The disposal of waste materials such as plastic, foam, cloth, paper, rubber, and wood in river areas significantly impacts local environments, affecting regions several kilometers away and even reaching the open sea. This study utilized data from the Sentinel-1 C-SAR spanning 2017–2024 to detect water bodies. Additionally, an adaptive decision tree algorithm method with the Otsu threshold was applied to the Sentinel-2 optical data of March 19, 2024, to identify aquatic vegetation and floating waste (non-vegetative objects) within the water body. The study focused on the inlet of the Saguling Reservoir, where the Citarum River flows near the Bridge of Babakan Sapan (BBS) in West Java Province. This location is known for frequent waste accumulation. The methodology was then employed to monitor accumulated waste along the Citarum River on June 17, 22, and July 12, 2024. The model demonstrated an overall accuracy of 86% and a Kappa coefficient of 79% in distinguishing waste, water, and vegetation, with most errors occurring in the vegetation and waste classes. Additionally, the model's effectiveness in detecting waste in narrow water bodies with densely populated buildings on both sides, as observed in a narrow river along slum areas in Cirebon City, was comparatively lower.


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    Titel :

    Adaptive Decision Tree Algorithm Using Remote Sensing Data to Monitor Waste Disposal Along Citarum River in West Java Province, Indonesia




    Erscheinungsdatum :

    08.11.2024


    Format / Umfang :

    625657 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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