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Random Forest–based Mapping of Riverine Plastic Pollution from SENTINEL-2 in Google Earth Engine: Lulc and Rainfall Controls in Kendal Regency, Indonesia
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Using Sentinel-2 satellite imagery and random forest machine learning in Google Earth Engine, researchers mapped riverine plastic pollution across Kendal Regency, Indonesia, finding that plastic accumulation is strongly linked to settlement and industrial land cover rather than rainfall patterns. This remote sensing approach offers a scalable method for monitoring plastic pollution hotspots and reveals that urbanization is the dominant spatial driver of riverine plastic contamination.
Plastic pollution is a major environmental issue, especially in riverine and urban systems.Understanding its spatial distribution relative to land use/land cover (LULC) and precipitation is crucial.This study examined plastic waste distribution in Kendal Regency using a Plastic Index (PI) derived from remote sensing, Sentinel-2-based LULC classification, and precipitation data.Statistical analyses included boxplots, swarmplots, and correlation tests.PI values differed across LULC types, with higher values in settlements and industrial areas, and lower or negative values in forests and plantations.Irrigated paddies and water bodies showed high variability.In contrast, precipitation showed weak, inconsistent, and non-significant correlations with PI.Plastic accumulation is strongly linked to anthropogenic land cover rather than rainfall.The results highlight urbanization as a key driver of plastic pollution and provide insights for sustainable waste management strategies.
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