Studi Pola dan Klasifikasi Perubahan Penggunaan Lahan Multitemporal di Sub DAS Ake Kobe Menggunakan Citra Satelit
Amirudin Miradj*, Muhammad Saleh Pallu, Rita Tahir Lopa, Muksan Putra Hatta
Jurusan Teknik Sipil, Universitas Hasanuddin, INDONESIA
*Corresponding author: pbpengineer@gmail.com
INTISARI
Penelitian ini menganalisis pola perubahan penggunaan lahan multitemporal di Sub DAS Ake Kobe selama periode 2019–2024 menggunakan citra satelit Sentinel-2 dan metode klasifikasi Random Forest. Pendekatan penginderaan jauh dan analisis spasial digunakan untuk mengidentifikasi perubahan kelas tutupan lahan yang mencakup hutan, permukiman, lahan pertanian, lahan terbuka, industri, dan badan air. Dengan melakukan klasifikasi citra satelit pada Google Eart Engine (GEE). Pengumpulan dan pra-pemrosesan Citra: mengambil dan membersihkan citra Sentinel-2 untuk setiap tahun (2019-2024), persiapan data latih dengan menggabungkan sampel tutupan lahan (air, vegetasi, lahan terbangun, dll.) dan membaginya menjadi data latih dan uji. Ekstraksi nilai citra dengan mengambil nilai piksel dari citra tahun 2024 pada lokasi sampel latih dan uji. pelatihan dan pengujian model yaitu melatih pengklasifikasi Random Forest menggunakan data latih tahun 2024 dan menguji akurasinya. Hasil menunjukkan adanya penurunan luas hutan sebesar 10,812 ha, serta peningkatan signifikan pada kategori pertanian, lahan terbuka dan industri masing-masing sebesar 2.601 ha, 1.250 ha dan 734.4 ha pada tahun 2024. Evaluasi akurasi klasifikasi menghasilkan nilai akurasi sebesar 93,62% dan Kappa 92,06, Temuan ini memberikan dasar ilmiah untuk pengambilan kebijakan pengelolaan sumber daya lahan dan air secara berkelanjutan di Sub DAS Ake Kobe.
REFERENSI
Adegun, A. A., Viriri, S., & Tapamo, J. (2023). Review of Deep Learning Methods for Remote Sensing
Satellite Images Classification: Experimental Survey and Comparative Analysis. Journal of Big
Data, 10(1). https://doi.org/10.1186/s40537-023-00772-x
Bonsu, K., & Bonin, O. (2023). Urban Growth Process in Greater Accra Metropolitan Area:
Characterization Using Fractal Analysis. Journal of Geovisualization and Spatial Analysis, 7(2).
https://doi.org/10.1007/s41651-023-00149-x
Datta, S., Karmakar, S., Islam, Md. N., Karim, M. E., Kabir, Md. H., & Uddin, J. (2022). Assessing
Landcover and Water Uses Effects on Water Quality in a Rapidly Developing Semi-Urban Coastal Area
of Bangladesh. Journal https://doi.org/10.1016/j.jcle ro.2022.130388 of Cleaner Production, 336, 130388.
He, X., Chen, C., Liu, Y., & Chu, Y. (2020). Inundation Analysis Method for Urban Mountainous Areas
Based on Soil Conservation Service Curve Number (SCS-CN) Model Using Remote Sensing Data.
Sensors and Materials, 32, 3813. https://doi.org/10.18494/sam.2020.2769
Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S., & Prodhan, F. A. (2022). Spatio-Temporal Changes of
Land Use Land Cover and Ecosystem Service Values in Coastal Bangladesh. The Egyptian Journal
of Remote Sensing and Space Science, 25(1), 173–180. https://doi.org/10.1016/j.ejrs.2022.01.008
Hu, S., & Shrestha, P. (2020). Examine the impact of land use and land cover changes on peak discharges
of a watershed in the midwestern United States using the HEC-HMS model. Papers in Applied
Geography, 6, 101–118. https://doi.org/10.1080/23754931.2020.1732447
Huang, J., Weng, L., Chen, B., & Xia, M. (2021). DFFAN: Dual Function Feature Aggregation Network
for Semantic Segmentation of Land Cover. Isprs International Journal of Geo-Information, 10(3),
125. https://doi.org/10.3390/ijgi10030125
Ibrahim, S. (2022). Improving Land Use/Cover Classification Accuracy from Random Forest Feature
Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in
Agriculturally Dominated Landscape. Agriculture. https://doi.org/10.3390/agriculture13010098
Kimijima, S., Sakakibara, M., & Nagai, M. (2022). Investigation of Long-Term Roving Artisanal and
Small-Scale Gold Mining Activities Using Time-Series Sentinel-1 and Global Surface Water
Datasets. International Journal of Environmental Research and Public Health, 19(9), 5530.
https://doi.org/10.3390/ijerph19095530
Li, W., Sun, K., Li, W., Wei, J., Miao, S., Gao, S., & Zhou, Q. (2023). Aligning Semantic Distribution in
Fusing Optical and SAR Images for Land Use Classification. Isprs Journal of Photogrammetry and
Remote Sensing, 199, 272–288. https://doi.org/10.1016/j.isprsjprs.2023.04.008
Masoudi, M., Centeri, C., Jakab, G., Nel, L. P., & Mojtahedi, M. (2021). GIS-Based Multi-Criteria and
Multi-Objective Evaluation for Sustainable Land-Use Planning (Case Study: Qaleh Ganj County,
Iran) “Landuse Planning Using MCE and Mola.” International Journal of Environmental Research,
15(3), 457–474. https://doi.org/10.1007/s41742-021-00326-0
Nguyen, H., Doan, T., & Radeloff, V. (2018). APPLYING RANDOM FOREST CLASSIFICATION TO
MAP LAND USE/LAND COVER USING LANDSAT 8 OLI. ISPRS – International Archives of
the
Photogrammetry, Remote Sensing and Spatial Information Sciences, 363–367.
https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-3-W4-363-2018
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2
Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12(14), 2291.
https://doi.org/10.3390/rs12142291
Rodriguez-Galiano, V., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sánchez, J. (2011). An
assessment of the effectiveness of a random forest classifier for land-cover classification. Isprs
Journal of Photogrammetry and https://doi.org/10.1016/J.ISPRSJPRS.2011.11.002 Remote Sensing, 67, 93–104.