Quantifying marine macro litter abundance on a sandy beach using unmanned aerial systems and object-oriented machine learning methods

UIDB 00308/2020 REM/30324/2017 IT057-18-7252 UIDB/04292/2020Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.publishersversionpublishe

Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences

The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys

Mini-review and discussion of a potential standardization

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(15 December), 1-8. [120370]. https://doi.org/10.1016/j.envpol.2022.120370The abundance of beach litter has been increasing globally during the last decades, and it is an issue of global concern. A new survey strategy, based on uncrewed aerial vehicles (UAV, aka drones), has been recently adopted to improve the monitoring of beach macro-litter items abundance and distribution. This work identified and analysed the 15 studies that used drone for beach litter surveys on an operational basis. The analysis of technical parameters for drone flight deployment revealed that flight altitude varied between 5 and 40 m. The analysis of final assessments showed that, through manual and/or automated items detection on images, most of studies provided litter bulk characteristics (type, material and size), along with litter distribution maps. The potential standardization of drone-based litter survey would allow a comparison among surveys, however it seems difficult to propose a standard set of flight parameters, given the wide variety of coastal environments, the different devices available, and the diverse objectives of drone-based litter surveys. On the other hand, in our view, a set of common outcomes can be proposed, based on the grid mapping process, which can be easily generated following the procedure indicated in the paper. This work sets the ground for the development of a standardized protocol for drone litter data collection, analysis and assessments. This would allow the provision of broad scale comparative studies to support coastal management at both national and international scales.publishersversionpublishe

Geospatial Methods for Mapping Domestic Waste Piles and Macro Plastics

There are growing concerns about the threats posed by plastics to human society and natural ecosystems. There is evidence of the harm presented to economies, public health and society. Although plastic pollution is an issue of great concern, low- and middle-income countries lack waste disposal services and this lead to disposal of waste including plastics into the environment. Monitoring presence of waste disposed into the environment is crucial for assessment of remedial measures . Traditional approach for identifying locations with plastic and waste accumulation in the environment involves field surveys, and drone technology is an emerging technology being applied for mapping the presence of plastics and waste in the environment. In this study, I have presented basic requirements for collecting data using Unmanned Aerial Vehicles (UAV) to map plastics and accumulation of domestic waste in the environment. For example, it was observed that a Ground Sampling Distance (GSD) of 2.51 cm is too coarse for mapping plastics of size less than 10 cm. Additionally, the study has also utilized random forest as a machine learning algorithm to classify and identify plastics and waste piles from UAV-derived imagery in a densely populated area of Blantyre, Malawi. The random forest predictions show high performance compared to prior studies for both waste piles (Precision: 0.9048, Recall: 0.95, and F-score: 0.9268) and plastics detection (Precision: 0.8905, Recall: 0.9421, and F-score: 0.9156). With the reported accuracies, UAV imagery can be employed to guide environmental policy implementation by helping in monitoring the effectiveness of policies that have been set to mitigate and address problems such as open waste dumping

Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress

From the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable performance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were conducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.Engineering and Physical Sciences Research Council (EPSRC): Industrial Case Studentship Voucher 586 Number 16000001. The Smith Institute, EDF Energy

From Pollution to Solution: A global assessment of marine litter and plastic pollution

This assessment describes the far-reaching impacts of plastics in our oceans and across the planet. Plastics are a marker of the current geological era, the Anthropocene (Zalasiewicz et al. 2016). They have given their name to a new microbial habitat known as the plastisphere (Amaral-Zettler et al. 2020; see Glossary). Increased awareness of the negative impacts of microplastics on marine ecosystems and human health has led them to be referred to as a type of "Ocean PM2.5" akin to air pollution (i.e. particulate matter less than 2.5 micrometres [?m] in diameter) (Shu 2018). With cumulative global production of primary plastic between 1950 and 2017 estimated at 9,200 million metric tons and forecast to reach 34 billion metric tons by 2050 (Geyer 2020) (Figure i), the most urgent issues now to be addressed are how to reduce the volume of uncontrolled or mismanaged waste streams going into the oceans (Andrades et al. 2018) and how to increase the level of recycling. Of the 7 billion tons of plastic waste generated globally so far, less than 10 per cent has been recycled (Geyer 2020)

Remote Sensing in Applications of Geoinformation

Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis