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: Sakti, Anjar Dimara, Sembiring, Emenda, Rohayani, Pitri, Fauzan, Kamal Nur, Anggraini, Tania Septi, Santoso, Cokro, Patricia, Vinka Aprilla, Ihsan, Kalingga Titon Nur, Ramadan, Attar Hikmahtiar, Arjasakusuma, Sanjiwana, Candra, Danang Surya
Format: Article PeerReviewed
Language:English
Published: Nature Research 2023
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Online Access:https://repository.ugm.ac.id/286357/1/scopus%20%282%29.bib
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Institution: Universitas Gadjah Mada
Language: English
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spelling id-ugm-repo.2863572024-08-29T02:20:19Z https://repository.ugm.ac.id/286357/ Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery Sakti, Anjar Dimara Sembiring, Emenda Rohayani, Pitri Fauzan, Kamal Nur Anggraini, Tania Septi Santoso, Cokro Patricia, Vinka Aprilla Ihsan, Kalingga Titon Nur Ramadan, Attar Hikmahtiar Arjasakusuma, Sanjiwana Candra, Danang Surya Photogrammetry and Remote Sensing 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). Nature Research 2023 Article PeerReviewed text/html en https://repository.ugm.ac.id/286357/1/scopus%20%282%29.bib Sakti, Anjar Dimara and Sembiring, Emenda and Rohayani, Pitri and Fauzan, Kamal Nur and Anggraini, Tania Septi and Santoso, Cokro and Patricia, Vinka Aprilla and Ihsan, Kalingga Titon Nur and Ramadan, Attar Hikmahtiar and Arjasakusuma, Sanjiwana and Candra, Danang Surya (2023) Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery. Scientific Reports, 13 (1). 10.1038/s41598-023-32087-5
institution Universitas Gadjah Mada
building UGM Library
continent Asia
country Indonesia
Indonesia
content_provider UGM Library
collection Repository Civitas UGM
language English
topic Photogrammetry and Remote Sensing
spellingShingle Photogrammetry and Remote Sensing
Sakti, Anjar Dimara
Sembiring, Emenda
Rohayani, Pitri
Fauzan, Kamal Nur
Anggraini, Tania Septi
Santoso, Cokro
Patricia, Vinka Aprilla
Ihsan, Kalingga Titon Nur
Ramadan, Attar Hikmahtiar
Arjasakusuma, Sanjiwana
Candra, Danang Surya
Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
description 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).
format Article
PeerReviewed
author Sakti, Anjar Dimara
Sembiring, Emenda
Rohayani, Pitri
Fauzan, Kamal Nur
Anggraini, Tania Septi
Santoso, Cokro
Patricia, Vinka Aprilla
Ihsan, Kalingga Titon Nur
Ramadan, Attar Hikmahtiar
Arjasakusuma, Sanjiwana
Candra, Danang Surya
author_facet Sakti, Anjar Dimara
Sembiring, Emenda
Rohayani, Pitri
Fauzan, Kamal Nur
Anggraini, Tania Septi
Santoso, Cokro
Patricia, Vinka Aprilla
Ihsan, Kalingga Titon Nur
Ramadan, Attar Hikmahtiar
Arjasakusuma, Sanjiwana
Candra, Danang Surya
author_sort Sakti, Anjar Dimara
title Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
title_short Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
title_full Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
title_fullStr Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
title_full_unstemmed Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery
title_sort identification of illegally dumped plastic waste in a highly polluted river in indonesia using sentinel-2 satellite imagery
publisher Nature Research
publishDate 2023
url https://repository.ugm.ac.id/286357/1/scopus%20%282%29.bib
https://repository.ugm.ac.id/286357/
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