty of Engineering, Universitas Semarang, Indonesia
Rahadian Zainul scopus  -  Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Negeri Padang, Indonesia

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Abstract

In the context of urban and regional planning, this study aims to produce land classification products covering 230 paths/rows throughout Indonesia, which can serve as an important tool in supporting planning and research projects. The research method used combines remote sensing in Geographic Information Systems (GIS) with the utilization of spectroscopy through QGIS software with Dzetsaka plugins (semi-automatic classification tools). Land cover classifications, which include water bodies, vegetation canopies, green open spaces, bare grounds, settlements, and built-up areas, as well as additional classifications of cloud cover, provide a comprehensive overview of land conditions in Indonesia. Based on the results of the study, the average distribution of land classes reached 10,116. The standard deviation was 14,786, which shows the level of variation in the data against the average value, with the higher value indicating the most significant variation in land classification. This study offers a more potential alternative by using Landsat 8 OLI 2022 satellite imagery data from the USGS as a basis for a more in-depth and accurate analysis of land classification. Thus, the results of this study not only contribute to mapping and understanding land use in Indonesia but also provide useful tools for supporting natural resource planning and management, as well as infrastructure development and sustainable development policies in Indonesia

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Keywords: Spectroscopy; Remote Sensing; Project based Learning
Funding: LPPM of Universitas Terbuka under contract PN2022-00003845

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Section: Original Research
Language : EN
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  1. Abdelkarim, A. (2023). Monitoring and forecasting of land use/land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Model (CA-Markov). Geology, Ecology, and Landscapes, 1–32. https://doi.org/10.1080/24749508.2022.2163741">[Crossref]

  2. Adnan, R. (2023). The Dynamic Role of Moslem in Building Indonesia as a Nation-State. Indonesian Journal of Religion and Society, 5(1), 58-69. https://doi.org/10.36256/ijrs.v5i1.339">[Crossref]

  3. Ayerbe-López, J., & Perales-Palacios, F. J. (2023). Evaluating a Secondary Education Urban Ecology Project within the Framework of a Problem-Based Learning Methodology. Education Sciences, 13(9), 915. https://doi.org/10.3390/educsci13090915">[Crossref]

  4. Bongasie, A., Dhakal, T., Ayalew, A., Kim, T. S., Lee, Y., & Jang, G. S. (2024). Analysis of forest cover change and its driving factors in Senan district, Amhara Region, Ethiopia. Environmental monitoring and assessment, 196(4), 339. https://doi.org/10.1007/s10661-024-12392-w">[Crossref]

  5. Buchori, I. (2007). Ilmu Geomatika Perencanaan dalam Pendidikan Perencanaan Wilayah dan Kota untuk Menghadapi Tantangan Global Abstrak Pendahuluan.

  6. Buchori, I., & Tanjung, K. (2014). Developing a simulation model for predicting inundated areas affected by land use change: a case study of Keduang Sub-watershed. The International Journal of Environmental Sustainability, 9(1), 79. [Crossref]

  7. Buchori, I., Zaki, A., Pangi, P., Sejati, A. W., Pramitasari, A., & Liu, Y. (2022). Adaptation strategies and community participation in government-led mitigation projects: A comparison between urban and suburban communities in Pekalongan, Indonesia. International Journal of Disaster Risk Reduction, 81, 103271. https://doi.org/10.1016/j.ijdrr.2022.103271">[Crossref]

  8. Carramiñana, D., Bernardos, A. M., Besada, J. A., & Casar, J. R. (2024). Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making. Simulation Modelling Practice and Theory, 133(November 2023). https://doi.org/10.1016/j.simpat.2024.102924">[Crossref]

  9. Carrasco, L., O’Neil, A. W., Daniel Morton, R., & Rowland, C. S. (2019). Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2, and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sensing, 11(3). https://doi.org/10.3390/rs11030288">[Crossref]

  10. Crawford, C. J., Roy, D. P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., Zahn, S. (2023). The 50-year Landsat collection 2 archive. Science of Remote Sensing, 8(July), 100103. https://doi.org/10.1016/j.srs.2023.100103">[Crossref]

  11. Delcourt, N., Farnet-Da Silva, A. M., Rébufa, C., Foli, L., & Dupuy, N. (2023). Land Use Legacy Footprint in Mediterranean Forest Soils: An Infrared Spectroscopy Approach. Geoderma, 430 (November 2022). https://doi.org/10.1016/j.geoderma.2022.116299">[Crossref]

  12. Dong, C., Qi, S., Dai, Z., Qiu, X., & Luo, T. (2024). Research on accurate and effective identification of ecosystem surface based on human footprint index. Ecological Indicators, 162(April), 112013. https://doi.org/10.1016/j.ecolind.2024.112013">[Crossref]

  13. Falcioni R, Antunes WC, Demattê JAM, Nanni MR (2023). Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops. Plants. 2023; 12(12):2347. https://doi.org/10.3390/plants12122347">[Crossref]

  14. Ferdous, J., Ur Rahman, M. T., & Ghosh, S. K. (2019). Detection of Total Dissolved Solids from Landsat 8 OLI Image in Coastal Bangladesh. June, 35–44. https://doi.org/10.17501/2513258x.2019.3103">[Crossref]

  15. Hede, A. N. H., Firdaus, M. A., Prianata, Y. L. O., Heriawan, M. N., Syafrizal, S., Syaeful, H., & Lubis, I. A. (2019). Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif. Eksplorium: Buletin Pusat Teknologi Bahan Galian Nuklir, 40(2), 89-98.

  16. Hede, A. N. H., & Heriawan, S. M. N. (2020). Pemanfaatan Spektroskopi Reflektansi Dalam Pengindraan Jauh Sensor Optis Untuk Eksplorasi Mineral. Prosiding Tpt Xxix Perhapi 2020 Optimalisasi, December, 221–232.

  17. Kalfas, D., Kalogiannidis, S., Chatzitheodoridis, F., & Toska, E. (2023). Urbanization and Land Use Planning for Achieving the Sustainable Development Goals (SDGs): A Case Study of Greece. Urban Science, 7(2), 43. https://doi.org/10.3390/urbansci7020043">[Crossref]

  18. Karpukhina, E. A., Volkov, D. S., & Proskurnin, M. A. (2023). Quantification of Lignosulfonates and Humic Components in Mixtures by ATR FTIR Spectroscopy. Agronomy, 13(4), 1141. https://doi.org/10.3390/agronomy13041141">[Crossref]

  19. Kuttruff, J., Romanelli, M., Pedrueza-Villalmanzo, E. et al. Sub-picosecond collapse of molecular polaritons to pure molecular transition in plasmonic photoswitch-nanoantennas. Nat Commun 14, 3875 (2023). https://doi.org/10.1038/s41467-023-39413-5">[Crossref]

  20. Levi, N., Karnieli, A., & Paz-Kagan, T. (2022). Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands. ISPRS Journal of Photogrammetry and Remote Sensing, 186(February), 34–54. https://doi.org/10.1016/j.isprsjprs.2022.01.018">[Crossref]

  21. Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., & Dur, F. (2023). Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustainable Cities and Society, 96(April), 104653. https://doi.org/10.1016/j.scs.2023.104653">[Crossref]

  22. Manetu, W. M., Mironga, J. M., & Ondiko, J. H. (2023). Remote Sensing for Land Resources: A Review on Satellites, Data Availability and Applications. American Journal of Remote Sensing, 10(2), 39–49. https://doi.org/10.11648/j.ajrs.20221002.12">[Crossref]

  23. Meidodga, I., Syahrin, A., Putra, R. T., Warfandu, F., & Bimasena, A. N. (2023). Pemanfaatan Data Geospasial dalam Mewujudkan Sistem Informasi Pertanahan Multiguna Bagi Multipihak. Widya Bhumi, 3(1), 62–80. https://doi.org/10.31292/wb.v3i1.51">[Crossref]

  24. Mikheytsev, N. A., & Korzhimanov, A. V. (2023). Generation of synchronized x-rays and mid-infrared pulses by Doppler shifting of relativistically intense radiation from near-critical-density plasmas. Matter and Radiation at Extremes, 8(2). https://doi.org/10.1063/5.0116660">[Crossref]

  25. https://www.emerald.com/insight/search?q=Long%20Thang%20Van%20Nguyen">Nguyen, L.T.V., https://www.emerald.com/insight/search?q=Donna%20Cleveland">Cleveland, D., https://www.emerald.com/insight/search?q=Chi%20Tran%20Mai%20Nguyen">Nguyen, C.T.M. and https://www.emerald.com/insight/search?q=Corinna%20Joyce">Joyce, C. (2024), "Problem-based Learning and the Integration of Sustainable Development Goals", https://www.emerald.com/insight/publication/issn/2205-2062">Journal of Work-Applied Management, 16(2), pp. 218-234. https://doi.org/10.1108/JWAM-12-2023-0142">[Crossref]

  26. Nunn, P. D., Kumar, R., Barrowman, H. M., Chambers, L., Fifita, L., Gegeo, D., Gomese, C., McGree, S., Rarai, A., Cheer, K., Esau, D., Fa'anunu, '., Fong, T., Fong-Lomavatu, M., Geraghty, P., Heorake, T., Kekeubata, E., Korovulavula, I., Kubunavanua, E., & Waiwai, M. (2024). Traditional Knowledge for Climate Resilience in the Pacific Islands. WIREs Climate Change, 15(4), e882. https://doi.org/10.1002/wcc.882">[Crossref]

  27. Padmini, Y., Rao, M. S., & Raja, G. R. (2023). Temporal Analysis of Land Use and Land Cover Changes in Vizianagaram District, Andhra Pradesh, India using Remote Sensing and GIS Techniques. Geoplanning: Journal of Geomatics and Planning, 10(1), 1-10. https://doi.org/10.14710/geoplanning.10.1.1-10">[Crossref]

  28. Pedrotti, F. L., Pedrotti, L. M., & Pedrotti, L. S. (2017). Introduction to Optics (3rd ed.). Cambridge: Cambridge University Press.

  29. Polyakova, A., Mukharamova, S., Yermolaev, O., & Shaykhutdinova, G. (2023). Automated Recognition of Tree Species Composition of Forest Communities Using Sentinel-2 Satellite Data. Remote Sensing, 15(2), 329. https://doi.org/10.3390/rs15020329">[Crossref]

  30. Prasad, R. D., Prasad, R. S., Prasad, R. B., Prasad, S. R., Singha, S. B., Singha, A. D., Prasad, R. J., Teli, S. B., Sinha, P., Vaidya, A. K., Saxena, S., Saxena, U. R., Harale, A., Deshmukh, M. B., Padvi, M. N., & Navathe, G. J. (2024). A Review on Modern Characterization Techniques for Analysis of Nanomaterials and Biomaterials. ES Energy and Environment, 23. https://doi.org/10.30919/esee1087">[Crossref]

  31. Simarmata, H. A., Rafliana, I., Herbeck, J., & Siriwardane-de Zoysa, R. (2023). Futuring ‘Nusantara’: Detangling Indonesia’s modernist archipelagic imaginaries. In Ocean Governance: Knowledge Systems, Policy Foundations and Thematic Analyses (pp. 337-363). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-20740-2_15">[Crossref]

  32. Sitinjak, K. P. H., Ibrahim, A. M., Putra, G. M., Mahlil, T., Januriyadi, N. F., & Rasyif, T. M. (2023). Probabilistic Analysis of the Tsunami Disaster on the Vulnerability Level of Buildings in Painan City, West Sumatra based on the Earthquake Ratio with the Logic Tree Method. E3S Web of Conferences, 447, 1–10. https://doi.org/10.1051/e3sconf/202344701010">[Crossref]

  33. Safithri, R., Syaiful, S., & Huda, N. (2021). Pengaruh Penerapan Problem Based Learning (PBL) dan Project Based Learning (PjBL) Terhadap Kemampuan Pemecahan Masalah Berdasarkan Self Efficacy Siswa. Jurnal Cendekia : Jurnal Pendidikan Matematika, 5(1), 335-346. https://doi.org/10.31004/cendekia.v5i1.539">[Crossref]

  34. 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(July 2018), 101432. https://doi.org/10.1016/j.scs.2019.101432">[Crossref]

  35. Shaikh, M., & Birajdar, F. (2024). Advancements in Remote Sensing and GIS for Sustainable Groundwater Monitoring: Applications, Challenges, and Future Directions. International Journal of Research in Engineering, Science and Management, 7(3), 16–24. https://journal.ijresm.com/index.php/ijresm/article/view/2955">

  36. Singh, B., Venkatramanan, V., & Deshmukh, B. (2022). Monitoring of land use land cover dynamics and prediction of urban growth using Land Change Modeler in Delhi and its environs, India. Environmental science and pollution research international, 29(47), 71534–71554. https://doi.org/10.1007/s11356-022-20900-z">[Crossref]

  37. Sugiri, A., Buchori, I., & Ma'rif, S. (2015). Towards participatory spatial policy: Facilitating rural non-farm activities in Susukan suburb of Semarang Metropolitan Region. International Journal of Civic, Political, and Community Studies, 13(4), 1-17https://doi.org/10.18848/2327-0047/cgp/v13i04/43588">.[Crossref]

  38. Srivastava, A., & Maity, R. (2023). Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development. Sustainability, 15(23), 16461. https://doi.org/10.3390/su152316461">[Crossref]

  39. Stevanović, M., Pavlićević, P., Vujinović, N. (2023). International relations challenges and sustainable development in developing countries after 2022: conceptualization of the risk assessment model. Energ Sustain Soc 13, 48(2023). https://doi.org/10.1186/s13705-023-00430-3">[Crossref]

  40. Swandi, A., Rahmadhanningsih, S., Viridi, S., Nurhayati, N., Putri, R. A., & Suryadi, A. (2021). Simulasi Gerak Translasi dan Gerak Melingkar Menggunakan Vba Macro Excel Melalui Project Based Learning (Pbl). JPF (Jurnal Pendidikan Fisika) Universitas Islam Negeri Alauddin Makassar, 9(1), 33-42. https://doi.org/10.24252/jpf.v9i1.20519">[Crossref]

  41. Tenorio, E. A., Montoya, P., Norden, N., Rodríguez-Buriticá, S., Salgado-Negret, B., & Gonzalez, M. A. (2023). Mountains exhibit a stronger latitudinal diversity gradient than lowland regions. Journal of Biogeography, 50, 1026-1036. https://doi.org/10.1111/jbi.14597">[Crossref]

  42. Vanderhoof, M. K., Alexander, L., Christensen, J., Solvik, K., Nieuwlandt, P., & Sagehorn, M. (2023). High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017-2021). Remote sensing of environment, 288, 1–28. https://doi.org/10.1016/j.rse.2023.113498">[Crossref]

  43. Wang, L., Cheng, Y., Meftaul, I. M., Luo, F., Kabir, M. A., Doyle, R., Lin, Z., & Naidu, R. (2024). Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis. Analytical chemistry, 96(20), 8109–8123. https://doi.org/10.1021/acs.analchem.3c05311">[Crossref]

  44. Wiatkowska, B., Słodczyk, J., & Stokowska, A. (2021). Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland. Geosciences, 11(8), 312. https://doi.org/10.3390/geosciences11080312">[Crossref]

  45. Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., & Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280(July), 113195. https://doi.org/10.1016/j.rse.2022.113195">[Crossref]

  46. Yu, D., & Fang, C. (2023). Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades. Remote Sensing, 15(5), 1307. https://doi.org/10.3390/rs15051307">[Crossref]

  47. Yuan, X., Tian, J., & Reinartz, P. (2023). Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification. Sensors, 23(9), 4179. https://doi.org/10.3390/s23094179">[Crossref]

  48. Zaki, A., Buchori, I., Pangi, P., Sejati, A. W., & Liu, Y. (2023). Google Earth Engine for improved spatial planning in agricultural and forested lands: A method for projecting future ecological quality. Remote Sensing Applications: Society and Environment, 32, 101078. https://doi.org/10.1016/j.rsase.2023.101078">[Crossref]


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