Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article PeerReviewed |
| Language: | English |
| Published: |
Nature Research
2023
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/286357/1/scopus%20%282%29.bib https://repository.ugm.ac.id/286357/ |
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| Institution: | Universitas Gadjah Mada |
| Language: | English |
| Summary: | Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring plastic waste in river areas. This study aims to identify illegal dumping in a river area using the adjusted plastic index (API) and Sentinel-2 satellite imagery data. Rancamanyar River has been selected as the research area; it is one of the tributaries of Citarum Indonesia and is an open lotic-simple form, oxbow lake type river. Our study is the first attempt to construct an API and random forest machine learning using Sentinel-2 to identify the illegal dumping of plastic waste. The algorithm development integrated the plastic index algorithm with the normalized difference vegetation index (NDVI) and normalized buildup indices. For the validation process, the results of plastic waste image classification based on Pleiades satellite imagery and Unmanned Aerial Vehicle (UAV) photogrammetry was used. The validation results show that the API succeeded in improving the accuracy of identifying plastic waste, which gave a better correlation in the r-value and p-value by + 0.287014 and + 3.76 × 10−26 with Pleiades, and + 0.143131 and + 3.17 × 10−10 with UAV. © 2023, The Author(s). |
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