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Field test of beach litter assessment by commercial aerial drone
Marine Pollution Bulletin 151 (2020) 110823
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Field test of beach litter assessment by commercial aerial drone a
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Hoi-Shing Lo , Leung-Chun Wong , Shu-Hin Kwok , Yan-Kin Lee , Beverly Hoi-Ki Po , ⁎ Chun-Yuen Wonga, Nora Fung-Yee Tama, Siu-Gin Cheunga,c, a b c
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Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada State Key Laboratory of Marine Pollution, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region
ARTICLE INFO
ABSTRACT
Keywords: Drone Beach litter assessment
The visual survey is the most common method to quantify and characterize beach litter. However, it is very labor intensive and difficult to carry out on beaches which are remote or difficult to access. We suggest an alternative approach for assessing beach litter using an unmanned aerial vehicle (UAV), or aerial drone, with automated image requisition and processing. Litter of different sizes, colours, and materials were placed randomly on two beaches. Images of beaches with different substrates were obtained by the drone at different operating heights and light conditions and litter on the beaches was identified from the photos by untrained personnel. The quantification of beach litter using the drone was three times faster than that by visual census. This study has demonstrated the potential of using the drone as a cost-effective and an efficient sampling method in routine beach litter monitoring programs.
1. Introduction Marine litter includes all types of persistent manufactured or processed solid material discarded in the marine environment, regardless of whether they are dumped purposely or unintentionally (Duhec et al., 2015). Legislative frameworks (e.g., the European Union's Marine Strategy Framework Directive (MSFD)) require regular monitoring of the trend in marine litter deposition on coastlines. Not surprisingly, plastics are the most dominant type of marine litter both at the local and global scale. It is estimated that 5.25 trillion pieces of plastic debris weighing 269,000 tons were dispersed in oceans in both Northern and Southern Hemispheres (Eriksen et al., 2014) and the number will keep increasing globally. In fact, a large amount of marine litter eventually ends up on shores around the world, even in very remote areas (Cole et al., 2011). Increasing volumes of beach litter constitutes a threat to ecological, economic, recreational and aesthetic values. Physical impacts such as entanglement and ingestion of plastic litter by marine life are examples of threat which have raised public concerns in the last decade (Gall and Thompson, 2015). The degradation of the aesthetic value of beaches results in the loss of revenue from tourism and incurs a cost in the coastal cleanup. Routine monitoring of beaches is therefore vital in identifying hotspots of beach litter and providing valuable information for the establishment of an efficient cleanup strategy.
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Guidelines have been set up by both authorities and NGOs to simplify the data collection process and minimize training required for beach litter monitoring, so that volunteers can help with the monitoring work which can become part of citizen science programs. Several protocols have been prepared by NOAA, IOC, OSPAR and the European Commission (Cheshire et al., 2009; Galgani et al., 2013; Opfer et al., 2012; OSPAR Commission, 2010).The most common approach in monitoring programs involves visual survey of beach litter by people walking along transect lines of 100 m long from strandline to the water's edge. The survey normally requires 3–5 personnel to complete in about 3 h. These manual surveys can provide accurate estimates of the litter standing stock but it is labor intensive and time consuming. The classification of beach litter, however, is subjected to the judgement of the participants, hence depends on their skills and experience. In addition, existing guidelines employ different approaches in the survey which make data comparison difficult. For example, the classification of marine litter varies between the guideline of OSPAR which includes medical waste and that of MSFD which does not. Accessibility of the beach is another concern as it is difficult and sometimes dangerous to conduct surveys in inaccessible areas. As the problem of marine litter is getting worse, new monitoring approaches which are more cost-effective and efficient with minimal labor are needed. This study employed an alternative approach in beach litter assessment using a commercial aerial drone. The rising popularity of the
Corresponding author at: Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region. E-mail address: [email protected] (S.-G. Cheung).
https://doi.org/10.1016/j.marpolbul.2019.110823 Received 1 August 2019; Received in revised form 25 November 2019; Accepted 10 December 2019 Available online 29 January 2020 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.
Marine Pollution Bulletin 151 (2020) 110823
H.-S. Lo, et al.
drone has raised the interest of its applications in various environmental field studies. Recent examples include air pollution monitoring (Alvear et al., 2017); vegetation classification (Huang et al., 2017); evaluation of topographic change (Lizarazo et al., 2017) and identification of animals (Longmore et al., 2017). To the best of our knowledge, the application of drone in beach litter assessment has been reported in two studies only. Deidun et al. (2018) developed a protocol to assess the litter standing stock along coastal stretches in the Maltese Islands, and tried to mainstream the methodology within national and regional monitoring programs for marine litter. On the other hand, Martin et al. (2018) employed a drone with the aid of a beta version of the machine learning tool in litter assessment along the Saudi Arabian Red Sea coastline, and compared the results with the visual census approach. Both studies have demonstrated that the drone is a powerful tool in beach litter monitoring with high efficiency in quantifying beach litter (e.g., 40 times faster than the visual census in the study of Martin et al., 2018). However, miscounting and/or misidentification of litter are major problems of the drone, especially for natural objects, such as rocks and plant materials, which are sometimes misidentified as beach litter. The performance of imaging was also affected by time of day, weather condition, and flight altitude. Therefore, the main goal of this study is to investigate how the operating conditions (i.e., weather condition, time of day, substrate heterogeneity and flight altitude) for the drone affected the accuracy of litter identification and provide recommendations for optimizing the protocol for beach litter assessment.
400 s–1/1000 s, depending on time of the day and weather condition) to avoid blurry images. The photos taken at each height were automatically aligned to generate a high resolution orthomosaic photo by Agisoft Photoscan Professional (version 1.5.0). Photo alignment required an overlap of > 70% of the area of each photo with the successive one which was achieved by varying the speed of the drone at different altitudes (0.8 m s−1 at 5 m, 2 m s−1 at 10 m, 3.5 m s−1 at 15 m AGL). The resolution of the orthomosaic photos was ~0.20 cm pixel−1, ~0.40 cm pixel−1, and ~0.60 cm pixel−1 at 5 m, 10 m and 15 m AGL, respectively. The experimental area scanned by the drone was dependent upon the altitude and was ~13.3 m2 min−1, ~33.3 m2 min−1, and ~53.3 m2 min−1, at 5 m, 10 m and 15 m, respectively. Each orthomosaic photo was examined separately by three untrained participants and each of them was asked to examine one photo only, therefore, 72 persons participated in this exercise. To reduce the error due to individuals' differences in the effort spent for litter identification, each participant was required to work on the photo no < 30 min. They were asked to identify individual pieces of beach litter and categorize them according to the colour and nature of the material (plastic, metal, glass, fabric/cloth, processed lumber, others, and unidentified). 2.2. Statistical analysis Statistical analyses were performed using R and SPSS 22. Prior to analysis, data were tested for normality and homogeneity of variance using Shapiro-Wilk test and Levene's test, respectively. To satisfy the above requirements, the data of the number of false positives were log transformed whereas percentage data, i.e., identification accuracy and recovery rate, were arcsine transformed. The accuracy of litter identification (%) was defined as the ratio between no. of correctly identified litter items and total no. of identified items, which included litter items placed on the beach and misidentified items. The recovery rate (%) for litter items based on size and colour was computed as the ratio between no. of litter items recovered and the actual no. of litter items put on the beach. The effects of operating conditions on the accuracy of beach litter identification were analyzed using four-way analysis of variance (ANOVA) followed by a post-hoc Tukey's multiple comparison test (p < 0.05) if the effects of individual factors were significant. If interactions occurred between factors, the effect of one factor was analyzed at individual levels of another factor using one-way ANOVA or Student t-test.
2. Materials and methods 2.1. Experimental design The main goal of this study is to find out the best operating condition for a commercial aerial drone to maximize the accuracy in beach litter identification. Factors investigated included operating altitude of the drone (5 m, 10 m, 15 m above ground level (AGL)), time of day (noon, afternoon between 2 pm and 3 pm), weather condition (sunny, cloudy), and substrate homogeneity of the beach (with high or low density of gravels and pebbles). We hypothesized that these factors would affect the quality of the photos taken, hence the accuracy in beach litter identification. This was a full factorial design with 24 treatments (3 operating altitudes × 2 time of day × 2 weather conditions × 2 beaches with different substrate characteristics). Two experimental beaches with different substrate characteristics were chosen for the study. Nai Chung (22°25′56.7 N 114°15′27.9 E) is a mudflat interspersed with gravels and pebbles whereas Starfish Bay (22°25′53.4″N 114°14′41.6″E) is a typical sandy beach with very low density of gravels and pebbles. Five size categories of beach litter with 10 pieces from each category were collected from another beach. The size range of litter collected followed the descriptors of the EU Marine Strategy Framework Directive (MSFD), i.e., 2.5–5 cm, 5–10 cm, 10–20 cm, 20–30 cm and 30–50 cm. Every piece of litter was photographed (Tables S1–S5 of the Supplementary information) and the characteristics (material, colour, dimensions) recorded. The litter was placed randomly in each of the experimental beaches. Before putting the litter on the beaches, an area of 20 m × 20 m was delineated and all the existing litter inside was removed. The area was subdivided into 400 grids (1 m × 1 m each) and 50 pieces of litter were placed randomly into the grids with one piece of litter in each grid. The drone used in this study was DJI Mavic Pro (specification: https://www.dji.com/mavic) which is a mid-range commercial drone equipped with a 12 megapixels camera. The drone was controlled by a DJI Ground Station Pro application on an iPad Mini that allowed an automatic flight for scanning a specific area. A series of photos were taken by a drone under different operating conditions as mentioned above. The camera was pointed at nadir (90° to the ground) with automatic settings but light sensitivity (ISO) was set at 1000 to ensure that the photos were taken at a shutter speed fast enough (usually 1/
3. Results 3.1. Accuracy of litter identification and recovery rate Examples of photos taken at different height for various sizes of litter items are shown in Fig. 1 and all the litter items used in this study are listed in Tables S1–S5 of Supplementary information. The accuracy of litter identification varied between 39.4% and 75.0% (Table 1) with the highest accuracy being obtained at 5 m and noon on a sunny day at Starfish Bay and the lowest accuracy at 15 m in the late afternoon of a sunny day at Nai Chung. Altitude was the only main factor that significantly affected the accuracy of litter identification (4-way ANOVA, F2,48 = 11.14, p < 0.001) with the accuracy being significantly lower at 15 m (Fig. 2, Tukey's test, p < 0.05). All the other main factors (i.e., weather condition, time of day, and substrate characteristics) were statistically insignificant (4-way ANOVA, p > 0.05). There was an interaction between substrate characteristics and time of day (4-way ANOVA, F2,48 = 4.73, p < 0.05) with a significantly higher accuracy in litter identification (Student's t-test, t = 0.27, p < 0.01) being found at noon at Starfish Bay, the site with homogeneous substrate. Accuracies also varied significantly with size of litter (one-way ANOVA, F4,359 = 122, p < 0.001) and results of Tukey's test are shown in Table 2
Marine Pollution Bulletin 151 (2020) 110823
H.-S. Lo, et al.
Fig. 1. Selected examples of photos taken at different altitudes for different sizes of litter items. Photos were taken in the afternoon of a cloudy day at Nai Chung.
S6. The accuracies for 30–50 cm items were always the highest among all the size classes in all the scenarios. In general, a higher photo resolution (i.e., taken at lower altitudes) would increase the accuracy of identification on smaller items. However, there was no clear indication of which factors (weather condition, time of day and substrate characteristics) would increase the accuracy for smaller items. The recovery rate of litter items of different sizes and colours is shown in Table 2 and results of statistical comparisons are shown in Table 3. Since we did not collect transparent litters in the 2.5–5 cm size group (Table S1 of Supplementary information), the interactive effect between the size and colour of litter was not determined. Smaller litter
items had a lower recovery rate, regardless of the weather condition, substrate characteristics, operating height, and time of day. The problem was more serious for the two smallest size groups, i.e., 2.5–5 cm and 5–10 cm. The effect of colour, however, was not significant in 83% of the cases. Irrespective of litter size, the recovery rate of the litter items from the photos was the lowest at the highest altitude, i.e., 15 m (one-way ANOVA, p < 0.05). The recovery rate was not significantly different between 5 m and 10 m for all the size groups of litter except 2.5–5 cm and 10–20 cm of which the lower the altitude, the higher the recovery rate (Table 3). The effect of weather condition was inconsistent with no significant effect for three size groups (2.5–5 cm,
Table 1 Percentage of accuracya in beach litter identification under different operating conditions for the drone.
Noon p.m.
a
5m 10 m 15 m 5m 10 m 15 m
Nai Chung (with gravels and pebbles)
Starfish Bay (homogeneous, mostly sand)
Sunny
Sunny
59.4 60.1 47.2 67.2 63.4 39.4
± ± ± ± ± ±
Cloudy 15.7% 2.32% 4.48% 4.62% 2.55% 9.12%
60.8 51.0 47.5 52.3 46.6 62.2
± ± ± ± ± ±
9.45% 7.42% 11.0% 18.8% 7.28% 6.26%
75.0 65.1 59.7 56.8 60.0 49.6
± ± ± ± ± ±
Cloudy 10.3% 9.07% 3.88% 8.10% 3.06% 2.00%
62.0 58.9 57.3 62.1 47.4 52.2
Accuracy of litter identification = no. of correctly identified items / (no. of litter items placed on the beach + no. of misidentified items). 3
± ± ± ± ± ±
7.02% 6.83% 7.31% 6.12% 18.0% 8.67%
Marine Pollution Bulletin 151 (2020) 110823
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of time was statistically indistinguishable for the other two heights. 4. Discussion The accuracy of litter identification was dependent on two factors. Firstly, how many litter items were identified correctly, i.e., recovery rate, and secondly, how many non-litter items were misidentified as litter items (i.e., false positive cases). Therefore, maximizing the accuracy of identification would be achieved by increasing the recovery rate and reducing the number of misidentified items. All the operating conditions affected the recovery rate which was enhanced at lower altitudes, in the middle of day, and at a site with homogeneous substrate. The lower the operating height, the higher the resolution of the photos taken, hence the higher the recovery rate. Therefore, the best operating height for the drone was 5 m AGL. To compromise between flight time, coverage and resolution, Martin et al. (2018) suggested an operating height of 10 m AGL. The resolution was also dependent on the size of litter, resulting in the recovery rate being roughly divided into three groups, namely A, B and C. Group A has the lowest recovery rate which included the two smallest size groups of litter, i.e., 2.5–5 cm and 5–10 cm. Group B was 10–20 cm and Group C included the two largest size groups, i.e., 20–30 cm and 30–50 cm. In view of the very low recovery rate for Group A that varied between 3.7% and 81.5%, the drone we used is not recommended for assessing litter items smaller than 10 cm. Although the automated program allows the drone to fly at heights below 5 m AGL which can further enhance the photo resolution, it is not advisable because of flight safety. The recovery rate of the litter items was higher when the photos were taken at noon because longer shadows of various objects, such as rocks, plants or even the drone itself, occurring in the afternoon rendered the littered items less conspicuous and caused confusion. Martin et al. (2018) also preferred photos to be taken around midday to limit object shadow for better automatic processing of images. The heterogeneous nature of a beach increased the level of difficulty in litter assessment using a drone, resulting in a reduction of the recovery rate because litter items would be mistakenly identified as gravels or pebbles (approximately 2–10 cm in size estimated by the images in this study, Fig. S1 of Supplementary information). This problem would be aggravated on shores with higher percentages of objects in this size range. Therefore, the best operating conditions to maximize the photo recovery rate was taking photos at 5 m AGL at noon for litter items not smaller than 10 cm on beaches with a more homogeneous substrate. Misidentification of litter objects on beach also increased at lower operating heights. At first it seemed that the two observations were contradictory. A lower recovery rate at higher operating heights was a result of the reduction in photo resolution. When the resolution of the photo was too low, the observer could not decide whether an object was a piece of litter or something else because of insufficient structural details, hence the number of false positive cases was not affected if this was an ordinary object. The recovery rate, however, became lower if this object was in fact a litter item. In contrast, at lower operating heights with sufficiently high resolution, it was easier for an observer to record a false positive, hence increasing the number of misidentified cases, resulting in an overestimation of the number of litter items. Taking photos at noon could reduce the shadowing effect, hence increasing the recovery rate because all the objects on the beach are potentially more conspicuous. However, this also increased the chance of making mistakes, because the observer was too confident in identifying objects when the shadows were small. This problem could be mitigated on sunny days because the contrast between the object and the background is likely to be greater. To improve the accuracy of litter identification through the reduction of false positive cases, the drone should be operated in the afternoon on a sunny day at higher altitudes. The best operating conditions for maximizing the recovery rate and minimizing the number of false positive cases was contradictory regarding the operating height and time of day. Since the accuracy of
Fig. 2. The accuracy (%) of litter identification from photos taken at three altitudes. Different letters indicate significant differences as tested by 4-way ANOVA followed by Tukey's multiple comparison test (p < 0.05).
20–30 cm, 30–50 cm), a higher recovery rate on a cloudy day for 5–10 cm but on a sunny day for 10–20 cm (Table 3). The recovery rate was either higher at noon (5–10 cm, 10–20 cm, 30–50 cm) or independent of the time of day (2.5–5 cm, 20–30 cm). A homogeneous substrate improved the recovery rate of litter items at 5–10 cm, 20–30 cm and 30–50 cm. The recovery rate for litter of different colours reduced at the highest altitude (Table 3). Compared with litter which was either white or with other colours, transparent litter items were more difficult to be identified, resulting in the higher the altitude, the lower the recovery rate (Tukey's test, p < 0.05). The weather condition did not affect the recovery rate except for transparent litter which had a higher recovery rate on a cloudy day (Tukey's test, p < 0.05). The recovery rate was also independent of the time of day except for white litter which had a higher recovery rate at noon (Tukey's test, p < 0.05). A homogeneous substrate increased the recovery rate of coloured and transparent litter but not white litter (Table 3). 3.2. False positive cases in litter identification False positive cases indicated the conditions likely to overestimate beach litter volume. Both the highest and lowest numbers of these cases were obtained in the afternoon of a cloudy day at Starfish Bay, a beach with homogeneous substrate. The highest mean value (41.0 items) was obtained at an altitude of 10 m while the lowest value (0.33 items) was at 15 m (Table 4). The number of false positives did not vary with the substrate characteristics (4-way ANOVA, F1,71 = 0.50, p = 0.483) (Table 5), with a mean value of 10.2 ± 11.0 items at Nai Chung and 13.1 ± 19.0 items at Starfish Bay. Both the time of day and weather condition affected the number of false positives. Photos taken at noon (mean = 11.9 ± 7.74 items) or on a cloudy day (mean = 15.6 ± 20.0 items) resulted in significantly more false positives (Tukey's test, p < 0.05) than those taken in the afternoon (mean = 11.3 ± 20.7 items) or on a sunny day (7.62 ± 7.32 items). Operating height of the drone also affected the number of false positives with photos taken at 15 m (6.58 ± 7.48 items) being significantly lower (Tukey's test, p < 0.05) than those taken at 5 m and 10 m (mean = 13.1 ± 8.70 items and 15.1 ± 23.8 items, respectively). A significant interaction was found between time of day and operating height (4-way ANOVA, F2,71 = 4.96, p < 0.05). The effect of height was significant only in the afternoon (one-way ANOVA, p < 0.001) with less false positive cases evident at 15 m than at 5 m and 10 m. Among the three heights, significantly more false positive cases were found at noon than in the afternoon at 15 m only, the effect 4
Marine Pollution Bulletin 151 (2020) 110823
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Table 2 Recovery ratesa of different sizes and colours of litter items under different operating conditions for the drone. Nai Chung (with interference)
Noon
5m
10 m
15 m
p.m.
5m
10 m
15 m
2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent
Starfish Bay (without interference)
Sunny
Cloudy
Sunny
Cloudy
63.0 ± 12.8% A 37.0 ± 28.0% AB 91.7 ± 7.22% BC 96.7 ± 5.77% C 100 ± 0.00% C 68.1 ± 10.9% a 74.4 ± 11.8% a 63.3 ± 20.8% a 48.2 ± 6.42% A 37.0 ± 17.0% A 70.8 ± 7.22% AB 93.3 ± 5.77% BC 96.7 ± 5.77% C 63.8 ± 10.9% a 69.2 ± 7.69% a 43.3 ± 15.3% a 3.70 ± 6.42% A 14.8 ± 6.42% A 58.3 ± 19.1% B 81.5 ± 6.42% B 100 ± 0.00% C 44.9 ± 2.51% a 48.7 ± 4.44% a 36.7 ± 11.6% a 70.4 ± 6.42% A 33.3 ± 29.4% AB 95.8 ± 7.22% B 93.3 ± 5.77% B 96.7 ± 5.77% B 62.3 ± 2.51% b 56.7 ± 5.77% b 74.4 ± 4.44% a 53.3 ± 30.6% A 20.8 ± 7.22% AB 70.8 ± 7.22% BC 96.7 ± 5.77% C 93.3 ± 5.77% C 64.9 ± 8.04% a 36.7 ± 11.6% b 69.4 ± 4.81% a 3.70 ± 6.42% A 11.1 ± 11.1% A 54.2 ± 14.4% B 56.7 ± 11.6% B 77.8 ± 11.1% B 39.1 ± 4.35% a 40.0 ± 10.0% a 28.2 ± 19.4% a
53.3 ± 11.6% A 53.3 ± 35.1% AB 63.0 ± 6.42% AB 93.3 ± 11.6% AB 96.7 ± 5.77% B 69.3 ± 8.33% a 69.7 ± 10.5% a 66.7 ± 19.4% a 53.3 ± 5.77% A 26.7 ± 11.6% A 48.2 ± 12.8% A 90.0 ± 10.0% B 100 ± 0.00% B 58.7 ± 2.31% a 45.5 ± 9.09% a 64.1 ± 8.88% a 40.0 ± 10.0% A 33.3 ± 20.8% AB 51.9 ± 17.0% AB 76.7 ± 5.77% B 100 ± 0.00% C 56.0 ± 10.6% a 42.4 ± 5.25% a 56.4 ± 16.0% a 90.0 ± 5.77% A 66.7 ± 11.6% A 70.0 ± 17.3% A 90.0 ± 10.0% A 93.3 ± 5.77% A 78.2 ± 12.4% a 72.7 ± 9.09% a 76.9 ± 13.3% a 53.3 ± 20.8% A 56.7 ± 5.77% A 50.0 ± 17.3% A 93.3 ± 11.6% B 96.7 ± 5.77% B 56.4 ± 2.22% a 51.5 ± 13.9% a 56.4 ± 4.44% a 16.7 ± 8.82% A 13.3 ± 5.77% A 33.3 ± 11.6% A 83.3 ± 15.3% B 93.3 ± 5.77% B 44.9 ± 9.68% a 30.3 ± 18.9% a 41.0 ± 11.8% a
66.7 ± 22.2% A 80.0 ± 10.0% A 88.9 ± 0.00% A 100 ± 0.00% B 100 ± 0.00% B 84.0 ± 4.00% a 54.6 ± 9.09% a 72.2 ± 17.3% a 51.9 ± 25.7% A 43.3 ± 5.77% AB 76.7 ± 5.77% BC 90.0 ± 0.00% CD 100 ± 0.00% D 64.0 ± 6.93% a 60.6 ± 18.9% a 61.5 ± 7.69% a 25.9 ± 23.1% A 23.3 ± 5.77% A 74.1 ± 6.42% B 100 ± 0.00% C 100 ± 0.00% C 60.0 ± 10.6% a 57.6 ± 18.9% a 58.3 ± 8.33% a 81.5 ± 17.0% A 46.7 ± 15.3% A 60.0 ± 10.0% AB 96.7 ± 5.77% B 100 ± 0.00% B 66.7 ± 2.31% a 36.4 ± 15.6% a 69.2 ± 20.4% a 48.2 ± 17.0% A 33.3 ± 5.77% AB 56.7 ± 5.77% B 100 ± 0.00% C 100 ± 0.00% C 66.7 ± 8.33% a 39.4 ± 5.25% b 53.9 ± 7.69% ab 25.9 ± 6.42% A 20.0 ± 0.00% A 23.3 ± 5.77% A 96.7 ± 5.77% B 93.3 ± 5.77% B 53.3 ± 4.62% a 30.3 ± 22.9% a 46.2 ± 7.69% a
54.2 ± 7.22% A 85.2 ± 6.42% AB 88.9 ± 11.1% B 93.3 ± 5.77% B 100 ± 0.00% B 68.1 ± 5.02% a 63.3 ± 15.3% a 84.6 ± 7.69% a 54.2 ± 14.4% A 85.2 ± 25.7% AB 85.2 ± 6.42% AB 93.3 ± 11.6% AB 100 ± 0.00% B 76.8 ± 14.0% a 70.0 ± 17.3% a 84.6 ± 7.69% a 20.8 ± 14.4% A 81.5 ± 6.42% B 81.5 ± 12.8% B 93.3 ± 5.77% BC 100 ± 0.00% C 60.9 ± 8.70% a 66.7 ± 15.3% a 64.1 ± 4.44% a 77.8 ± 11.1% A 46.7 ± 15.3% A 60.0 ± 10.0% AB 93.3 ± 5.77% C 100 ± 0.00% C 69.3 ± 6.11% a 42.4 ± 5.25% a 71.8 ± 23.5% a 51.9 ± 23.1% A 50.0 ± 34.6% A 53.3 ± 5.77% A 100 ± 0.00% B 100 ± 0.00% B 72.0 ± 12.0% a 48.5 ± 13.9% a 59.0 ± 8.88% a 8.33 ± 7.22% A 37.0 ± 28.0% A 20.8 ± 7.22% A 93.3 ± 5.77% B 86.7 ± 5.77% B 55.1 ± 5.02% a 26.7 ± 5.77% c 41.7 ± 0.00% b
a
Recovery rate is the ratio between no. of litter items of particular size/colour identified and the total no. of litter items of particular size/colour placed on the beach. Different letters indicate significant differences (Tukey's test, p < 0.05) among different sizes/colours under each operating condition for the drone.
litter identification was significantly higher at a lower operating height, the effect of photo resolution was more important than the problem of misidentification. The effect of time of day on the accuracy of identification only occurred at the site with a homogeneous substrate,
therefore, it should not make much difference whether the photos were taken at noon or in the afternoon for a non-homogeneous substrate. All in all, the assessment of litter using a drone is recommended at lower operating heights between 5 m and 10 m on a sunny day for litter items
Table 3 Comparisons of recovery rate of different sizes and colours of litter items under different operating conditions for the drone. Size/colour of litter items
Operating heighta
Weather
Time of day
Site
2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent
5 5 5 5 5 5 5 5
NS Cloudy > sunny Sunny > cloudy NS NS NS NS Cloudy > sunny
NS Noon Noon NS Noon NS Noon NS
NS Starfish NS Starfish Starfish Starfish NS Starfish
a
m m m m m m m m
> 10 m > 15 m = 10 m > 15 m > 10 m > 15 m = 10 m > 15 m = 10 m > 15 m = 10 m > 15 m = 10 m > 15 m > 10 m > 15 m
> p.m. > p.m. > p.m. > p.m.
Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung
(=) and NS mean no significant difference at p = 0.05; (>) indicates significant difference at p < 0.05 as analyzed by Tukey pairwise multiple comparison test. 5
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battery, or flight time. The actual flight time in this study was ~15 min, which limited the area that could be scanned per flight. This limitation could be improved by using power-efficient drone (e.g., fixed-wing drone for better aerodynamics) rather than a multicopter drone as used in this study, though the fixed-wing drone is much more expensive (Siebert and Teizer, 2014). Wind speed or temperature also affects the power consumption of the drone. Secondly, a longer time, hence more power, is required for a drone to scan a unit area at a lower operating height, e.g., 5 m AGL because the photo was taken in a smaller area at lower altitude. This limits the versatility because it will not be able to monitor beach which exceeds certain area (in our case, the maximum scan area at 5 m AGL is approximately 600 m2). Therefore, it is suggested that subsamples would be needed as adopted in traditional beach surveys (Cheshire et al., 2009; Galgani et al., 2013; Opfer et al., 2012; OSPAR Commission, 2010). Thirdly, overlapping items of litter cannot be differentiated from the images taken from the drone. This reduces the applicability of using drone images to quantify the number of individual items on beaches heavily polluted with litter. Although the 3D mapping technique exists for the drone, it is not possible to monitor litter with lower height (e.g., 1–2 cm for bottle cap) because it requires the drone flying at extremely low altitude. Notwithstanding the above limitations, the drone can be used in routine monitoring to increase the efficiency and help identify marine litter hotspots for prioritizing coastal cleanup activities. As the aerial drone is becoming more popular for commercial, research and recreational purposes, regulations for the safe use of drone are changing rapidly. To ensure flight safety, national and regional legislations should be checked carefully (e.g., altitude restriction, operator certification) before flight operations.
Table 4 Number of false positive items obtained under different operating conditions for the drone. Time of day
Noon p.m.
5m 10 m 15 m 5m 10 m 15 m
Nai Chung (with gravels and pebbles)
Starfish Bay (mostly sand)
Sunny
Sunny
16.7 8.00 6.33 7.67 3.67 2.00
± ± ± ± ± ±
Cloudy 12.7 6.24 3.21 3.21 2.89 3.46
9.33 13.0 14.3 14.7 21.3 5.00
± ± ± ± ± ±
2.08 5.29 9.71 12.2 31.1 7.81
8.00 6.33 5.00 19.0 6.67 2.67
± ± ± ± ± ±
Cloudy 5.00 6.11 2.00 13.1 3.51 2.08
18.0 21.0 17.0 11.7 41.0 0.33
± ± ± ± ± ±
7.00 5.29 9.17 9.07 61.5 0.58
not smaller than 10 cm. The results of the assessment would be more accurate on beaches with a more homogeneous substrate. In the present study the highest percentage of accuracy in litter identification was 75%. Martin et al. (2018) compared the results of litter assessment obtained by drone with those by visual census along the Saudi Arabian Red Sea coastline. The difference between the two survey methods indicated the percentage of accuracy was higher using the drone survey (61.2%). The authors also suggested that by surveying more shores using both methods, a robust empirical factor could be computed and used as a correction factor for the drone survey to improve the accuracy. Since the present study has demonstrated that various operating conditions would affect the accuracy of the litter assessment by between 39% and 75%, a single empirical factor would not fit all the conditions. Rather separate empirical factors should be computed for specific operating conditions to improve the assessment accuracy. Using a drone can improve the efficiency of beach litter monitoring and allow monitoring of beaches which are relatively remote or inaccessible as the aerial drone promises a safe survey of inaccessible or dangerous terrain (Lee and Choi, 2016). The photos taken can be identified by amateurs as part of citizen science programs. A recent successful example was in flood hydrology in which crowdsourced images gathered by citizen using drone were used for discharge estimation or flood mapping (Le Coz et al., 2016). If a higher accuracy is required for litter identification, proper training can be provided to the observers. As technology advances rapidly, the quality of the monitoring work can be improved further as new models of drone with better configurations, especially the quality of camera resolution, will be available. Nevertheless, the proposed method has some inherent limitations. Firstly, although a drone can scan a large area in a relatively short period of time, it is limited by the operational time of the
5. Conclusions As the aerial drone is becoming more and more popular and relatively inexpensive, it is being used or tested for its applicability for various purposes. The present study was a field trial to understand how different operating conditions affect the performance of a mid-range commercial drone for beach litter assessment and provided recommendations on optimizing the performance. To increase accuracy of positive identification of litter and reduce the prevalence of misidentified non-litter items, we recommend that the drone is flown at lower operating heights (5–10 m) on a sunny day for litter items not smaller than 10 cm. Beaches with a more homogeneous substrate will obtain better assessment results. As technology continues to advance, drones with longer battery life and higher resolution cameras and sensors will become more readily available. This advance in technology
Table 5 The effects of homogeneity of substrate, weather, time of day and altitude on the number of false positive cases as analyzed by four-way ANOVA. Source
DF
Adj SS
Adj MS
F-value
p-Value
Homogeneity of substrate Weather Time of day Altitude Homogeneity of substrate × weather Homogeneity of substrate × time of day Homogeneity of substrate × altitude Weather × time of day Weather × altitude Time of day × altitude Homogeneity of substrate × weather × time of day Homogeneity of substrate × weather × altitude Homogeneity of substrate × time of day × altitude Weather × time of day × altitude Homogeneity of substrate × weather × time of day × altitude Error Total
1 1 1 2 1 1 2 1 2 2 1 2 2 2 2 48 71
0.0660 0.5988 1.2250 2.1314 0.0027 0.0463 0.1121 0.2285 0.3428 1.3096 0.4732 0.1626 0.0871 0.1397 0.0945 6.3316 13.3518
0.06600 0.59881 1.22496 1.06569 0.00271 0.04625 0.05604 0.22852 0.17139 0.65479 0.47316 0.08128 0.04357 0.06987 0.04727 0.13191
0.50 4.54 9.29 8.08 0.02 0.35 0.42 1.73 1.30 4.96 3.59 0.62 0.33 0.53 0.36
0.483 0.038⁎ 0.004⁎ 0.001⁎ 0.887 0.557 0.656 0.194 0.282 0.011⁎ 0.064 0.544 0.720 0.592 0.701
⁎
Comparisons are statistically significant at p < 0.05. 6
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will facilitate future implementation of the automated approach to routine beach litter monitoring not only at a local scale, but also across far larger spatial and temporal scales.
Cole, M., Lindeque, P., Halsband, C., Galloway, T.S., 2011. Microplastics as contaminants in the marine environment: a review. Mar. Pollut. Bull. 62, 2588–2597. https://doi. org/10.1016/j.marpolbul.2011.09.025. Deidun, A., Gauci, A., Lagorio, S., Galgani, F., 2018. Optimising beached litter monitoring protocols through aerial imagery. Mar. Pollut. Bull. 131, 212–217. https://doi.org/ 10.1016/j.marpolbul.2018.04.033. 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. Mar. Pollut. Bull. 96, 76–86. https://doi.org/10.1016/j. marpolbul.2015.05.042. Eriksen, M., Lebreton, L.C.M., Carson, H.S., Thiel, M., Moore, C.J., Borerro, J.C., Galgani, F., Ryan, P.G., Reisser, J., 2014. Plastic pollution in the world’s oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS One 9, e111913. https://doi.org/10.1371/journal.pone.0111913. Galgani, F., Hanke, G., Werner, S., Oosterbaan, L., Nilsson, P., Fleet, D., Kinsey, S., Thompson, R., Van Franeker, J., Vlachogianni, T., Scoullous, M., Veiga, J., Palatinus, A., Matiddi, M., Maes, T., Korpinen, S., Budziak, A., Leslie, H., Gago, J., Liebezeit, G., 2013. Guidance on Monitoring of Marine Litter in European Seas; European Commission - MSFD Technical Subgroup on Marine Litter. Institute of Environment and Sustainability, European Commission. Gall, S.C., Thompson, R.C., 2015. The impact of debris on marine life. Mar. Pollut. Bull. 92, 170–179. https://doi.org/10.1016/j.marpolbul.2014.12.041. Huang, J., Sun, Y., Wang, M., Zhang, D., Sada, R., Li, M.C., 2017. Juvenile tree classification based on hyperspectral image acquired from an unmanned aerial vehicle. Int. J. Remote Sens. 38, 2273–2295. https://doi.org/10.1080/01431161.2016.1219076. Le Coz, J., Patalano, A., Collins, D., Guillén, N.F., García, C.M., Smart, G.M., Bind, J., Chiaverini, A., Le Boursicaud, R., Dramais, G., Braud, I., 2016. Crowdsourced data for flood hydrology: feedback from recent citizen science projects in Argentina, France and New Zealand. J. Hydrol. 541, 766–777. https://doi.org/10.1016/j.jhydrol.2016. 07.036. Lee, S., Choi, Y., 2016. Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry. Geosyst. Eng. 19, 197–204. https://doi.org/ 10.1080/12269328.2016.1162115. Lizarazo, I., Angulo, V., Rodríguez, J., 2017. Automatic mapping of land surface elevation changes from UAV-based imagery. Int. J. Remote Sens. 38, 2603–2622. https://doi. org/10.1080/01431161.2016.1278313. Longmore, S.N., Collins, R.P., Pfeifer, S., Fox, S.E., Mulero-Pázmány, M., Bezombes, F., Goodwin, A., De Juan Ovelar, M., Knapen, J.H., Wich, S.A., 2017. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. Int. J. Remote Sens. 38, 2623–2638. https://doi.org/10. 1080/01431161.2017.1280639. 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. Mar. Pollut. Bull. 131, 662–673. https://doi.org/10.1016/j.marpolbul.2018.04.045. Opfer, S., Arthur, C., Lippiatt, S., 2012. NOAA Marine Debris Shorline Survey Field Guide. National Oceanic and Admospheric Administration, USA Department of Commerce. OSPAR Commission, 2010. Guideline for Monitoring Marine Litter on the Beaches in the OSPAR Maritime Area. OSPAR Commission, London, UK. Siebert, S., Teizer, J., 2014. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom. Constr. 41, 1–14. https://doi.org/ 10.1016/j.autcon.2014.01.004.
CRediT author statement Hoi-Shing Lo: Methodology, Investigation, Writing - original draft Leung-Chun Wong: Investigation Shu-Hin Kwok: Investigation Yan-Kin Lee: Investigation Beverly Hoi-Ki Po: Investigation Chun-Yuen Wong: Funding acquisition, Project administration Nora Fung-Yee Tam: Funding acquisition, Resources Siu-Gin Cheung: Writing - review & editing, Project administration, Funding acquisition, Supervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors would like to thank Niko Wing-Sang Wong and Yiu-Sing Tang for providing technical support. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.110823. References Alvear, O., Zema, N.R., Natalizio, E., Calafate, C.T., 2017. Using UAV-based systems to monitor air pollution in areas with poor accessibility. J. Adv. Transp., 8204353. https://doi.org/10.1155/2017/8204353. Cheshire, A.C., Adler, E., Barbière, J., Cohen, Y., Evans, S., Jarayabhand, S., Jeftic, L., Jung, R.T., Kinsey, S., Kusui, E.T., Lavine, I., Manyara, P., Oosterbaan, L., Pereira, M.A., Sheavly, S., Tkalin, A., Varadarajan, S., Wenneker, B., Westphalen, G., 2009. UNEP/IOC guidelines on survey and monitoring of marine litter. In: UNEP Regional Seas Reports and Studies, No. 186. IOC Technical Series No. 83 (xii + 120 pp).
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Field test of beach litter assessment by commercial aerial drone a
a
a
a
b
Hoi-Shing Lo , Leung-Chun Wong , Shu-Hin Kwok , Yan-Kin Lee , Beverly Hoi-Ki Po , ⁎ Chun-Yuen Wonga, Nora Fung-Yee Tama, Siu-Gin Cheunga,c, a b c
T
Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada State Key Laboratory of Marine Pollution, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region
ARTICLE INFO
ABSTRACT
Keywords: Drone Beach litter assessment
The visual survey is the most common method to quantify and characterize beach litter. However, it is very labor intensive and difficult to carry out on beaches which are remote or difficult to access. We suggest an alternative approach for assessing beach litter using an unmanned aerial vehicle (UAV), or aerial drone, with automated image requisition and processing. Litter of different sizes, colours, and materials were placed randomly on two beaches. Images of beaches with different substrates were obtained by the drone at different operating heights and light conditions and litter on the beaches was identified from the photos by untrained personnel. The quantification of beach litter using the drone was three times faster than that by visual census. This study has demonstrated the potential of using the drone as a cost-effective and an efficient sampling method in routine beach litter monitoring programs.
1. Introduction Marine litter includes all types of persistent manufactured or processed solid material discarded in the marine environment, regardless of whether they are dumped purposely or unintentionally (Duhec et al., 2015). Legislative frameworks (e.g., the European Union's Marine Strategy Framework Directive (MSFD)) require regular monitoring of the trend in marine litter deposition on coastlines. Not surprisingly, plastics are the most dominant type of marine litter both at the local and global scale. It is estimated that 5.25 trillion pieces of plastic debris weighing 269,000 tons were dispersed in oceans in both Northern and Southern Hemispheres (Eriksen et al., 2014) and the number will keep increasing globally. In fact, a large amount of marine litter eventually ends up on shores around the world, even in very remote areas (Cole et al., 2011). Increasing volumes of beach litter constitutes a threat to ecological, economic, recreational and aesthetic values. Physical impacts such as entanglement and ingestion of plastic litter by marine life are examples of threat which have raised public concerns in the last decade (Gall and Thompson, 2015). The degradation of the aesthetic value of beaches results in the loss of revenue from tourism and incurs a cost in the coastal cleanup. Routine monitoring of beaches is therefore vital in identifying hotspots of beach litter and providing valuable information for the establishment of an efficient cleanup strategy.
⁎
Guidelines have been set up by both authorities and NGOs to simplify the data collection process and minimize training required for beach litter monitoring, so that volunteers can help with the monitoring work which can become part of citizen science programs. Several protocols have been prepared by NOAA, IOC, OSPAR and the European Commission (Cheshire et al., 2009; Galgani et al., 2013; Opfer et al., 2012; OSPAR Commission, 2010).The most common approach in monitoring programs involves visual survey of beach litter by people walking along transect lines of 100 m long from strandline to the water's edge. The survey normally requires 3–5 personnel to complete in about 3 h. These manual surveys can provide accurate estimates of the litter standing stock but it is labor intensive and time consuming. The classification of beach litter, however, is subjected to the judgement of the participants, hence depends on their skills and experience. In addition, existing guidelines employ different approaches in the survey which make data comparison difficult. For example, the classification of marine litter varies between the guideline of OSPAR which includes medical waste and that of MSFD which does not. Accessibility of the beach is another concern as it is difficult and sometimes dangerous to conduct surveys in inaccessible areas. As the problem of marine litter is getting worse, new monitoring approaches which are more cost-effective and efficient with minimal labor are needed. This study employed an alternative approach in beach litter assessment using a commercial aerial drone. The rising popularity of the
Corresponding author at: Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region. E-mail address: [email protected] (S.-G. Cheung).
https://doi.org/10.1016/j.marpolbul.2019.110823 Received 1 August 2019; Received in revised form 25 November 2019; Accepted 10 December 2019 Available online 29 January 2020 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.
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drone has raised the interest of its applications in various environmental field studies. Recent examples include air pollution monitoring (Alvear et al., 2017); vegetation classification (Huang et al., 2017); evaluation of topographic change (Lizarazo et al., 2017) and identification of animals (Longmore et al., 2017). To the best of our knowledge, the application of drone in beach litter assessment has been reported in two studies only. Deidun et al. (2018) developed a protocol to assess the litter standing stock along coastal stretches in the Maltese Islands, and tried to mainstream the methodology within national and regional monitoring programs for marine litter. On the other hand, Martin et al. (2018) employed a drone with the aid of a beta version of the machine learning tool in litter assessment along the Saudi Arabian Red Sea coastline, and compared the results with the visual census approach. Both studies have demonstrated that the drone is a powerful tool in beach litter monitoring with high efficiency in quantifying beach litter (e.g., 40 times faster than the visual census in the study of Martin et al., 2018). However, miscounting and/or misidentification of litter are major problems of the drone, especially for natural objects, such as rocks and plant materials, which are sometimes misidentified as beach litter. The performance of imaging was also affected by time of day, weather condition, and flight altitude. Therefore, the main goal of this study is to investigate how the operating conditions (i.e., weather condition, time of day, substrate heterogeneity and flight altitude) for the drone affected the accuracy of litter identification and provide recommendations for optimizing the protocol for beach litter assessment.
400 s–1/1000 s, depending on time of the day and weather condition) to avoid blurry images. The photos taken at each height were automatically aligned to generate a high resolution orthomosaic photo by Agisoft Photoscan Professional (version 1.5.0). Photo alignment required an overlap of > 70% of the area of each photo with the successive one which was achieved by varying the speed of the drone at different altitudes (0.8 m s−1 at 5 m, 2 m s−1 at 10 m, 3.5 m s−1 at 15 m AGL). The resolution of the orthomosaic photos was ~0.20 cm pixel−1, ~0.40 cm pixel−1, and ~0.60 cm pixel−1 at 5 m, 10 m and 15 m AGL, respectively. The experimental area scanned by the drone was dependent upon the altitude and was ~13.3 m2 min−1, ~33.3 m2 min−1, and ~53.3 m2 min−1, at 5 m, 10 m and 15 m, respectively. Each orthomosaic photo was examined separately by three untrained participants and each of them was asked to examine one photo only, therefore, 72 persons participated in this exercise. To reduce the error due to individuals' differences in the effort spent for litter identification, each participant was required to work on the photo no < 30 min. They were asked to identify individual pieces of beach litter and categorize them according to the colour and nature of the material (plastic, metal, glass, fabric/cloth, processed lumber, others, and unidentified). 2.2. Statistical analysis Statistical analyses were performed using R and SPSS 22. Prior to analysis, data were tested for normality and homogeneity of variance using Shapiro-Wilk test and Levene's test, respectively. To satisfy the above requirements, the data of the number of false positives were log transformed whereas percentage data, i.e., identification accuracy and recovery rate, were arcsine transformed. The accuracy of litter identification (%) was defined as the ratio between no. of correctly identified litter items and total no. of identified items, which included litter items placed on the beach and misidentified items. The recovery rate (%) for litter items based on size and colour was computed as the ratio between no. of litter items recovered and the actual no. of litter items put on the beach. The effects of operating conditions on the accuracy of beach litter identification were analyzed using four-way analysis of variance (ANOVA) followed by a post-hoc Tukey's multiple comparison test (p < 0.05) if the effects of individual factors were significant. If interactions occurred between factors, the effect of one factor was analyzed at individual levels of another factor using one-way ANOVA or Student t-test.
2. Materials and methods 2.1. Experimental design The main goal of this study is to find out the best operating condition for a commercial aerial drone to maximize the accuracy in beach litter identification. Factors investigated included operating altitude of the drone (5 m, 10 m, 15 m above ground level (AGL)), time of day (noon, afternoon between 2 pm and 3 pm), weather condition (sunny, cloudy), and substrate homogeneity of the beach (with high or low density of gravels and pebbles). We hypothesized that these factors would affect the quality of the photos taken, hence the accuracy in beach litter identification. This was a full factorial design with 24 treatments (3 operating altitudes × 2 time of day × 2 weather conditions × 2 beaches with different substrate characteristics). Two experimental beaches with different substrate characteristics were chosen for the study. Nai Chung (22°25′56.7 N 114°15′27.9 E) is a mudflat interspersed with gravels and pebbles whereas Starfish Bay (22°25′53.4″N 114°14′41.6″E) is a typical sandy beach with very low density of gravels and pebbles. Five size categories of beach litter with 10 pieces from each category were collected from another beach. The size range of litter collected followed the descriptors of the EU Marine Strategy Framework Directive (MSFD), i.e., 2.5–5 cm, 5–10 cm, 10–20 cm, 20–30 cm and 30–50 cm. Every piece of litter was photographed (Tables S1–S5 of the Supplementary information) and the characteristics (material, colour, dimensions) recorded. The litter was placed randomly in each of the experimental beaches. Before putting the litter on the beaches, an area of 20 m × 20 m was delineated and all the existing litter inside was removed. The area was subdivided into 400 grids (1 m × 1 m each) and 50 pieces of litter were placed randomly into the grids with one piece of litter in each grid. The drone used in this study was DJI Mavic Pro (specification: https://www.dji.com/mavic) which is a mid-range commercial drone equipped with a 12 megapixels camera. The drone was controlled by a DJI Ground Station Pro application on an iPad Mini that allowed an automatic flight for scanning a specific area. A series of photos were taken by a drone under different operating conditions as mentioned above. The camera was pointed at nadir (90° to the ground) with automatic settings but light sensitivity (ISO) was set at 1000 to ensure that the photos were taken at a shutter speed fast enough (usually 1/
3. Results 3.1. Accuracy of litter identification and recovery rate Examples of photos taken at different height for various sizes of litter items are shown in Fig. 1 and all the litter items used in this study are listed in Tables S1–S5 of Supplementary information. The accuracy of litter identification varied between 39.4% and 75.0% (Table 1) with the highest accuracy being obtained at 5 m and noon on a sunny day at Starfish Bay and the lowest accuracy at 15 m in the late afternoon of a sunny day at Nai Chung. Altitude was the only main factor that significantly affected the accuracy of litter identification (4-way ANOVA, F2,48 = 11.14, p < 0.001) with the accuracy being significantly lower at 15 m (Fig. 2, Tukey's test, p < 0.05). All the other main factors (i.e., weather condition, time of day, and substrate characteristics) were statistically insignificant (4-way ANOVA, p > 0.05). There was an interaction between substrate characteristics and time of day (4-way ANOVA, F2,48 = 4.73, p < 0.05) with a significantly higher accuracy in litter identification (Student's t-test, t = 0.27, p < 0.01) being found at noon at Starfish Bay, the site with homogeneous substrate. Accuracies also varied significantly with size of litter (one-way ANOVA, F4,359 = 122, p < 0.001) and results of Tukey's test are shown in Table 2
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Fig. 1. Selected examples of photos taken at different altitudes for different sizes of litter items. Photos were taken in the afternoon of a cloudy day at Nai Chung.
S6. The accuracies for 30–50 cm items were always the highest among all the size classes in all the scenarios. In general, a higher photo resolution (i.e., taken at lower altitudes) would increase the accuracy of identification on smaller items. However, there was no clear indication of which factors (weather condition, time of day and substrate characteristics) would increase the accuracy for smaller items. The recovery rate of litter items of different sizes and colours is shown in Table 2 and results of statistical comparisons are shown in Table 3. Since we did not collect transparent litters in the 2.5–5 cm size group (Table S1 of Supplementary information), the interactive effect between the size and colour of litter was not determined. Smaller litter
items had a lower recovery rate, regardless of the weather condition, substrate characteristics, operating height, and time of day. The problem was more serious for the two smallest size groups, i.e., 2.5–5 cm and 5–10 cm. The effect of colour, however, was not significant in 83% of the cases. Irrespective of litter size, the recovery rate of the litter items from the photos was the lowest at the highest altitude, i.e., 15 m (one-way ANOVA, p < 0.05). The recovery rate was not significantly different between 5 m and 10 m for all the size groups of litter except 2.5–5 cm and 10–20 cm of which the lower the altitude, the higher the recovery rate (Table 3). The effect of weather condition was inconsistent with no significant effect for three size groups (2.5–5 cm,
Table 1 Percentage of accuracya in beach litter identification under different operating conditions for the drone.
Noon p.m.
a
5m 10 m 15 m 5m 10 m 15 m
Nai Chung (with gravels and pebbles)
Starfish Bay (homogeneous, mostly sand)
Sunny
Sunny
59.4 60.1 47.2 67.2 63.4 39.4
± ± ± ± ± ±
Cloudy 15.7% 2.32% 4.48% 4.62% 2.55% 9.12%
60.8 51.0 47.5 52.3 46.6 62.2
± ± ± ± ± ±
9.45% 7.42% 11.0% 18.8% 7.28% 6.26%
75.0 65.1 59.7 56.8 60.0 49.6
± ± ± ± ± ±
Cloudy 10.3% 9.07% 3.88% 8.10% 3.06% 2.00%
62.0 58.9 57.3 62.1 47.4 52.2
Accuracy of litter identification = no. of correctly identified items / (no. of litter items placed on the beach + no. of misidentified items). 3
± ± ± ± ± ±
7.02% 6.83% 7.31% 6.12% 18.0% 8.67%
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of time was statistically indistinguishable for the other two heights. 4. Discussion The accuracy of litter identification was dependent on two factors. Firstly, how many litter items were identified correctly, i.e., recovery rate, and secondly, how many non-litter items were misidentified as litter items (i.e., false positive cases). Therefore, maximizing the accuracy of identification would be achieved by increasing the recovery rate and reducing the number of misidentified items. All the operating conditions affected the recovery rate which was enhanced at lower altitudes, in the middle of day, and at a site with homogeneous substrate. The lower the operating height, the higher the resolution of the photos taken, hence the higher the recovery rate. Therefore, the best operating height for the drone was 5 m AGL. To compromise between flight time, coverage and resolution, Martin et al. (2018) suggested an operating height of 10 m AGL. The resolution was also dependent on the size of litter, resulting in the recovery rate being roughly divided into three groups, namely A, B and C. Group A has the lowest recovery rate which included the two smallest size groups of litter, i.e., 2.5–5 cm and 5–10 cm. Group B was 10–20 cm and Group C included the two largest size groups, i.e., 20–30 cm and 30–50 cm. In view of the very low recovery rate for Group A that varied between 3.7% and 81.5%, the drone we used is not recommended for assessing litter items smaller than 10 cm. Although the automated program allows the drone to fly at heights below 5 m AGL which can further enhance the photo resolution, it is not advisable because of flight safety. The recovery rate of the litter items was higher when the photos were taken at noon because longer shadows of various objects, such as rocks, plants or even the drone itself, occurring in the afternoon rendered the littered items less conspicuous and caused confusion. Martin et al. (2018) also preferred photos to be taken around midday to limit object shadow for better automatic processing of images. The heterogeneous nature of a beach increased the level of difficulty in litter assessment using a drone, resulting in a reduction of the recovery rate because litter items would be mistakenly identified as gravels or pebbles (approximately 2–10 cm in size estimated by the images in this study, Fig. S1 of Supplementary information). This problem would be aggravated on shores with higher percentages of objects in this size range. Therefore, the best operating conditions to maximize the photo recovery rate was taking photos at 5 m AGL at noon for litter items not smaller than 10 cm on beaches with a more homogeneous substrate. Misidentification of litter objects on beach also increased at lower operating heights. At first it seemed that the two observations were contradictory. A lower recovery rate at higher operating heights was a result of the reduction in photo resolution. When the resolution of the photo was too low, the observer could not decide whether an object was a piece of litter or something else because of insufficient structural details, hence the number of false positive cases was not affected if this was an ordinary object. The recovery rate, however, became lower if this object was in fact a litter item. In contrast, at lower operating heights with sufficiently high resolution, it was easier for an observer to record a false positive, hence increasing the number of misidentified cases, resulting in an overestimation of the number of litter items. Taking photos at noon could reduce the shadowing effect, hence increasing the recovery rate because all the objects on the beach are potentially more conspicuous. However, this also increased the chance of making mistakes, because the observer was too confident in identifying objects when the shadows were small. This problem could be mitigated on sunny days because the contrast between the object and the background is likely to be greater. To improve the accuracy of litter identification through the reduction of false positive cases, the drone should be operated in the afternoon on a sunny day at higher altitudes. The best operating conditions for maximizing the recovery rate and minimizing the number of false positive cases was contradictory regarding the operating height and time of day. Since the accuracy of
Fig. 2. The accuracy (%) of litter identification from photos taken at three altitudes. Different letters indicate significant differences as tested by 4-way ANOVA followed by Tukey's multiple comparison test (p < 0.05).
20–30 cm, 30–50 cm), a higher recovery rate on a cloudy day for 5–10 cm but on a sunny day for 10–20 cm (Table 3). The recovery rate was either higher at noon (5–10 cm, 10–20 cm, 30–50 cm) or independent of the time of day (2.5–5 cm, 20–30 cm). A homogeneous substrate improved the recovery rate of litter items at 5–10 cm, 20–30 cm and 30–50 cm. The recovery rate for litter of different colours reduced at the highest altitude (Table 3). Compared with litter which was either white or with other colours, transparent litter items were more difficult to be identified, resulting in the higher the altitude, the lower the recovery rate (Tukey's test, p < 0.05). The weather condition did not affect the recovery rate except for transparent litter which had a higher recovery rate on a cloudy day (Tukey's test, p < 0.05). The recovery rate was also independent of the time of day except for white litter which had a higher recovery rate at noon (Tukey's test, p < 0.05). A homogeneous substrate increased the recovery rate of coloured and transparent litter but not white litter (Table 3). 3.2. False positive cases in litter identification False positive cases indicated the conditions likely to overestimate beach litter volume. Both the highest and lowest numbers of these cases were obtained in the afternoon of a cloudy day at Starfish Bay, a beach with homogeneous substrate. The highest mean value (41.0 items) was obtained at an altitude of 10 m while the lowest value (0.33 items) was at 15 m (Table 4). The number of false positives did not vary with the substrate characteristics (4-way ANOVA, F1,71 = 0.50, p = 0.483) (Table 5), with a mean value of 10.2 ± 11.0 items at Nai Chung and 13.1 ± 19.0 items at Starfish Bay. Both the time of day and weather condition affected the number of false positives. Photos taken at noon (mean = 11.9 ± 7.74 items) or on a cloudy day (mean = 15.6 ± 20.0 items) resulted in significantly more false positives (Tukey's test, p < 0.05) than those taken in the afternoon (mean = 11.3 ± 20.7 items) or on a sunny day (7.62 ± 7.32 items). Operating height of the drone also affected the number of false positives with photos taken at 15 m (6.58 ± 7.48 items) being significantly lower (Tukey's test, p < 0.05) than those taken at 5 m and 10 m (mean = 13.1 ± 8.70 items and 15.1 ± 23.8 items, respectively). A significant interaction was found between time of day and operating height (4-way ANOVA, F2,71 = 4.96, p < 0.05). The effect of height was significant only in the afternoon (one-way ANOVA, p < 0.001) with less false positive cases evident at 15 m than at 5 m and 10 m. Among the three heights, significantly more false positive cases were found at noon than in the afternoon at 15 m only, the effect 4
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Table 2 Recovery ratesa of different sizes and colours of litter items under different operating conditions for the drone. Nai Chung (with interference)
Noon
5m
10 m
15 m
p.m.
5m
10 m
15 m
2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent 2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent
Starfish Bay (without interference)
Sunny
Cloudy
Sunny
Cloudy
63.0 ± 12.8% A 37.0 ± 28.0% AB 91.7 ± 7.22% BC 96.7 ± 5.77% C 100 ± 0.00% C 68.1 ± 10.9% a 74.4 ± 11.8% a 63.3 ± 20.8% a 48.2 ± 6.42% A 37.0 ± 17.0% A 70.8 ± 7.22% AB 93.3 ± 5.77% BC 96.7 ± 5.77% C 63.8 ± 10.9% a 69.2 ± 7.69% a 43.3 ± 15.3% a 3.70 ± 6.42% A 14.8 ± 6.42% A 58.3 ± 19.1% B 81.5 ± 6.42% B 100 ± 0.00% C 44.9 ± 2.51% a 48.7 ± 4.44% a 36.7 ± 11.6% a 70.4 ± 6.42% A 33.3 ± 29.4% AB 95.8 ± 7.22% B 93.3 ± 5.77% B 96.7 ± 5.77% B 62.3 ± 2.51% b 56.7 ± 5.77% b 74.4 ± 4.44% a 53.3 ± 30.6% A 20.8 ± 7.22% AB 70.8 ± 7.22% BC 96.7 ± 5.77% C 93.3 ± 5.77% C 64.9 ± 8.04% a 36.7 ± 11.6% b 69.4 ± 4.81% a 3.70 ± 6.42% A 11.1 ± 11.1% A 54.2 ± 14.4% B 56.7 ± 11.6% B 77.8 ± 11.1% B 39.1 ± 4.35% a 40.0 ± 10.0% a 28.2 ± 19.4% a
53.3 ± 11.6% A 53.3 ± 35.1% AB 63.0 ± 6.42% AB 93.3 ± 11.6% AB 96.7 ± 5.77% B 69.3 ± 8.33% a 69.7 ± 10.5% a 66.7 ± 19.4% a 53.3 ± 5.77% A 26.7 ± 11.6% A 48.2 ± 12.8% A 90.0 ± 10.0% B 100 ± 0.00% B 58.7 ± 2.31% a 45.5 ± 9.09% a 64.1 ± 8.88% a 40.0 ± 10.0% A 33.3 ± 20.8% AB 51.9 ± 17.0% AB 76.7 ± 5.77% B 100 ± 0.00% C 56.0 ± 10.6% a 42.4 ± 5.25% a 56.4 ± 16.0% a 90.0 ± 5.77% A 66.7 ± 11.6% A 70.0 ± 17.3% A 90.0 ± 10.0% A 93.3 ± 5.77% A 78.2 ± 12.4% a 72.7 ± 9.09% a 76.9 ± 13.3% a 53.3 ± 20.8% A 56.7 ± 5.77% A 50.0 ± 17.3% A 93.3 ± 11.6% B 96.7 ± 5.77% B 56.4 ± 2.22% a 51.5 ± 13.9% a 56.4 ± 4.44% a 16.7 ± 8.82% A 13.3 ± 5.77% A 33.3 ± 11.6% A 83.3 ± 15.3% B 93.3 ± 5.77% B 44.9 ± 9.68% a 30.3 ± 18.9% a 41.0 ± 11.8% a
66.7 ± 22.2% A 80.0 ± 10.0% A 88.9 ± 0.00% A 100 ± 0.00% B 100 ± 0.00% B 84.0 ± 4.00% a 54.6 ± 9.09% a 72.2 ± 17.3% a 51.9 ± 25.7% A 43.3 ± 5.77% AB 76.7 ± 5.77% BC 90.0 ± 0.00% CD 100 ± 0.00% D 64.0 ± 6.93% a 60.6 ± 18.9% a 61.5 ± 7.69% a 25.9 ± 23.1% A 23.3 ± 5.77% A 74.1 ± 6.42% B 100 ± 0.00% C 100 ± 0.00% C 60.0 ± 10.6% a 57.6 ± 18.9% a 58.3 ± 8.33% a 81.5 ± 17.0% A 46.7 ± 15.3% A 60.0 ± 10.0% AB 96.7 ± 5.77% B 100 ± 0.00% B 66.7 ± 2.31% a 36.4 ± 15.6% a 69.2 ± 20.4% a 48.2 ± 17.0% A 33.3 ± 5.77% AB 56.7 ± 5.77% B 100 ± 0.00% C 100 ± 0.00% C 66.7 ± 8.33% a 39.4 ± 5.25% b 53.9 ± 7.69% ab 25.9 ± 6.42% A 20.0 ± 0.00% A 23.3 ± 5.77% A 96.7 ± 5.77% B 93.3 ± 5.77% B 53.3 ± 4.62% a 30.3 ± 22.9% a 46.2 ± 7.69% a
54.2 ± 7.22% A 85.2 ± 6.42% AB 88.9 ± 11.1% B 93.3 ± 5.77% B 100 ± 0.00% B 68.1 ± 5.02% a 63.3 ± 15.3% a 84.6 ± 7.69% a 54.2 ± 14.4% A 85.2 ± 25.7% AB 85.2 ± 6.42% AB 93.3 ± 11.6% AB 100 ± 0.00% B 76.8 ± 14.0% a 70.0 ± 17.3% a 84.6 ± 7.69% a 20.8 ± 14.4% A 81.5 ± 6.42% B 81.5 ± 12.8% B 93.3 ± 5.77% BC 100 ± 0.00% C 60.9 ± 8.70% a 66.7 ± 15.3% a 64.1 ± 4.44% a 77.8 ± 11.1% A 46.7 ± 15.3% A 60.0 ± 10.0% AB 93.3 ± 5.77% C 100 ± 0.00% C 69.3 ± 6.11% a 42.4 ± 5.25% a 71.8 ± 23.5% a 51.9 ± 23.1% A 50.0 ± 34.6% A 53.3 ± 5.77% A 100 ± 0.00% B 100 ± 0.00% B 72.0 ± 12.0% a 48.5 ± 13.9% a 59.0 ± 8.88% a 8.33 ± 7.22% A 37.0 ± 28.0% A 20.8 ± 7.22% A 93.3 ± 5.77% B 86.7 ± 5.77% B 55.1 ± 5.02% a 26.7 ± 5.77% c 41.7 ± 0.00% b
a
Recovery rate is the ratio between no. of litter items of particular size/colour identified and the total no. of litter items of particular size/colour placed on the beach. Different letters indicate significant differences (Tukey's test, p < 0.05) among different sizes/colours under each operating condition for the drone.
litter identification was significantly higher at a lower operating height, the effect of photo resolution was more important than the problem of misidentification. The effect of time of day on the accuracy of identification only occurred at the site with a homogeneous substrate,
therefore, it should not make much difference whether the photos were taken at noon or in the afternoon for a non-homogeneous substrate. All in all, the assessment of litter using a drone is recommended at lower operating heights between 5 m and 10 m on a sunny day for litter items
Table 3 Comparisons of recovery rate of different sizes and colours of litter items under different operating conditions for the drone. Size/colour of litter items
Operating heighta
Weather
Time of day
Site
2.5–5 cm 5–10 cm 10–20 cm 20–30 cm 30–50 cm Coloured White Transparent
5 5 5 5 5 5 5 5
NS Cloudy > sunny Sunny > cloudy NS NS NS NS Cloudy > sunny
NS Noon Noon NS Noon NS Noon NS
NS Starfish NS Starfish Starfish Starfish NS Starfish
a
m m m m m m m m
> 10 m > 15 m = 10 m > 15 m > 10 m > 15 m = 10 m > 15 m = 10 m > 15 m = 10 m > 15 m = 10 m > 15 m > 10 m > 15 m
> p.m. > p.m. > p.m. > p.m.
Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung Bay > Nai Chung
(=) and NS mean no significant difference at p = 0.05; (>) indicates significant difference at p < 0.05 as analyzed by Tukey pairwise multiple comparison test. 5
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battery, or flight time. The actual flight time in this study was ~15 min, which limited the area that could be scanned per flight. This limitation could be improved by using power-efficient drone (e.g., fixed-wing drone for better aerodynamics) rather than a multicopter drone as used in this study, though the fixed-wing drone is much more expensive (Siebert and Teizer, 2014). Wind speed or temperature also affects the power consumption of the drone. Secondly, a longer time, hence more power, is required for a drone to scan a unit area at a lower operating height, e.g., 5 m AGL because the photo was taken in a smaller area at lower altitude. This limits the versatility because it will not be able to monitor beach which exceeds certain area (in our case, the maximum scan area at 5 m AGL is approximately 600 m2). Therefore, it is suggested that subsamples would be needed as adopted in traditional beach surveys (Cheshire et al., 2009; Galgani et al., 2013; Opfer et al., 2012; OSPAR Commission, 2010). Thirdly, overlapping items of litter cannot be differentiated from the images taken from the drone. This reduces the applicability of using drone images to quantify the number of individual items on beaches heavily polluted with litter. Although the 3D mapping technique exists for the drone, it is not possible to monitor litter with lower height (e.g., 1–2 cm for bottle cap) because it requires the drone flying at extremely low altitude. Notwithstanding the above limitations, the drone can be used in routine monitoring to increase the efficiency and help identify marine litter hotspots for prioritizing coastal cleanup activities. As the aerial drone is becoming more popular for commercial, research and recreational purposes, regulations for the safe use of drone are changing rapidly. To ensure flight safety, national and regional legislations should be checked carefully (e.g., altitude restriction, operator certification) before flight operations.
Table 4 Number of false positive items obtained under different operating conditions for the drone. Time of day
Noon p.m.
5m 10 m 15 m 5m 10 m 15 m
Nai Chung (with gravels and pebbles)
Starfish Bay (mostly sand)
Sunny
Sunny
16.7 8.00 6.33 7.67 3.67 2.00
± ± ± ± ± ±
Cloudy 12.7 6.24 3.21 3.21 2.89 3.46
9.33 13.0 14.3 14.7 21.3 5.00
± ± ± ± ± ±
2.08 5.29 9.71 12.2 31.1 7.81
8.00 6.33 5.00 19.0 6.67 2.67
± ± ± ± ± ±
Cloudy 5.00 6.11 2.00 13.1 3.51 2.08
18.0 21.0 17.0 11.7 41.0 0.33
± ± ± ± ± ±
7.00 5.29 9.17 9.07 61.5 0.58
not smaller than 10 cm. The results of the assessment would be more accurate on beaches with a more homogeneous substrate. In the present study the highest percentage of accuracy in litter identification was 75%. Martin et al. (2018) compared the results of litter assessment obtained by drone with those by visual census along the Saudi Arabian Red Sea coastline. The difference between the two survey methods indicated the percentage of accuracy was higher using the drone survey (61.2%). The authors also suggested that by surveying more shores using both methods, a robust empirical factor could be computed and used as a correction factor for the drone survey to improve the accuracy. Since the present study has demonstrated that various operating conditions would affect the accuracy of the litter assessment by between 39% and 75%, a single empirical factor would not fit all the conditions. Rather separate empirical factors should be computed for specific operating conditions to improve the assessment accuracy. Using a drone can improve the efficiency of beach litter monitoring and allow monitoring of beaches which are relatively remote or inaccessible as the aerial drone promises a safe survey of inaccessible or dangerous terrain (Lee and Choi, 2016). The photos taken can be identified by amateurs as part of citizen science programs. A recent successful example was in flood hydrology in which crowdsourced images gathered by citizen using drone were used for discharge estimation or flood mapping (Le Coz et al., 2016). If a higher accuracy is required for litter identification, proper training can be provided to the observers. As technology advances rapidly, the quality of the monitoring work can be improved further as new models of drone with better configurations, especially the quality of camera resolution, will be available. Nevertheless, the proposed method has some inherent limitations. Firstly, although a drone can scan a large area in a relatively short period of time, it is limited by the operational time of the
5. Conclusions As the aerial drone is becoming more and more popular and relatively inexpensive, it is being used or tested for its applicability for various purposes. The present study was a field trial to understand how different operating conditions affect the performance of a mid-range commercial drone for beach litter assessment and provided recommendations on optimizing the performance. To increase accuracy of positive identification of litter and reduce the prevalence of misidentified non-litter items, we recommend that the drone is flown at lower operating heights (5–10 m) on a sunny day for litter items not smaller than 10 cm. Beaches with a more homogeneous substrate will obtain better assessment results. As technology continues to advance, drones with longer battery life and higher resolution cameras and sensors will become more readily available. This advance in technology
Table 5 The effects of homogeneity of substrate, weather, time of day and altitude on the number of false positive cases as analyzed by four-way ANOVA. Source
DF
Adj SS
Adj MS
F-value
p-Value
Homogeneity of substrate Weather Time of day Altitude Homogeneity of substrate × weather Homogeneity of substrate × time of day Homogeneity of substrate × altitude Weather × time of day Weather × altitude Time of day × altitude Homogeneity of substrate × weather × time of day Homogeneity of substrate × weather × altitude Homogeneity of substrate × time of day × altitude Weather × time of day × altitude Homogeneity of substrate × weather × time of day × altitude Error Total
1 1 1 2 1 1 2 1 2 2 1 2 2 2 2 48 71
0.0660 0.5988 1.2250 2.1314 0.0027 0.0463 0.1121 0.2285 0.3428 1.3096 0.4732 0.1626 0.0871 0.1397 0.0945 6.3316 13.3518
0.06600 0.59881 1.22496 1.06569 0.00271 0.04625 0.05604 0.22852 0.17139 0.65479 0.47316 0.08128 0.04357 0.06987 0.04727 0.13191
0.50 4.54 9.29 8.08 0.02 0.35 0.42 1.73 1.30 4.96 3.59 0.62 0.33 0.53 0.36
0.483 0.038⁎ 0.004⁎ 0.001⁎ 0.887 0.557 0.656 0.194 0.282 0.011⁎ 0.064 0.544 0.720 0.592 0.701
⁎
Comparisons are statistically significant at p < 0.05. 6
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will facilitate future implementation of the automated approach to routine beach litter monitoring not only at a local scale, but also across far larger spatial and temporal scales.
Cole, M., Lindeque, P., Halsband, C., Galloway, T.S., 2011. Microplastics as contaminants in the marine environment: a review. Mar. Pollut. Bull. 62, 2588–2597. https://doi. org/10.1016/j.marpolbul.2011.09.025. Deidun, A., Gauci, A., Lagorio, S., Galgani, F., 2018. Optimising beached litter monitoring protocols through aerial imagery. Mar. Pollut. Bull. 131, 212–217. https://doi.org/ 10.1016/j.marpolbul.2018.04.033. 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. Mar. Pollut. Bull. 96, 76–86. https://doi.org/10.1016/j. marpolbul.2015.05.042. Eriksen, M., Lebreton, L.C.M., Carson, H.S., Thiel, M., Moore, C.J., Borerro, J.C., Galgani, F., Ryan, P.G., Reisser, J., 2014. Plastic pollution in the world’s oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS One 9, e111913. https://doi.org/10.1371/journal.pone.0111913. Galgani, F., Hanke, G., Werner, S., Oosterbaan, L., Nilsson, P., Fleet, D., Kinsey, S., Thompson, R., Van Franeker, J., Vlachogianni, T., Scoullous, M., Veiga, J., Palatinus, A., Matiddi, M., Maes, T., Korpinen, S., Budziak, A., Leslie, H., Gago, J., Liebezeit, G., 2013. Guidance on Monitoring of Marine Litter in European Seas; European Commission - MSFD Technical Subgroup on Marine Litter. Institute of Environment and Sustainability, European Commission. Gall, S.C., Thompson, R.C., 2015. The impact of debris on marine life. Mar. Pollut. Bull. 92, 170–179. https://doi.org/10.1016/j.marpolbul.2014.12.041. Huang, J., Sun, Y., Wang, M., Zhang, D., Sada, R., Li, M.C., 2017. Juvenile tree classification based on hyperspectral image acquired from an unmanned aerial vehicle. Int. J. Remote Sens. 38, 2273–2295. https://doi.org/10.1080/01431161.2016.1219076. Le Coz, J., Patalano, A., Collins, D., Guillén, N.F., García, C.M., Smart, G.M., Bind, J., Chiaverini, A., Le Boursicaud, R., Dramais, G., Braud, I., 2016. Crowdsourced data for flood hydrology: feedback from recent citizen science projects in Argentina, France and New Zealand. J. Hydrol. 541, 766–777. https://doi.org/10.1016/j.jhydrol.2016. 07.036. Lee, S., Choi, Y., 2016. Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry. Geosyst. Eng. 19, 197–204. https://doi.org/ 10.1080/12269328.2016.1162115. Lizarazo, I., Angulo, V., Rodríguez, J., 2017. Automatic mapping of land surface elevation changes from UAV-based imagery. Int. J. Remote Sens. 38, 2603–2622. https://doi. org/10.1080/01431161.2016.1278313. Longmore, S.N., Collins, R.P., Pfeifer, S., Fox, S.E., Mulero-Pázmány, M., Bezombes, F., Goodwin, A., De Juan Ovelar, M., Knapen, J.H., Wich, S.A., 2017. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. Int. J. Remote Sens. 38, 2623–2638. https://doi.org/10. 1080/01431161.2017.1280639. 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. Mar. Pollut. Bull. 131, 662–673. https://doi.org/10.1016/j.marpolbul.2018.04.045. Opfer, S., Arthur, C., Lippiatt, S., 2012. NOAA Marine Debris Shorline Survey Field Guide. National Oceanic and Admospheric Administration, USA Department of Commerce. OSPAR Commission, 2010. Guideline for Monitoring Marine Litter on the Beaches in the OSPAR Maritime Area. OSPAR Commission, London, UK. Siebert, S., Teizer, J., 2014. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom. Constr. 41, 1–14. https://doi.org/ 10.1016/j.autcon.2014.01.004.
CRediT author statement Hoi-Shing Lo: Methodology, Investigation, Writing - original draft Leung-Chun Wong: Investigation Shu-Hin Kwok: Investigation Yan-Kin Lee: Investigation Beverly Hoi-Ki Po: Investigation Chun-Yuen Wong: Funding acquisition, Project administration Nora Fung-Yee Tam: Funding acquisition, Resources Siu-Gin Cheung: Writing - review & editing, Project administration, Funding acquisition, Supervision. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors would like to thank Niko Wing-Sang Wong and Yiu-Sing Tang for providing technical support. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.110823. References Alvear, O., Zema, N.R., Natalizio, E., Calafate, C.T., 2017. Using UAV-based systems to monitor air pollution in areas with poor accessibility. J. Adv. Transp., 8204353. https://doi.org/10.1155/2017/8204353. Cheshire, A.C., Adler, E., Barbière, J., Cohen, Y., Evans, S., Jarayabhand, S., Jeftic, L., Jung, R.T., Kinsey, S., Kusui, E.T., Lavine, I., Manyara, P., Oosterbaan, L., Pereira, M.A., Sheavly, S., Tkalin, A., Varadarajan, S., Wenneker, B., Westphalen, G., 2009. UNEP/IOC guidelines on survey and monitoring of marine litter. In: UNEP Regional Seas Reports and Studies, No. 186. IOC Technical Series No. 83 (xii + 120 pp).
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