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Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Marine litter is a major global problem; it originates on land and enters the ocean via rivers, coastal erosion, and extreme events. Over time, marine litter collects in coastal areas. As a result, the research on litter dispersal and buildup is critical for successful coastal area management. Addressing the knowledge gap is critical for establishing successful solutions to fight that problem. In recent years, a variety of remote sensing techniques have been used to better understand litter abundance, distribution patterns, and dynamics in marine as well as coastal ecosystems. Marine litter detection and quantification are carried out using aircraft-based imaging systems, satellite images, and unmanned aerial vehicles (UAVs). The purpose of this study was to create a beach litter monitoring system or technical reference using a small UAV and geographic information system (GIS), with the test location at Batu Belig Beach, Badung Regency, Bali, Indonesia. The box-plot approach was used to determine the reflectance threshold on the orthophoto. GIS is used to determine the regions with and without litter based on the set threshold values. To verify the model, Slovin’s Formula was used to collect the sample, with a confusion matrix indicating an accuracy of 80%. This monitoring system provides a simple approach for identifying and measuring litter, even with only one person handling the entire operation. The outcomes of this analysis indicated that the majority of litter at the study location was made up of white plastic bags and styrofoam. As a last step, portraying litter abundance as a percentage per square meter was considered.
Twórcy
  • Doctor Study Program of Environmental Science, Graduate Program, Universitas Udayana, Jalan P.B. Sudirman, Denpasar, Bali 80232, Indonesia
  • Research Center for Environmental, The Institute of Research and Community Services, Universitas Udayana, Jalan P.B. Sudirman, Denpasar, Bali 80232, Indonesia
  • Doctor Study Program of Environmental Science, Graduate Program, Universitas Udayana, Jalan P.B. Sudirman, Denpasar, Bali 80232, Indonesia
  • Doctor Study Program of Environmental Science, Graduate Program, Universitas Udayana, Jalan P.B. Sudirman, Denpasar, Bali 80232, Indonesia
  • Doctor Study Program of Environmental Science, Graduate Program, Universitas Udayana, Jalan P.B. Sudirman, Denpasar, Bali 80232, Indonesia
  • Research Center for Oceanography, The Indonesian National Research and Innovation Agency (BRIN), BRIN Kawasan Jakarta Ancol Jalan Pasir Putih 1, Ancol Timur, Jakarta 14430, Indonesia
Bibliografia
  • 1. Aber, J.S., Marzolff, I., Ries, J.B., Aber, S.E.W. 2019. Small-format aerial photography and UAS imagery (Second Edi). Elsevier. https://doi.org/10.1016/C2016-0-03506-4
  • 2. Anadkat, A.P., Monisha, B.V, Puthineedi, M., Patnaik, A.K., Shekhar, R., Syed, R. Drone based solid waste detection using deep learning & image processing. In: Alliance International Conference on Artificial Intelligence and Machine Learning (AICAAM), April 2019, 357–364.
  • 3. Andriolo, U., Garcia-Garin, O., Vighi, M., Borrell, A., Gonçalves, G. 2022. Beached and Floating litter surveys by unmanned aerial vehicles: operational analogies and differences. Remote Sensing, 14(6), 1336. https://doi.org/10.3390/rs14061336
  • 4. Andriolo, U., Gonçalves, G., Rangel-Buitrago, N., Paterni, M., Bessa, F., Gonçalves, L. M.S., Sobral, P., Bini, M., Duarte, D., Fontán-Bouzas, Á., Gonçalves, D., Kataoka, T., Luppichini, M., Pinto, L., Topouzelis, K., Vélez-Mendoza, A., Merlino, S. 2021. Drones for litter mapping: an inter-operator concordance test in marking beached items on aerial images. Marine Pollution Bulletin, 169, 112542. https://doi.org/10.1016/j.marpolbul.2021.112542
  • 5. Andriolo, U., Gonçalves, G., Sobral, P., Fontán-Bouzas, Á., Bessa, F. 2020. Beach-dune morphodynamics and marine macro-litter abundance: An integrated approach with unmanned aerial System. Science of the Total Environment, 749, 141474. https://doi.org/10.1016/j.scitotenv.2020.141474
  • 6. Argeswara, J., Hendrawan, I.G., Dharma, I.G.B.S., Germanov, E. 2021. What’s in the soup? Visual characterization and polymer analysis of microplastics from an Indonesian manta ray feeding ground. Marine Pollution Bulletin, 168, 112427. https://doi.org/10.1016/j.marpolbul.2021.112427
  • 7. Bak, S.H., Hwang, D.H., Kim, H.M., Yoon, H.J., Hwang, D.H., Kim, H.M., Yoon, H.J. 2019. Detection and monitoring of beach litter using uav image and deep neural network. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W8, 55–58. https://doi.org/10.5194/isprs-archives-XLII-3-W8-55-2019
  • 8. Bali Partnership. 2019. Solving waste management issues together. https://www.balipartnership.org/
  • 9. Bao, Z., Sha, J., Li, X., Hanchiso, T., Shifaw, E. 2018. Monitoring of beach litter by automatic interpretation of unmanned aerial vehicle images using the segmentation threshold method. Marine Pollution Bulletin, 137(July), 388–398. https://doi.org/10.1016/j.marpolbul.2018.08.009
  • 10. Carmi, N. 2019. On social distress, littering and nature conservation: the case of Jisr A-Zarka. Coastal Management, 47(4), 347–361. https://doi.org/10.1 080/08920753.2019.1598223
  • 11. CNN Indonesia. 2021. The Environment and Forestry Service Transports 1,150 tons of garbage from the beach in badung, Bali. https://www.cnnindonesia.com/nasional/20211231045143-20-740842/dlhk-angkut1150-ton-sampah-kiriman-dari-pantai-di-badung-bali
  • 12. Conley, G., Zinn, S.C., Hanson, T., McDonald, K., Beck, N., Wen, H. 2022. Using a deep learning model to quantify trash accumulation for cleaner urban stormwater. Computers, Environment and Urban Systems, 93, 101752. https://doi.org/10.1016/j.compenvurbsys.2021.101752
  • 13. Cordova, M.R., Iskandar, M.R., Muhtadi, A., Nurhasanah, Saville, R., Riani, E. 2022. Spatio-temporal variation and seasonal dynamics of stranded beach anthropogenic debris on Indonesian beach from the results of nationwide monitoring. Marine Pollution Bulletin, 182, 114035. https://doi.org/10.1016/j.marpolbul.2022.114035
  • 14. Cordova, M.R., Nurhati, I. S. 2019. Major sources and monthly variations in the release of land-derived marine debris from the Greater Jakarta area, Indonesia. Scientific Reports, 9(1), 18730. https://doi.org/10.1038/s41598-019-55065-2
  • 15. Cózar, A., Aliani, S., Basurko, O.C., Arias, M., Isobe, A., Topouzelis, K., Rubio, A., Morales-Caselles, C. 2021. Marine Litter Windrows: A Strategic Target to Understand and Manage the Ocean Plastic Pollution. Frontiers in Marine Science, 8(February), 1–9. https://doi.org/10.3389/fmars.2021.571796
  • 16. Deidun, A., Gauci, A., Lagorio, S., Galgani, F. 2018. Optimising beached litter monitoring protocols through aerial imagery. Marine Pollution Bulletin, 131(February), 212–217. https://doi.org/10.1016/j.marpolbul.2018.04.033
  • 17. Directorate of Waste Management Directorate General of Waste and B3 Management Minister of Environment and Forestry. 2020. Achievement of Waste Management Performance. https://sipsn.menlhk.go.id/sipsn/
  • 18. Duhec, A.V., Jeanne, R.F., Maximenko, N., Hafner, J. 2015. Composition and potential origin of marine debris stranded in the western indian ocean on remote Alphonse Island, Seychelles. Marine Pollution Bulletin, 96(1–2), 76–86. https://doi.org/10.1016/j. marpolbul.2015.05.042
  • 19. Faizal, A., Samad, W., Werorilangi, S. 2019. Visible reflectance characteristics of marine debris in the sandy beach. Journal of Physics: Conference Series, 1341(2), 022011. https://doi.org/10.1088/1742-6596/1341/2/022011
  • 20. Faizal, A., Werorilangi, S., Samad, W. 2020. Spectral characteristics of plastic debris in the beach: case study of Makassar coastal area. Indonesian Journal of Geography, 52(1), 8–14. https://doi.org/10.22146/ijg.40519
  • 21. Freitas, S., Silva, H., Silva, E. 2021. Remote hyperspectral imaging acquisition and characterization for marine litter detection. Remote Sensing, 13(13), 1–22. https://doi.org/10.3390/rs13132536
  • 22. Garcia-Garin, O., Monleón-Getino, T., López-Brosa, P., Borrell, A., Aguilar, A., Borja-Robalino, R., Cardona, L.,Vighi, M. 2021. Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R. Environmental Pollution, 273, 116490. https://doi.org/10.1016/j.envpol.2021.116490
  • 23. García-Rivera, S., Lizaso, J.L.S., Millán, J.M.B. 2018. Spatial and temporal trends of marine litter in the Spanish Mediterranean seafloor. Marine Pollution Bulletin, 137, 252–261. https://doi.org/10.1016/j.marpolbul.2018.09.051
  • 24. Geraeds, M., van Emmerik, T., de Vries, R., bin Ab Razak, M. S. 2019. Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs). Remote Sensing, 11(17), 2045. https://doi.org/10.3390/rs11172045
  • 25. Goddijn-Murphy, L., Peters, S., van Sebille, E., James, N.A., Gibb, S. 2018. Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics. Marine Pollution Bulletin, 126(October 2017), 255–262. https://doi.org/10.1016/j. marpolbul.2017.11.011
  • 26. Goddijn-Murphy, L., Williamson, B. J., McIlvenny, J., Corradi, P., Goddijn‐murphy, L., Williamson, B. J., McIlvenny, J., Corradi, P. 2022. Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sensing, 14(13), 3179. https://doi.org/10.3390/rs14133179
  • 27. Gonçalves, G., Andriolo, U., Gonçalves, L.M.S., Sobral, P., Bessa, F. 2022. Beach litter survey by drones: Mini-review and discussion of a potential standardization. Environmental Pollution, 315(September), 120370. https://doi.org/10.1016/j.envpol.2022.120370
  • 28. Gonçalves, G., Andriolo, U., Gonçalves, L., Sobral, P., Bessa, F. 2020. Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods. Remote Sensing, 12(16), 1–19. https://doi.org/10.3390/rs12162599
  • 29. Gonçalves, G., Andriolo, U., Pinto, L., Bessa, F. 2020. Mapping marine litter using UAS on a beach-dune system: a multidisciplinary approach. Science of The Total Environment, 706, 135742. https://doi.org/10.1016/j.scitotenv.2019.135742
  • 30. González-Fernández, D., Hanke, G. 2017. Toward a Harmonized Approach for Monitoring of Riverine Floating Macro Litter Inputs to the Marine Environment. Frontiers in Marine Science, 4. https://doi.org/10.3389/fmars.2017.00086
  • 31. González, D., Hanke, G., Tweehuysen, G., Bellert, B., Holzhauer, M., Palatinus, A., Hohenblum, P., Oosterbaan, L. 2016. Riverine Litter Monitoring - Options and Recommendations - Thematic Report. In JRC Scientific and Technical Reports (Issue February). https://doi.org/10.2788/461233
  • 32. Grelaud, M., Ziveri, P. 2020. The generation of marine litter in Mediterranean island beaches as an effect of tourism and its mitigation. Scientific Reports, 10(1), 20326. https://doi.org/10.1038/s41598-020-77225-5
  • 33. Gunawan, H., Yeny, I., Karlina, E., Suharti, S., Murniati, Subarudi, Mulyanto, B., Ekawati, S., Garsetiasih, R., Pratiwi, Sumirat, B. K., Sawitri, R., Heriyanto, N. M., Takandjandji, M., Widarti, A., Surati, Desmiwati, Kalima, T., Effendi, R., Nurlia, A. 2022. Integrating social forestry and biodiversity conservation in Indonesia. Forests, 13(12), 2152. https://doi.org/10.3390/f13122152
  • 34. Hajar, N. R. 2019. The Potential Effects of Marine Litter on Tourism at Kuta Beach, Bali : A systemic analysis.
  • 35. Hardesty, B.D., Roman, L., Leonard, G.H., Mallos, N., Pragnell-Raasch, H., Campbell, I., Wilcox, C. 2021. Socioeconomics effects on global hotspots of common debris items on land and the seafloor. Global Environmental Change, 71, 102360. https://doi.org/10.1016/j.gloenvcha.2021.102360
  • 36. Haseler, M., Schernewski, G., Balciunas, A., Sabaliauskaite, V. 2018. Monitoring methods for large micro-and meso-litter and applications at Baltic beaches. Journal of Coastal Conservation, 22(1), 27–50. https://doi.org/10.1007/s11852-017-0497-5
  • 37. Jakovljevic, G., Govedarica, M., Alvarez-Taboada, F. 2020. A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data. Remote Sensing, 12(9), 1515. https://doi.org/10.3390/rs12091515
  • 38. Kako, S., Isobe, A., Magome, S. 2012. Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera. Marine Pollution Bulletin, 64(6), 1156–1162. https://doi.org/10.1016/j.marpolbul.2012.03.024
  • 39. Kiessling, T., Salas, S., Mutafoglu, K., Thiel, M. 2017. Who cares about dirty beaches? Evaluating environmental awareness and action on coastal litter in Chile. Ocean and Coastal Management, 137, 82–95. https://doi.org/10.1016/j.ocecoaman.2016.11.029
  • 40. Kikaki, A., Karantzalos, K., Power, C.A., Raitsos, D.E. 2020. Remotely sensing the source and transport of marine plastic debris in bay islands of Honduras (Caribbean Sea). Remote Sensing, 12(11), 1727. https://doi.org/10.3390/rs12111727
  • 41. Kraft, M., Piechocki, M., Ptak, B., Walas, K. 2021. Autonomous, onboard vision-based trash and litter detection in low altitude aerial images collected by an unmanned aerial vehicle. Remote Sensing, 13(5), 965. https://doi.org/10.3390/rs13050965
  • 42. Kylili, K., Kyriakides, I., Artusi, A., Hadjistassou, C. 2019. Identifying floating plastic marine debris using a deep learning approach. Environmental Science and Pollution Research, 26(17), 17091–17099. https://doi.org/10.1007/s11356-019-05148-4
  • 43. Lam, T. W.L., Fok, L., Ma, A.T.H., Li, H.-X., Xu, X.-R., Cheung, L.T.O., Wong, M.H. 2022. Microplastic contamination in marine-cultured fish from the Pearl River Estuary, South China. Science of The Total Environment, 827, 154281. https://doi.org/10.1016/j.scitotenv.2022.154281
  • 44. Lebreton, L.C.M., Greer, S.D., Borrero, J.C. 2012. Numerical modelling of floating debris in the world’s oceans. Marine Pollution Bulletin, 64(3), 653–661. https://doi.org/10.1016/j.marpolbul.2011.10.027
  • 45. Leslie, H.A., van Velzen, M.J.M., Brandsma, S.H., Vethaak, A.D., Garcia-Vallejo, J.J., Lamoree, M.H. 2022. Discovery and quantification of plastic particle pollution in human blood. Environment International, 107199. https://doi.org/10.1016/j.envint.2022.107199
  • 46. Lestari, P., Trihadiningrum, Y. 2019. The impact of improper solid waste management to plastic pollution in Indonesian coast and marine environment. Marine Pollution Bulletin, 149, 110505. https://doi.org/10.1016/j.marpolbul.2019.110505
  • 47. Liro, M., Emmerik, T. van, Wyżga, B., Liro, J., Mikuś, P. 2020. Macroplastic storage and remobilization in rivers. Water, 12(7), 2055. https://doi.org/10.3390/w12072055
  • 48. Liu, T.K., Wang, M.W., Chen, P. 2013. Influence of waste management policy on the characteristics of beach litter in Kaohsiung, Taiwan. Marine Pollution Bulletin, 72(1), 99–106. https://doi.org/10.1016/j.marpolbul.2013.04.015
  • 49. Lo, H.-S., Wong, L.-C., Kwok, S.-H., Lee, Y.-K., Po, B. H.-K., Wong, C.-Y., Tam, N. F.-Y., Cheung, S.-G. 2020. Field test of beach litter assessment by commercial aerial drone. Marine pollution bulletin, 151, 110823. https://doi.org/10.1016/j.marpolbul.2019.110823
  • 50. Maharjan, N., Miyazaki, H., Pati, B.M., Dailey, M.N., Shrestha, S., Nakamura, T. 2022. Detection of river plastic using uav sensor data and deep learning. remote sensing, 14(13). https://doi.org/10.3390/rs14133049
  • 51. Mandal, K., Dey, P. 2022. Coastal vulnerability analysis and RIDIT scoring of socio-economic vulnerability indicators – A case of Jagatsinghpur, Odisha. International Journal of Disaster Risk Reduction, 79, 103143. https://doi.org/10.1016/j. ijdrr.2022.103143
  • 52. Mandirola, M., Casarotti, C., Peloso, S., Lanese, I., Brunesi, E., Senaldi, I., Risi, F., Monti, A., Facchetti, C. 2021. Guidelines for the use of Unmanned Aerial Systems for fast photogrammetry-oriented mapping in emergency response scenarios. International Journal of Disaster Risk Reduction, 58, 102207. https://doi.org/10.1016/j.ijdrr.2021.102207
  • 53. Martin, C., Parkes, S., Zhang, Q., Zhang, X., McCabe, M.F., Duarte, C.M. 2018. Use of unmanned aerial vehicles for efficient beach litter monitoring. Marine Pollution Bulletin, 131, 662–673. https://doi.org/10.1016/j.marpolbul.2018.04.045
  • 54. Mauludy, M.S., Yunanto, A., Yona, D. 2019. Microplastic Abundances in the Sediment of Coastal Beaches in Badung, Bali. Jurnal Perikanan Universitas Gadjah Mada, 21(2), 73. https://doi.org/10.22146/jfs.45871
  • 55. Maximenko, N., Corradi, P., Law, K.L., Sebille, E. Van, Garaba, S.P., Lampitt, R.S., Galgani, F., Martinez-Vicente, V., Goddijn-Murphy, L., Veiga, J.M., Thompson, R.C., Maes, C., Moller, D., Löscher, C.R., Addamo, A.M., Lamson, M.R., Centurioni, L.R., Posth, N.R., Lumpkin, R., Wilcox, C. 2019. Towards the integrated marine debris observing system. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00447
  • 56. Mecho, A., Sellanes, J., Aguzzi, J. 2021. Seafloor litter at oceanic islands and seamounts of the southeastern Pacific. Marine Pollution Bulletin, 170, 112641. https://doi.org/10.1016/j.marpolbul.2021.112641
  • 57. Meijer, L.J.J., van Emmerik, T., van der Ent, R., Schmidt, C., Lebreton, L. 2021. More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean. Science Advances, 7(18), 1–14. https://doi.org/10.1126/sciadv.aaz5803
  • 58. Merlino, S., Paterni, M., Berton, A., Massetti, L. 2020. Unmanned Aerial Vehicles for Debris Survey in Coastal Areas: Long-Term Monitoring Programme to Study Spatial and Temporal Accumulation of the Dynamics of Beached Marine Litter. Remote Sensing, 12(8), 1260. https://doi.org/10.3390/rs12081260
  • 59. Merlino, S., Paterni, M., Locritani, M., Andriolo, U., Gonçalves, G., Massetti, L. 2021. Citizen science for marine litter detection and classification on unmanned aerial vehicle images. Water, 13(23), 3349. https://doi.org/10.3390/w13233349
  • 60. Miladinova, S., Macias, D., Stips, A., Garcia-Gorriz, E. 2020. Identifying distribution and accumulation patterns of floating marine debris in the Black Sea. Marine Pollution Bulletin, 153, 110964. https://doi.org/10.1016/j.marpolbul.2020.110964
  • 61. Moy, K., Neilson, B., Chung, A., Meadows, A., Castrence, M., Ambagis, S., Davidson, K. 2018. Mapping coastal marine debris using aerial imagery and spatial analysis. Marine Pollution Bulletin, 132, 52–59. https://doi.org/10.1016/j.marpolbul.2017.11.045
  • 62. Muhajir, A. 2019. This is the latest data and sources of garbage in Bali. In Mongabay. https://www.mongabay.co.id/2019/07/02/inilah-data-dan-sumber-sampah-terbaru-di-bali/
  • 63. Nazerdeylami, A., Majidi, B., Movaghar, A. 2021. Autonomous litter surveying and human activity monitoring for governance intelligence in coastal eco-cyber-physical systems. Ocean & Coastal Management, 200, 105478. https://doi.org/10.1016/j.ocecoaman.2020.105478
  • 64. Over, J.-S.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D.D., Noble, T., Sherwood, C. R., Warrick, J.A., Wernette, P.A., Jenna A.,B., Buscombe, D.D., Noble, T., Sherwood, C.R., Warrick, J.A., Wernette, P.A. 2021. Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation. In U.S. Geological Survey Open-File Report 2021–1039. https://doi.org/10.3133/ofr20211039
  • 65. Papakonstantinou, A., Batsaris, M., Spondylidis, S., Topouzelis, K. 2021. A citizen science unmanned aerial system data acquisition protocol and deep learning techniques for the automatic detection and mapping of marine litter concentrations in the coastal zone. Drones, 5(1), 1–21. https://doi.org/10.3390/ drones5010006
  • 66. Phelan, A.A., Ross, H., Setianto, N.A., Fielding, K., Pradipta, L. 2020. Ocean plastic crisis—mental models of plastic pollution from remote Indonesian coastal communities. PLOS ONE, 15(7), e0236149. https://doi.org/10.1371/journal.pone.0236149
  • 67. Pinto, L., Andriolo, U., Gonçalves, G. 2021. Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network. Marine Pollution Bulletin, 169(16), 112594. https://doi.org/10.1016/j.marpolbul.2021.112594
  • 68. Rochwulaningsih, Y., Sulistiyono, S.T., Masruroh, N.N., Maulany, N.N. 2019. Marine policy basis of Indonesia as a maritime state: The importance of integrated economy. Marine Policy, 108, 103602. https://doi.org/10.1016/j.marpol.2019.103602
  • 69. Sakti, A.D., Sembiring, E., Rohayani, P., Fauzan, K.N., Anggraini, S., Santoso, C., Patricia, V.A., Titon, K., Ihsan, N., Ramadan, A. H., Arjakusuma, S., Candra, D. S. 2023. Identification of Illegally Dumped Plastic Waste in a Highly Polluted River in Indonesia Using Sentinel-2 Satellite Imagery. Scientific Reports, 1–16. https://doi.org/10.1038/s41598-023-32087-5
  • 70. Schuyler, Q., Willis, K., Lawson, T.J., Mann, V., Wilcox, C., Hardesty, B.D. 2020. Handbook of Survey Methodology - Plastics Leakage. CSIRO, Australia, EP178700, 1–47.
  • 71. Secretariat of the Convention on Biological Diversity 2016. Marine Debris: Understanding, Preventing and Mitigating the Significant Adverse Impacts on Marine and Coastal Biodiversity. In CBD Technical Series, 83. https://www.cbd.int/doc/publications/cbd-ts-83-en.pdf
  • 72. Serafino, F., Bianco, A. 2021. Use of X-Band Radars to Monitor Small Garbage Islands. Remote Sensing, 13(18), 3558. https://doi.org/10.3390/rs13183558
  • 73. Suteja, Y., Atmadipoera, A.S., Riani, E., Nurjaya, I.W., Nugroho, D., Purwiyanto, A.I.S. 2021. Stranded marine debris on the touristic beaches in the south of Bali Island, Indonesia: The spatiotemporal abundance and characteristic. Marine Pollution Bulletin, 173(PA), 113026. https://doi.org/10.1016/j.marpolbul.2021.113026
  • 74. Topouzelis, K., Papageorgiou, D., Karagaitanakis, A., Papakonstantinou, A., Ballesteros, M.A. 2020. Remote sensing of sea surface artificial floating plastic targets with Sentinel-2 and unmanned aerial systems (plastic litter project 2019). Remote Sensing, 12(12). https://doi.org/10.3390/rs12122013
  • 75. Topouzelis, K., Papakonstantinou, A., Garaba, S.P. 2019. Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018). International Journal of Applied Earth Observation and Geoinformation, 79, 175–183. https://doi.org/10.1016/j.jag.2019.03.011
  • 76. van Emmerik, T., Roebroek, C., de Winter, W., Vriend, P., Boonstra, M., Hougee, M. 2020. Riverbank macrolitter in the Dutch Rhine–Meuse delta. Environmental Research Letters, 15(10), 104087. https://doi.org/10.1088/1748-9326/abb2c6
  • 77. van Emmerik, T., Schwarz, A. 2020. Plastic debris in rivers. WIREs Water, 7(1), 1–24. https://doi.org/10.1002/wat2.1398
  • 78. van Emmerik, T., Seibert, J., Strobl, B., Etter, S., den Oudendammer, T., Rutten, M., bin Ab Razak, M. S., van Meerveld, I. 2020. Crowd-Based Observations of Riverine Macroplastic Pollution. Frontiers in Earth Science, 8. https://doi.org/10.3389/ feart.2020.00298
  • 79. van Lieshout, C., van Oeveren, K., van Emmerik, T., Postma, E. 2020. Automated River Plastic Monitoring Using Deep Learning and Cameras. Earth and Space Science, 7(8). https://doi.org/10.1029/2019EA000960
  • 80. Veiga, J.M., Fleet, D., Kinsey, S., Nilsson, P., Vlachogianni, T., Werner, S., Galgani, F., Thompson, R.C., Dagevos, J., Gago, J., Sobral, P., Cronin, R. 2016. JRC Technical Report -Identifying Sources of Marine Litter. https://doi.org/10.2788/018068
  • 81. Vriend, P., Roebroek, C.T.J., van Emmerik, T. 2020. Same but different: a framework to design and compare riverbank plastic monitoring strategies. Frontiers. Water, 2. https://doi.org/10.3389/frwa.2020.563791
  • 82. Wang, P., Li, X., Tang, J., Yang, J., Ma, Y., Wu, D., Huo, Z. 2023. Determining the critical threshold of meteorological heat damage to tea plants based on MODIS LST products for tea planting areas in China. Ecological Informatics, 77, 102235. https://doi.org/10.1016/j.ecoinf.2023.102235
  • 83. Williams, A.T., Rangel-Buitrago, N.G., Anfuso, G., Cervantes, O., Botero, C.M. 2016. Litter impacts on scenery and tourism on the Colombian north Caribbean coast. Tourism Management, 55, 209–224. https://doi.org/10.1016/j.tourman.2016.02.008
  • 84. Wolf, M., van den Berg, K., Garaba, S.P., Gnann, N., Sattler, K., Stahl, F., Zielinski, O. 2020. Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q). Environmental Research Letters, 15(11), 114042. https://doi.org/10.1088/1748-9326/abbd01
  • 85. Xiao, Y., Dai, S., Xiao, C., Xu, X. 2022. Research and Development of a Real-time UAV Flight Visualization Simulation System. Journal of Physics: Conference Series, 2218(1), 012081. https://doi.org/10.1088/1742-6596/2218/1/012081
  • 86. Yang, Z., Yu, X., Dedman, S., Rosso, M., Zhu, J., Yang, J., Xia, Y., Tian, Y., Zhang, G., Wang, J. 2022. UAV remote sensing applications in marine monitoring: Knowledge visualization and review. Science of The Total Environment, 155939. https://doi.org/10.1016/j.scitotenv.2022.155939
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c2c99d6a-cb7f-47a5-805e-ddc9586224b6
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