Land Cover Change Dynamics And Potential Acid Sulfate Soil Formation in Segara Anakan
DOI:
https://doi.org/10.53824/ijddi.v5i2.111Keywords:
Land cover change, Acid sulfate soils, Tropical coastal wetlands, Remote sensing, Machine learning classificationAbstract
Tropical coastal regions are highly susceptible to acid sulfate soi formation due to ecological and hydrological changes driven by land cover dynamics and sedimentation. This study analyzes land cover changes from 1990 to 2025 and their implications for ASS development in Segara Anakan, Indonesia. Landsat imagery (Landsat 5 and Landsat 8/9 OLI) was classified using Random Forest and Gradient Boosting Tree algorithms within Google Earth Engine. Classification accuracy was assessed using overall accuracy and the Kappa coefficient. Land cover classes included mangrove, nipa palm, paddy fields, aquaculture ponds, settlements, bare land, water bodies, and forest. Results reveal substantial conversion of natural vegetation into paddy fields, bare land, and settlements, particularly in low-lying tidal areas. These changes disrupted ecological conditions that previously sustained organic matter accumulation, low-energy environments, and anaerobic waterlogging—three of the five key factors for ASS formation. Field validation confirmed soil pH < 4 in high-risk areas. This research demonstrates the effectiveness of integrating multi-temporal Landsat imagery with machine learning to detect spatio-temporal land cover dynamics and to identify areas prone to ASS formation, offering valuable insights for adaptive coastal management.
References
Darmawan, A. A., Suhardjono, Bisri, M., & Suhartanto, E. (2021). Assessment of spatial changes of LULC dynamics, using multi temporal landsat data (case study: Lesti Sub Watershed, Malang Regency, Indonesia). IOP Conference Series: Earth and Environmental Science, 930(1), 012075. https://doi.org/10.1088/1755-1315/930/1/012075
Farda, N. M. (2017). Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine. IOP Conference Series: Earth and Environmental Science, 98, 012042. https://doi.org/10.1088/1755-1315/98/1/012042
Florek, P., & Zagdański, A. (2023). Benchmarking state-of-the-art gradient boosting algorithms for classification. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.17094
Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300. https://doi.org/10.1016/j.patrec.2005.08.011
Hamza, M., & Larocque, D. (2005). An empirical comparison of ensemble methods based on classification trees. Journal of Statistical Computation and Simulation, 75(8), 629–643. https://doi.org/10.1080/00949650410001729472
Handoko, J., Herwindiati, D. E., & Hendryli, J. (2020). Gradient Boosting Tree for Land Use Change Detection Using Landsat 7 and 8 Imageries: A Case Study of Bogor Area as Water Buffer Zone of Jakarta. IOP Conference Series Earth and Environmental Science, 581(1), 012045–012045. https://doi.org/10.1088/1755-1315/581/1/012045
Hilmi, E., Sari, L. K., Cahyo, T. N., Amron, A., & Siregar, A. S. (2021). The Sedimentation Impact for the Lagoon and Mangrove Stabilization. E3S Web of Conferences, 324, 02001–02001. https://doi.org/10.1051/e3sconf/202132402001
Ihsan, K. T. N., Harto, A. B., Sakti, A. D., & Wikantika, K. (2023). Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based On Cloud Computation Google Earth Engine And Apps. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences/International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-M-3-2023, 109–114. https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-109-2023
Kulkarni, A., & Lowe, B. (2016). Random Forest Algorithm for Land Cover Classification. Computer Science Faculty Publications and Presentations. https://scholarworks.uttyler.edu/compsci_fac/1/
Mellor, A., Haywood, A., Stone, C., & Jones, S. (2013). The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification. Remote Sensing, 5(6), 2838–2856. https://doi.org/10.3390/rs5062838
Mendonça, S. K. G., Moraes , E. M. V. de , Otero, X. L., Ferreira, T. O., Corrêa, M. M., de Sousa, J. E. S., Nascimento , C. W. A. do , Neves, L. V. de M. W., & Junior, V. S. de S. (2020). Occurrence and pedogenesis of acid sulfate soils in northeastern Brazil. CATENA, 196, 104937–104937. https://doi.org/10.1016/j.catena.2020.104937
Sharapova, A. V., Semenkov, I. N., Karpachevsky, A. M., Lednev, S. A., & Koroleva, T. V. (2021). Morphological and chemical properties of soils within geological complexes affected by sulfuric acid in forest-steppe of the Central Russian Upland (Russia). IOP Conference Series Earth and Environmental Science, 862(1), 012013–012013. https://doi.org/10.1088/1755-1315/862/1/012013
Sharma, A., Liu, X., & Yang, X. (2018). Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks. Neural Networks, 105, 346–355. https://doi.org/10.1016/j.neunet.2018.05.019
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
Toivonen, J., Hudd, R., Nystrand, M., & Österholm, P. (2020). Climatic effects on water quality in areas with acid sulfate soils with commensurable consequences on the reproduction of burbot (Lota lota L.). Environmental Geochemistry and Health, 42(10), 3141–3156. https://doi.org/10.1007/s10653-020-00550-1
Widyatmanti, W., & Sammut, J. (2017). Hydro-geomorphic controls on the development and distribution of acid sulfate soils in Central Java, Indonesia. Geoderma, 308, 321–332. https://doi.org/10.1016/j.geoderma.2017.08.024
Yuniarti, E., Surono, N., Susilowati, D. N., & Anggria, L. (2022). Microbial activity of potential and actual acid sulphate soil from Kalimantan Island. IOP Conference Series Earth and Environmental Science, 976(1), 012047–012047. https://doi.org/10.1088/1755-1315/976/1/012047
Zakia, R., Lestari, F., & Susiana, S. (2022). Ecological suitability of mangrove ecosystems as mangrove rehabilitation areas in the Sei Carang estuary waters of Tanjungpinang City. Akuatikisle: Jurnal Akuakultur, Pesisir Dan Pulau-Pulau Kecil, 6(2), 149–155. https://doi.org/10.29239/j.akuatikisle.6.2.149-155
Zhao, C., Wu, D., Huang, J., Yuan, Y., Zhang, H.-T., Peng, R., & Shi, Z. (2020). BoostTree and BoostForest for Ensemble Learning. ArXiv.org. https://arxiv.org/abs/2003.09737
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Hyundra Zakiya Putri Wahyu, Wirastuti Widyatmanti, Sandy Budi Wibowo
This work is licensed under a Creative Commons Attribution 4.0 International License.
