Next Article in Journal
Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance
Previous Article in Journal
Automated Identification and Classification of Plant Species in Heterogeneous Plant Areas Using Unmanned Aerial Vehicle-Collected RGB Images and Transfer Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Drones
Volume 7
Issue 10
10.3390/drones7100600
drones-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Drone-Based Monitoring to Remotely Assess a Beach Nourishment Program on Lord Howe Island

by
Brendan P. Kelaher
1,*,
Tommaso Pappagallo
1,
Sebastian Litchfield
1 and
Thomas E. Fellowes
2
1
National Marine Science Centre, Southern Cross University, P.O. Box 4321, Coffs Harbour, NSW 2450, Australia
2
Geocoastal Research Group, Marine Studies Institute, The University of Sydney, Camperdown, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Drones 2023, 7(10), 600; https://doi.org/10.3390/drones7100600
Submission received: 21 August 2023 / Revised: 21 September 2023 / Accepted: 22 September 2023 / Published: 25 September 2023
Browse Figures
Versions Notes

Abstract

:
Beach nourishment is a soft engineering technique that is used to combat coastal erosion. To assess the efficacy of a beach nourishment program on the northwest coast of Lord Howe Island, remotely coordinated drone-based monitoring was undertaken at Lagoon Beach. Specifically, hypotheses were tested that beach nourishment could increase the dune height and the width of the beach where the sand was translocated but would not have any long-term impacts on other parts of the beach. During the beach nourishment program, sand was translocated from the north end to the south end of Lagoon Beach, where it was deposited over 2800 m2. Lagoon Beach was monitored using a time series of 3D orthomosaics (2019–2021) based on orthorectified drone imagery. The data were then analysed using a robust before-after-control-impact (BACI) experimental design. Initially, a fully automated drone mapping program and permanent ground control points were set up. After this, a local drone pilot facilitated automated drone mapping for the subsequent times of sampling and transferred data to mainland researchers. As well as being more cost-effective, this approach allowed data collection to continue during Island closures due to the COVID-19 pandemic. After sand translocation, the south end of Lagoon Beach had a lower dune with more vegetation and a more expansive beach with a gentler slope than the prior arrangement. Overall, drone monitoring demonstrated the efficacy of the beach nourishment program on Lord Howe Island and highlighted the capacity for drones to deliver cost-effective data in locations that were difficult for researchers to access.

1. Introduction

Coastal erosion threatens built infrastructure adjacent to the shoreline [1,2,3]. Coastal erosion is caused by the natural movement of shorelines being subject to waves, tides, currents, storm surges, and winds [4,5]. Coastal erosion is, however, being exacerbated by human activities, such as the removal of stabilising vegetation [6,7], sand mining [8,9], channel dredging [10,11], hard coastal protection infrastructure [12,13], and climate change [14,15]. In particular, rising climate-driven sea levels and predicted increases in the frequency and intensity of storms are likely to accelerate coastal erosion and deposition in many parts of the world [16,17].
Protecting shorelines from coastal erosion can involve a managed retreat, where the coast is returned to a more natural state by moving built infrastructure away from vulnerable shorelines and restoring protective vegetation [18,19]. In cases where a managed retreat is neither economically nor socially acceptable, a range of hard or soft engineering approaches can be employed [20,21]. While hard coastal protection structures, such as seawalls, breakwaters, or groynes, can protect built infrastructure adjacent to eroding shorelines [22,23], they can also exacerbate coastal erosion in nearby areas [24,25]. Coastal protection infrastructure also changes the aesthetics of natural landscapes and requires constant maintenance [26,27]. As sea levels rise, most coastal protection infrastructure also require significant upgrades to maintain its protective function [28,29]. Soft engineering approaches, such as dunes, vegetation, and reef restorations, could also reduce coastal erosion with less impact on the natural environment [30,31]. In some cases, the most appropriate solution to shoreline erosion is beach nourishment, where sand is translocated from an area of sediment accumulation to the erosion site, where it is used to replenish the sand that has been lost [32,33].
As well as protecting coastal infrastructure, beach nourishment can restore the recreational and aesthetic value of beaches and enhance their ecological and biodiversity values [34,35]. Individual beach nourishment actions are not a permanent solution to coastal erosion, as the replenished sand eventually erodes again due to natural processes [35,36]. The longevity of sand nourishment depends on local coastal dynamics, storm frequency, and sediment supply [34,35]. As a result, ongoing beach nourishment programs require the constant monitoring of erosion sites to determine how often and how much sand needs to be translocated [37,38]. In the past, such monitoring has been conducted using aerial imagery from satellites [39,40] or crewed aircraft [41,42], as well as traditional beach surveys from ground-based transect methods [43] and sediment sampling [41,44]. With rapidly evolving drone technology, surveys of changes in beach morphology are being increasingly undertaken by remotely piloted aircraft (hereafter called drones) [45,46].
The reliability and utility of small affordable, commercial drones have rapidly improved in recent years [47,48], particularly for monitoring natural habitats such as sandy beaches [46,49,50]. Unlike satellite data, drone-based imagery can have higher resolution, less cloud-cover impact, and can be orthorectified to a higher accuracy using ground control points [51,52]. Furthermore, for localised environmental monitoring such as beach nourishment programs, drones are generally more cost-effective [53,54], safer [54,55], and provide higher resolution imagery than crewed aircraft [56,57]. Another advantage of the new generation of commercial multi-rotor drones over crewed aircraft is their ease of use and improved automation [47,58]. While research drones have required highly trained pilots in the past, fully automated mapping software can now enable people with little training to collect high-quality research data safely [54,59]. It is, therefore, currently feasible for researchers to engage local pilots with a basic level of training to collect high-quality environmental data using drones in remote locations without researchers having to be on-site. This approach reduces the need for expert researchers to continuously travel to remote locations for data collection, saving time and money.
Here, drone-based monitoring was used to assess the effectiveness of a beach nourishment program on Lord Howe Island from November 2019 to December 2021. Lord Howe Island management agencies implemented a sand nourishment program to address coastal erosion at the southern end of Lagoon Beach, threatening an essential road. Lord Howe Island is a small, irregularly crescent-shaped volcanic remnant in the Tasman Sea, located approximately 580 km off the east coast of Australia. The remoteness of Lord Howe Island makes fieldwork at this site relatively challenging and expensive. To overcome these challenges, a local pilot based on Lord Howe Island was employed to facilitate the fully automated drone monitoring of the beach nourishment program and to upload data. The local pilot’s period of employment coincided with the closure of Lord Howe Island from outside people during the COVID-19 pandemic. As well as demonstrating the efficacy of the beach nourishment program on Lord Howe Island using a robust before-after-control-impact (BA × CI) experimental design, this approach also highlighted the capacity for automated consumer drones to collect high-quality data in remote locations cost-effectively.

2. Materials and Methods

2.1. Study Location and Sampling Methodology

Drone-based imagery was used to assess the efficacy of a sand nourishment program to mitigate coastal erosion at the south end of Lagoon Beach, which is a 1.34 km stretch of sand situated on the northwest coast of Lord Howe Island (Figure 1). Specifically, the hypotheses tested were that beach nourishment could increase the height of the dune and the width of the beach where the sand was translocated but would not have any long-term impacts on other parts of the beach. During the beach nourishment program, sand was translocated from the north end to the south end of Lagoon Beach, where it was deposited over 2800 m2 (Figure 1). This program also involved the physical removal of a dilapidated seawall from the area that received translocated sand (Figure 2).
Lagoon Beach is subject to relatively low wave energy, as it is protected within a lagoon created by offshore sandbars and reefs [60]. Lagoon Beach was monitored through a time series of 3D orthomosaics from drone imagery, which was orthorectified using precise ground control points. At the start of the sampling program, researchers travelled to Lord Howe Island to set up the automated drone mapping flight paths, complete the first-time of sampling, and establish the ground control points. After this, drone-based surveys were undertaken by a local pilot with appropriate training and all necessary drone and safety equipment. The local pilot, who did not have a research background, completed fully automated drone missions to accurately repeat image collection for the remainder of the monitoring period. From there, the data collected were returned to a research team at the National Marine Science Centre (Coffs Harbour, Australia) for analysis. Using this remote data-collection method, the monitoring of the beach nourishment program continued during travel bans to Lord Howe Island associated with the COVID-19 pandemic.
The drone imagery of Lagoon Beach was collected three times before the commencement of the beach nourishment program (November 2019, May 2020, and December 2020). The drone mapping was then repeated four times (April 2021, August 2021, October 2021, and December 2021) following the completion of sand translocation. The drone-based imagery was collected using a DJI Phantom 4 Pro drone with a 1” CMOS and an 84° field of view that produced 18 MP images. The camera lens was in Nadir (−90° or directly down) with a Polar Pro ND4 filter to reduce glare and sun glint. For each time of sampling, the drone autonomously flew an optimised, pre-programmed flight path at 65 m ASL with a flight speed of 5 m/s. For each time of sampling, the drone collected 695 images with an 80% front and 70% side overlap, respectively.
The automated flight paths were created using the DroneDeploy flight planning application. For each sampling time, DroneDeploy modelling software was used to produce a high-resolution 3D elevation map. The photogrammetry engine was orthorectified with six ground control points (±2 cm elevation accuracy) obtained with a Trimble® R2 GNSS receiver. These ground control points were spaced out along the entire length of Lagoon Beach. On average, the resolution of the orthomosaic maps was 1.77 cm/px, and the resolution of the digital elevation models was 7.08 cm/px.

2.2. Analysis of Data

The measurement functions within DroneDeploy software were used to estimate changes in the volume of sand at the south end of Lagoon Beach over time. The volume tool used a Digital Surface Model with a linear base plane fit. Raw elevation values were exported from DroneDeploy into ArcGIS, where we used the 3D analyst tool to create a series of 20 transects along Lagoon Beach (Figure 1), with eight of these in the area where the sand was translocated (Figure 1). Point cloud data were used to take cross-sections of the beach profiles at each transect. Each profile was taken from the top of the dune to the high-water mark.
A BA × CI experimental design [61] was used to test hypotheses regarding the changes in dune height, beach width, and beach slope from before and after the translocation of sand. Analyses were carried out using the permutational multivariate analysis of variance (PERMANOVA, [62]) and post hoc tests when appropriate. The beach characteristics for each transect were averaged in the before and after periods to ensure the independence of data rather than taking a repeated measures approach. This experimental design was based on two factors, including before versus after (BA, orthogonal and fixed) and sites (Si, north, middle, and south [nourishment site] areas, orthogonal and fixed). To generate the BA × CI interaction required to test for impacts, an apriori contrast (CI) was set up, which compared transects in the control areas against those in the nourishment site. All PERMANOVAs were conducted using PRIMER v. 7 + PERMANOVA add-on (PRIMERe, Pty. Ltd., Plymouth, UK) and Euclidean distances on untransformed data.

3. Results

Based on drone imagery, it was estimated that an extra 8187 m3 of sand was added to the beach nourishment site at the south end of Lagoon Beach (Figure 1). Although this was 4.4% less than the amount of sand estimated to have been trucked in, some sediments could have eroded over the three months that the beach sand was being translocated. At the end of the post-nourishment monitoring period (December 2021), 5802 m3 of sand remained, which meant that 70.9% of the sand translocated was still present after 245 days. The erosion of sand from the nourishment area in the post-translocation period was not a constant process. In fact, between August and December 2021, there was a 6.1% increase in the volume of sand in the beach nourishment area, which was in part due to natural accretion (Figure 3). From before to after the translocation of sand, the drone imagery shows that the additional dune habitat was rapidly colonised by dune plants over the post-translocation period (Figure 2).
There was a significant BA × CI interaction in dune height because there was a significant decrease (20.4%) in dune height in the nourishment area from before compared to after the translocation of sand (Table 1, Figure 3). However, this change was not observed in the north or middle parts of Lagoon Beach (Table 1, Figure 3). While this appears counter-intuitive, it is the result of the unnaturally high dune maintained by a dilapidated seawall that existed prior to the commencement of sand translocation. This seawall likely accelerated erosion due to wave refraction, and there was virtually no beach in front of it prior to nourishment (Figure 2). Once the wall was removed and the area nourished with sand, the beach slope became similar to the rest of the beach (Figure 3).
There was a significant increase in the beach width across all areas from before to after the translocation of sand (Table 1, Figure 3). From before compared to after the nourishment program, the beach width increased by 13%, 20%, and 232% in the north, middle, and southern areas of Lagoon Beach, respectively. The translocation of sand was the primary driver of the increase in beach width for the nourishment site (Figure 3). However, the beach overall was net depositional in the monitoring period, with all areas increasing in width. Notably, there was no noticeable impact from the removal of sand from the north end of Lagoon Beach, which ended up being more expansive than it was in the pre-translocation period but with a similar dune height and beach slope (Figure 3).
The slope of Lagoon Beach did not vary significantly from before to after the translocation of sand (Table 1, Figure 3). There was, however, a strong trend for a reduction in the slope in the middle and nourishment areas of the beach, with the angle decreasing by 19.9% and 22.8% from before to after the translocation of sand, respectively (Figure 3). This change was in part due to sediment accretion during our monitoring period but could also be associated with the addition of sand to the nourishment area (Figure 3).

4. Discussion

Drone-based monitoring demonstrated that the beach nourishment on Lord Howe Island temporarily reduced the erosion problem at the south end of Lagoon Beach, which is a positive outcome that has been achieved by some [33,63,64,65], but not in all sand nourishment programs [66,67,68]. This approach of periodically surveying the beach with drones before and after nourishment using a locally trained pilot and automated missions allowed us to obtain accurate mapping data during COVID-19 closures. Additionally, it reduced the cost of researchers travelling to and from a remote island. After the sand was deposited in the nourishment site and a dilapidated seawall was removed, the area developed a much more natural dune with native vegetation. Furthermore, the slope of the beach changed from a step from the top to the bottom of the seawall to a beach with a gentler gradient. In addition, the beach was significantly broader, retaining 71% of deposited sand after 245 days. Further monitoring is, however, required to determine if there is an ongoing erosion problem and the magnitude and frequency of any future beach nourishment.
The restoration of the morphology of the south end of Lagoon Beach decreased the erosion risk to adjacent coastal infrastructure. The drone-based imagery and mapping measurements showed that the slope and width of the beach became gentler and wider, respectively. Such improvements in beach morphology are usually reported from sand nourishment programs that are considered effective [33,64]. The one anomaly was the significant decrease in dune height from before to after sand nourishment at the erosion site. This counter-intuitive result was caused by removing a delipidated seawall that artificially maintained a higher dune before sand translocation. When the seawall was removed, the decrease in dune height made it similar to the rest of the beach. Furthermore, after nourishment and seawall removal, the more natural-looking beach was aesthetically appealing, adding socio-economic value to a prominent part of Lord Howe Island [69].
Following sand translocation, there was an expansion of native dune vegetation in the erosion site. This was a substantial change, as dune vegetation plays a crucial role in the stability and functioning of sandy beach ecosystems [70]. The natural recovery of native dune vegetation is regarded as a successful outcome for beach restoration [70]. Dune vegetation acts as a natural barrier against wind and wave erosion, stabilising sand and limiting the impact of storm surges and spring tides [71]. Dune vegetation can also trap and accumulate wind-blown sand, contributing to the overall growth and stability of dune ecosystems [72]. Dune vegetation supports biodiversity, including invertebrates, reptiles, birds, and mammals [73]. On Lord Howe Island, this can include threatened species, such as the sooty tern, Onychoprion fuscata [74]. Overall, the restored foredune and vegetation following sand nourishment enhanced the structure and function of the beach’s ecosystem [75] and improved the dune’s coastal protection capacity [76].
Although an estimated ~8200 m3 of sand was removed from the north end of Lagoon Beach and translocated to the eroding south end, no significant changes in dune height or beach slope were detected from before compared to after sand removal at the beach’s northern end. This may, in part, have been due to the relatively broad area over which the sand was collected [77] and to the challenge of accurately estimating small changes in height using drone-based mapping [78]. Another key factor could have been the significant increase in the beach width in the northern and middle parts of the beach from before compared to after sand nourishment. This suggests that Lagoon Beach accreted sand over the monitoring period. This is not uncommon for beaches, which are in a constant flux between eroding and accreting in response to wind, waves, currents, tides, and storm surges [41,79,80]. Notably, no significant storms or cyclones impacted Lagoon Beach during the drone-monitoring period. Such extreme weather phenomena are often responsible for significant beach erosion and coastal infrastructure damage [81,82] and have the capacity to remove all translocated sand and more within a short period [34].
While drone-based surveys and orthomosaics effectively monitored the exposed beach areas at low tide, they did not allow the accurate measurement of the beach below the water line. Although the subtidal areas of Lagoon Beach were visible in the orthomosaics, the underwater heights provided were not an accurate representation of the beaches’ subtidal morphology. Quantifying the subtidal areas of nourished beaches is useful, as they can be an important source or sink for sediments [83]. The benthic topography of subtidal areas off beaches can also influence current and wave dynamics, either modulating or exacerbating coastal erosion [84] or affecting the capacity of habitats to support fish and invertebrates [85]. While extending traditional beach transects from dunes through the intertidal zone and into subtidal areas to accurately characterise beach morphology is relatively straightforward, this is more challenging using drone-based photogrammetry. This issue could be overcome using a drone-mounted water-penetrating LiDAR (e.g., a RIEGL VQ-840-G Compact Airborne Laser Scanner). However, it is not known how effective this might be at capturing the benthic topography of the swash zone, as the green laser needs to penetrate ocean foam.
The drone-based monitoring of Lagoon Beach allowed researchers based on the Australian mainland to continue receiving data during Lord Howe Island’s closures during the COVID-19 pandemic. Here, a trained local drone pilot on-site facilitated the drone flying fully automated mapping missions designed by researchers. This approach was cost-effective, as it only required two people to remain <3 h in the field per time of sampling after the automated flight plan was established. By contrast, traditional beach profiles usually require a team of researchers to work up and down the beach for many hours. The drone-based approach used here also did not require researchers to travel to and from mainland Australia or stay on a remote island for extended periods. While a local pilot was used in the present study, the “drone in a box” concept might make a pilot unnecessary for such monitoring in the near future. A “drone in a box” involves a fully autonomous drone in a weatherproof box and charging station [86]. When required for a mission, the box opens, and the drone takes off, completes the mission, and lands autonomously, or it can be controlled remotely via operators using satellite or mobile internet communications [87]. There are, however, plenty of downsides to not having researchers on-site (e.g., troubleshooting, sample collection, or ground truthing). In the case of monitoring the Lord Howe Island beach nourishment program, the absence of researchers did not allow the collection and analysis of sediment cores or wildlife sampling. However, beach nourishment sampling programs in the future could be optimised by incorporating a combination of autonomous drone sampling and in situ beach sampling by researchers to maximise cost-effectiveness.

5. Conclusions

From our drone-based monitoring, it is concluded that the sand nourishment program on Lord Howe Island successfully created a more natural vegetated dune and a beach profile that had better coastal protection properties than the previous arrangement with a dilapidated sea wall. By engaging a local drone pilot who facilitated autonomous mapping missions, the beach continued to be sampled during the COVID-19 pandemic. This project highlights the potential for drones to sample remote locations cost-effectively, and this capacity could improve with the rapidly developing “drone-in-a-box” technology. While our drone-based approach to sampling beach nourishment on Lord Howe Island had clear advantages, it could not accurately assess beach morphology below the waterline nor assess impacts on sediment characteristics or biodiversity using drones. Future research should consider a combined approach using drone-based monitoring, traditional sampling of beach morphology, and biodiversity assessments to determine the cost and benefits of ongoing beach nourishment, particularly in high-value conservation areas such as Lord Howe Island.

Author Contributions

Conceptualisation, B.P.K.; methodology, B.P.K., T.E.F. and S.L.; formal analysis, S.L. and B.P.K.; investigation, B.P.K., T.E.F. and S.L.; resources, B.P.K.; data curation, S.L.; writing—original draft preparation, B.P.K. and T.P.; writing—review and editing, B.P.K., T.P., S.L. and T.E.F.; visualisation, B.P.K. and S.L.; project administration, B.P.K.; funding acquisition, B.P.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by an SCU DVCR Industry Seed Grant to B.P.K., which involved a collaboration between Southern Cross University, the Lord Howe Island Board and the Lord Howe Island Marine Park.

Data Availability Statement

The data presented are available on request to the corresponding author.

Acknowledgments

Blake Thompson, Caitlin Woods, Anna Giles, Sallyann Gudge and Justin Gilligan helped with fieldwork and data transfer.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cooper, J.A.G.; McKenna, J. Working with natural processes: The challenge for coastal protection strategies. Geogr. J. 2008, 174, 315–331. [Google Scholar] [CrossRef]
  2. Hapke, C.; Reid, D.; Richmond, B.M.; Ruggiero, P.; List, J. National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss along Sandy Shorelines of the California Coast; Open-File Report 2006-1219; U.S. Geological Survey: Reston, VA, USA, 2006. [Google Scholar]
  3. Leo, K.L.; Gillies, C.L.; Fitzsimons, J.A.; Hale, L.Z.; Beck, M.W. Coastal habitat squeeze: A review of adaptation solutions for saltmarsh, mangrove and beach habitats. Ocean Coast. Manag. 2019, 175, 180–190. [Google Scholar] [CrossRef]
  4. Asari, N.; Suratman, M.N.; Mohd Ayob, N.A.; Abdul Hamid, N.H. Mangroves as a natural barrier to environmental risks and coastal protection. In Mangroves: Ecology, Biodiversity and Management; Springer Nature: Singapore, 2021; pp. 305–322. [Google Scholar]
  5. Sudha Rani, N.N.V.; Satyanarayana, A.N.V.; Bhaskaran, P.K. Coastal vulnerability assessment studies over India: A review. Nat. Hazards 2015, 77, 405–428. [Google Scholar] [CrossRef]
  6. Besset, M.; Gratiot, N.; Anthony, E.J.; Bouchette, F.; Goichot, M.; Marchesiello, P. Mangroves and shoreline erosion in the Mekong River delta, Vietnam. Estuar. Coast. Shelf Sci. 2019, 226, 106263. [Google Scholar] [CrossRef]
  7. Martínez, M.L.; Psuty, N.P. Coastal Dunes: Ecology and Conservation; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
  8. Anthony, E.J.; Brunier, G.; Besset, M.; Goichot, M.; Dussouillez, P.; Nguyen, V.L. Linking rapid erosion of the Mekong River delta to human activities. Sci. Rep. 2015, 5, 14745. [Google Scholar] [CrossRef] [PubMed]
  9. Jordan, C.; Tiede, J.; Lojek, O.; Visscher, J.; Apel, H.; Nguyen, H.Q.; Quang, C.N.X.; Schlurmann, T. Sand mining in the Mekong Delta revisited—Current scales of local sediment deficits. Sci. Rep. 2019, 9, 17823. [Google Scholar] [CrossRef]
  10. Kennish, M.J. Coastal salt marsh systems in the U.S.: A review of anthropogenic impacts. J. Coast. Res. 2001, 17, 731–748. [Google Scholar]
  11. Žilinskas, G.; Janušaitė, R.; Jarmalavičius, D.; Pupienis, D. The impact of Klaipėda Port entrance channel dredging on the dynamics of coastal zone, Lithuania. Oceanologia 2020, 62, 489–500. [Google Scholar] [CrossRef]
  12. Leafe, R.; Pethick, J.; Townend, I. Realising the benefits of shoreline management. Geogr. J. 1998, 164, 282–290. [Google Scholar] [CrossRef]
  13. Rangel-Buitrago, N.; Neal, W.J.; de Jonge, V.N. Risk assessment as tool for coastal erosion management. Ocean Coast. Manag. 2020, 186, 105099. [Google Scholar] [CrossRef]
  14. Cai, F.; Su, X.; Liu, J.; Li, B.; Lei, G. Coastal erosion in China under the condition of global climate change and measures for its prevention. Prog. Nat. Sci. 2009, 19, 415–426. [Google Scholar] [CrossRef]
  15. Toimil, A.; Camus, P.; Losada, I.J.; Le Cozannet, G.; Nicholls, R.J.; Idier, D.; Maspataud, A. Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment. Earth-Sci. Rev. 2020, 202, 103110. [Google Scholar] [CrossRef]
  16. Hanley, M.E.; Bouma, T.J.; Mossman, H.L. The gathering storm: Optimising management of coastal ecosystems in the face of a climate-driven threat. Ann. Bot. 2020, 125, 197–212. [Google Scholar] [CrossRef]
  17. Passeri, D.L.; Hagen, S.C.; Medeiros, S.C.; Bilskie, M.V.; Alizad, K.; Wang, D. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review. Earth’s Future 2015, 3, 159–181. [Google Scholar] [CrossRef]
  18. Neal, W.J.; Bush, D.M.; Pilkey, O.H. Managed Retreat. In Encyclopedia of Engineering Geology; Encyclopedia of Earth Sciences Series; 2017; pp. 1–7. Available online: https://link.springer.com/referenceworkentry/10.1007/978-3-319-48657-4_201-2 (accessed on 5 July 2023).
  19. Sutton-Grier, A.E.; Wowk, K.; Bamford, H. Future of our coasts: The potential for natural and hybrid infrastructure to enhance the resilience of our coastal communities, economies and ecosystems. Environ. Sci. Policy 2015, 51, 137–148. [Google Scholar] [CrossRef]
  20. Masselink, G.; Russell, P.; Rennie, A.; Brooks, S.; Spencer, T. Impacts of climate change on coastal geomorphology and coastal erosion relevant to the coastal and marine environment around the U.K. MCCIP Sci. Rev. 2020, 2020, 158–189. [Google Scholar]
  21. Williams, A.; Rangel-Buitrago, N.; Pranzini, E.; Anfuso, G. The management of coastal erosion. Ocean Coast. Manag. 2018, 156, 4–20. [Google Scholar] [CrossRef]
  22. French, P.W. Coastal Defences: Processes, Problems and Solutions; Routledge: London, UK, 2002. [Google Scholar]
  23. Sundar, V.; Sannasiraj, S.; Murali, K.; Singaravelu, V. Impact of coastal structure on shorelines along the southeast and southwest coasts of India. ISH J. Hydraul. Eng. 2022, 28, 1–19. [Google Scholar] [CrossRef]
  24. Arnall, A. Where land meets sea: Islands, erosion and the thing-power of hard coastal protection structures. Environ. Plan. E Nat. Space 2022, 6, 69–86. [Google Scholar] [CrossRef]
  25. Rangel-Buitrago, N.; Williams, A.T.; Anfuso, G. Hard protection structures as a principal coastal erosion management strategy along the Caribbean coast of Colombia. A chronicle of pitfalls. Ocean Coast. Manag. 2018, 156, 58–75. [Google Scholar] [CrossRef]
  26. Mamo, L.T.; Coleman, M.A.; Dwyer, P.G.; Kelaher, B.P. Listing may not achieve conservation: A call for proactive approaches to threatened species management. Aquat. Conserv. Mar. Freshw. Ecosyst. 2020, 30, 611–616. [Google Scholar] [CrossRef]
  27. Mamo, L.T.; Dwyer, P.G.; Coleman, M.A.; Dengate, C.; Kelaher, B.P. Beyond coastal protection: A robust approach to enhance environmental and social outcomes of coastal adaptation. Ocean Coast. Manag. 2022, 217, 106007. [Google Scholar] [CrossRef]
  28. Hallegatte, S.; Ranger, N.; Mestre, O.; Dumas, P.; Corfee-Morlot, J.; Herweijer, C.; Wood, R.M. Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen. Clim. Chang. 2011, 104, 113–137. [Google Scholar] [CrossRef]
  29. Martyr-Koller, R.; Thomas, A.; Schleussner, C.-F.; Nauels, A.; Lissner, T. Loss and damage implications of sea-level rise on Small Island Developing States. Curr. Opin. Environ. Sustain. 2021, 50, 245–259. [Google Scholar] [CrossRef]
  30. Morris, R.L.; Konlechner, T.M.; Ghisalberti, M.; Swearer, S.E. From grey to green: Efficacy of eco-engineering solutions for nature-based coastal defence. Glob. Chang. Biol. 2018, 24, 1827–1842. [Google Scholar] [CrossRef] [PubMed]
  31. Silva, R.; Martínez, M.L.; van Tussenbroek, B.I.; Guzmán-Rodríguez, L.O.; Mendoza, E.; López-Portillo, J. A framework to manage coastal squeeze. Sustainability 2020, 12, 10610. [Google Scholar] [CrossRef]
  32. Davison, A.T.; Nicholls, R.J.; Leatherman, S.P. Beach nourishment as a coastal management tool: An annotated bibliography on developments associated with the artificial nourishment of beaches. J. Coast. Res. 1992, 8, 984–1022. [Google Scholar]
  33. Pinto, C.A.; Silveira, T.M.; Teixeira, S.B. Beach nourishment practice in mainland Portugal (1950–2017): Overview and retrospective. Ocean Coast. Manag. 2020, 192, 105211. [Google Scholar] [CrossRef]
  34. Cooke, B.C.; Jones, A.R.; Goodwin, I.D.; Bishop, M.J. Nourishment practices on Australian sandy beaches: A review. J. Environ. Manag. 2012, 113, 319–327. [Google Scholar] [CrossRef]
  35. de Schipper, M.A.; Ludka, B.C.; Raubenheimer, B.; Luijendijk, A.P.; Schlacher, T.A. Beach nourishment has complex implications for the future of sandy shores. Nat. Rev. Earth Environ. 2020, 2, 70–84. [Google Scholar] [CrossRef]
  36. Cooke, B.C.; Morton, J.K.; Baldry, A.; Bishop, M.J. Backshore nourishment of a beach degraded by off-road vehicles: Ecological impacts and benefits. Sci. Total Environ. 2020, 724, 138115. [Google Scholar] [CrossRef] [PubMed]
  37. Gares, P.A.; Wang, Y.; White, S.A. Using LIDAR to monitor a beach nourishment project at Wrightsville Beach, North Carolina, U.S.A. J. Coast. Res. 2006, 22, 1206–1219. [Google Scholar] [CrossRef]
  38. Reine, K.J. A Literature Review of Beach Nourishment Impacts on Marine Turtles; U.S. Army Corps of Engineers: Washington, DC, USA, 2022. [Google Scholar]
  39. Hagenaars, G.; de Vries, S.; Luijendijk, A.P.; de Boer, W.P.; Reniers, A.J.H.M. On the accuracy of automated shoreline detection derived from satellite imagery: A case study of the sand motor mega-scale nourishment. Coast. Eng. 2018, 133, 113–125. [Google Scholar] [CrossRef]
  40. Vos, K.; Harley, M.D.; Splinter, K.D.; Simmons, J.A.; Turner, I.L. Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coast. Eng. 2019, 150, 160–174. [Google Scholar] [CrossRef]
  41. Pucino, N.; Kennedy, D.M.; Carvalho, R.C.; Allan, B.; Ierodiaconou, D. Citizen science for monitoring seasonal-scale beach erosion and behaviour with aerial drones. Sci. Rep. 2021, 11, 3935. [Google Scholar] [CrossRef]
  42. White, S.A.; Wang, Y. Utilizing DEMs derived from LIDAR data to analyse morphologic change in the North Carolina coastline. Remote Sens. Environ. 2003, 85, 39–47. [Google Scholar] [CrossRef]
  43. Turner, I.L.; Harley, M.D.; Short, A.D.; Simmons, J.A.; Bracs, M.A.; Phillips, M.S.; Splinter, K.D. A multi-decade dataset of monthly beach profile surveys and inshore wave forcing at Narrabeen, Australia. Sci. Data 2016, 3, 160024. [Google Scholar] [CrossRef]
  44. Hamm, L.; Capobianco, M.; Dette, H.; Lechuga, A.; Spanhoff, R.; Stive, M. A summary of European experience with shore nourishment. Coast. Eng. 2002, 47, 237–264. [Google Scholar] [CrossRef]
  45. Kelaher, B.P.; Colefax, A.P.; Tagliafico, A.; Bishop, M.J.; Giles, A.; Butcher, P.A. Assessing variation in assemblages of large marine fauna off ocean beaches using drones. Mar. Freshw. Res. 2019, 71, 68–77. [Google Scholar] [CrossRef]
  46. Provost, E.J.; Coleman, M.A.; Butcher, P.A.; Colefax, A.; Schlacher, T.A.; Bishop, M.J.; Connolly, R.M.; Gilby, B.L.; Henderson, C.J.; Jones, A. Quantifying human use of sandy shores with aerial remote sensing technology: The sky is not the limit. Ocean Coast. Manag. 2021, 211, 105750. [Google Scholar] [CrossRef]
  47. Colefax, A.P.; Butcher, P.A.; Pagendam, D.E.; Kelaher, B.P. Reliability of marine faunal detections in drone-based monitoring. Ocean Coast. Manag. 2019, 174, 108–115. [Google Scholar] [CrossRef]
  48. Giones, F.; Brem, A. From toys to tools: The co-evolution of technological and entrepreneurial developments in the drone industry. Bus. Horiz. 2017, 60, 875–884. [Google Scholar] [CrossRef]
  49. Goncalves, J.A.; Henriques, R. UAV photogrammetry for topographic monitoring of coastal areas. ISPRS J. Photogramm. Remote Sens. 2015, 104, 101–111. [Google Scholar] [CrossRef]
  50. Joyce, K.E.; Fickas, K.C.; Kalamandeen, M. The unique value proposition for using drones to map coastal ecosystems. Camb. Prism. Coast. Futures 2023, 1, e6. [Google Scholar] [CrossRef]
  51. Escobar Villanueva, J.R.; Iglesias Martinez, L.; Perez Montiel, J.I. DEM Generation from fixed-wing UAV imaging and LiDAR-derived ground control points for flood estimations. Sensors 2019, 19, 3205. [Google Scholar] [CrossRef]
  52. Provost, E.J.; Butcher, P.A.; Coleman, M.A.; Bloom, D.; Kelaher, B.P. Aerial drone technology can assist compliance of trap fisheries. Fish. Manag. Ecol. 2020, 27, 381–388. [Google Scholar] [CrossRef]
  53. Hodgson, J.C.; Mott, R.; Baylis, S.M.; Pham, T.T.; Wotherspoon, S.; Kilpatrick, A.D.; Raja Segaran, R.; Reid, I.; Terauds, A.; Koh, L.P.; et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 2018, 9, 1160–1167. [Google Scholar] [CrossRef]
  54. Kelaher, B.P.; Peddemors, V.M.; Hoade, B.; Colefax, A.P.; Butcher, P.A. Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys. J. Unmanned Veh. Syst. 2020, 8, 30–43. [Google Scholar] [CrossRef]
  55. Clothier, R.A.; Greer, D.A.; Greer, D.G.; Mehta, A.M. Risk perception and the public acceptance of drones. Risk Anal. 2015, 35, 1167–1183. [Google Scholar] [CrossRef]
  56. Colefax, A.P.; Butcher, P.A.; Kelaher, B.P.; Browman, H. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft. ICES J. Mar. Sci. 2018, 75, 1–8. [Google Scholar] [CrossRef]
  57. Provost, E.J.; Butcher, P.A.; Colefax, A.P.; Coleman, M.A.; Curley, B.G.; Kelaher, B.P. Using drones to quantify beach users across a range of environmental conditions. J. Coast. Conserv. 2019, 23, 633–642. [Google Scholar] [CrossRef]
  58. Harris, J.M.; Nelson, J.A.; Rieucau, G.; Broussard, W.P. Use of drones in fishery science. Trans. Am. Fish. Soc. 2019, 148, 687–697. [Google Scholar] [CrossRef]
  59. Kent, A.J.; Vujakovic, P. The Routledge Handbook of Mapping and Cartography; Routledge: London, UK, 2017. [Google Scholar]
  60. Dickson, M.; Kennedy, D.; Woodroffe, C. The influence of rock resistance on coastal morphology around Lord Howe Island, southwest Pacific. Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group 2004, 29, 629–643. [Google Scholar] [CrossRef]
  61. Christie, A.P.; Abecasis, D.; Adjeroud, M.; Alonso, J.C.; Amano, T.; Anton, A.; Baldigo, B.P.; Barrientos, R.; Bicknell, J.E.; Buhl, D.A.; et al. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nat. Commun. 2020, 11, 6377. [Google Scholar] [CrossRef]
  62. Anderson, M.J. Permutational Multivariate Analysis of Variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online; 2017; pp. 1–15. Available online: https://onlinelibrary.wiley.com/doi/10.1002/9781118445112.stat07841 (accessed on 5 July 2023).
  63. Armstrong, S.B.; Lazarus, E.D. Masked shoreline erosion at large spatial scales as a collective effect of beach nourishment. Earth’s Future 2019, 7, 74–84. [Google Scholar] [CrossRef]
  64. Blott, S.J.; Pye, K. Morphological and sedimentological changes on an artificially nourished beach, Lincolnshire, U.K. J. Coast. Res. 2004, 20, 214–233. [Google Scholar] [CrossRef]
  65. Dyson, A.; Victory, S.; Connor, T. Sand bypassing the Tweed River entrance: An overview. Proc. Coasts Ports 2001, 2001, 310–315. [Google Scholar]
  66. Ariffin, E.H.; Sedrati, M.; Akhir, M.F.; Daud, N.R.; Yaacob, R.; Husain, M.L. Beach morphodynamics and evolution of monsoon-dominated coasts in Kuala Terengganu, Malaysia: Perspectives for integrated management. Ocean Coast. Manag. 2018, 163, 498–514. [Google Scholar] [CrossRef]
  67. Bitan, M.; Zviely, D. Sand beach nourishment: Experience from the Mediterranean Coast of Israel. J. Mar. Sci. Eng. 2020, 8, 273. [Google Scholar] [CrossRef]
  68. Wanless, H.R.; Maier, K.L. An evaluation of beach renourishment sands adjacent to reefal settings, southeast Florida. Southeast. Geol. 2007, 45, 25–42. [Google Scholar]
  69. McLaughlin, S.; McKenna, J.; Cooper, J. Socio-economic data in coastal vulnerability indices: Constraints and opportunities. J. Coast. Res. 2002, 36, 487–497. [Google Scholar] [CrossRef]
  70. McLachlan, A.; Defeo, O. The Ecology of Sandy Shores; Academic Press: London, UK, 2018. [Google Scholar]
  71. Sigren, J.M.; Figlus, J.; Armitage, A.R. Coastal sand dunes and dune vegetation: Restoration, erosion, and storm protection. Shore Beach 2014, 82, 5–12. [Google Scholar]
  72. Seely, M.; Louw, G. First approximation of the effects of rainfall on the ecology and energetics of a Namib Desert dune ecosystem. J. Arid Environ. 1980, 3, 25–54. [Google Scholar] [CrossRef]
  73. Schlacher, T.A.; de Jager, R.; Nielsen, T. Vegetation and ghost crabs in coastal dunes as indicators of putative stressors from tourism. Ecol. Indic. 2011, 11, 284–294. [Google Scholar] [CrossRef]
  74. Hutton, I.; Parkes, J.P.; Sinclair, A.R.E. Reassembling island ecosystems: The case of Lord Howe Island. Anim. Conserv. 2007, 10, 22–29. [Google Scholar] [CrossRef]
  75. Nordstrom, K.F.; Gamper, U.; Fontolan, G.; Bezzi, A.; Jackson, N.L. Characteristics of coastal dune topography and vegetation in environments recently modified using beach fill and vegetation plantings, Veneto, Italy. Environ. Manag. 2009, 44, 1121–1135. [Google Scholar] [CrossRef]
  76. Nordstrom, K.F.; Jackson, N.L. Foredune restoration in urban settings. In Restoration of Coastal Dunes; Martínez, M.L., Gallego-Fernández, J.B., Hesp, P.A., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 17–31. [Google Scholar]
  77. van der Wal, D. Aeolian Transport of Nourishment Sand in Beach-Dune Environments; University of Amsterdam: Amsterdam, The Netherlands, 1999. [Google Scholar]
  78. Gillan, J.K.; McClaran, M.P.; Swetnam, T.L.; Heilman, P. Estimating forage utilisation with drone-based photogrammetric point clouds. Rangel. Ecol. Manag. 2019, 72, 575–585. [Google Scholar] [CrossRef]
  79. Bishop, M.J.; Peterson, C.H.; Summerson, H.C.; Lenihan, H.S.; Grabowski, J.H. Deposition and long-shore transport of dredge spoils to nourish beaches: Impacts on benthic infauna of an ebb-tidal delta. J. Coast. Res. 2006, 22, 530–546. [Google Scholar] [CrossRef]
  80. Harley, M.D.; Turner, I.; Short, A.; Ranasinghe, R. A reevaluation of coastal embayment rotation: The dominance of cross-shore versus alongshore sediment transport processes, Collaroy-Narrabeen Beach, southeast Australia. J. Geophys. Res. Earth Surf. 2011, 116, F04033. [Google Scholar] [CrossRef]
  81. Gallop, S.L.; Kennedy, D.M.; Loureiro, C.; Naylor, L.A.; Muñoz-Pérez, J.J.; Jackson, D.W.; Fellowes, T.E. Geologically controlled sandy beaches: Their geomorphology, morphodynamics and classification. Sci. Total Environ. 2020, 731, 139123. [Google Scholar] [CrossRef]
  82. Harley, M.D.; Turner, I.L.; Kinsela, M.A.; Middleton, J.H.; Mumford, P.J.; Splinter, K.D.; Phillips, M.S.; Simmons, J.A.; Hanslow, D.J.; Short, A.D. Extreme coastal erosion enhanced by anomalous extratropical storm wave direction. Sci. Rep. 2017, 7, 6033. [Google Scholar] [CrossRef] [PubMed]
  83. Manning, L.M.; Peterson, C.H.; Bishop, M.J. Dominant macrobenthic populations experience sustained impacts from annual disposal of fine sediments on sandy beaches. Mar. Ecol. Prog. Ser. 2014, 508, 1–15. [Google Scholar] [CrossRef]
  84. McGlathery, K.J.; Reidenbach, M.A.; D’Odorico, P.; Fagherazzi, S.; Pace, M.L.; Porter, J.H. Nonlinear dynamics and alternative stable states in shallow coastal systems. Oceanography 2013, 26, 220–231. [Google Scholar] [CrossRef]
  85. Wilson, S.K.; Graham, N.A.J.; Polunin, N.V.C. Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs. Mar. Biol. 2006, 151, 1069–1076. [Google Scholar] [CrossRef]
  86. Grlj, C.G.; Krznar, N.; Pranjić, M. A decade of UAV docking stations: A brief overview of mobile and fixed landing platforms. Drones 2022, 6, 17. [Google Scholar] [CrossRef]
  87. Langåker, H.-A.; Kjerkreit, H.; Syversen, C.L.; Moore, R.J.; Holhjem, Ø.H.; Jensen, I.; Morrison, A.; Transeth, A.A.; Kvien, O.; Berg, G.; et al. An autonomous drone-based system for inspection of electrical substations. Int. J. Adv. Robot. Syst. 2021, 18. [Google Scholar] [CrossRef]
Drones 07 00600 g001
Figure 1. Map showing the location of Lagoon Beach on Lord Howe Island. The map indicates the northern, middle, and south end of Lagoon Beach, which were the areas included in statistical analyses. The map also shows the areas were sand was collected and deposited during the nourishment program.
Figure 1. Map showing the location of Lagoon Beach on Lord Howe Island. The map indicates the northern, middle, and south end of Lagoon Beach, which were the areas included in statistical analyses. The map also shows the areas were sand was collected and deposited during the nourishment program.
Drones 07 00600 g001
Drones 07 00600 g002
Figure 2. Changes in the sand nourishment site on Lagoon Beach (see Figure 1 for specific location) from before to after sand translocation. The drone-based orthomosaic images show the removal of the dilapidated seawall (April 2021) and the growth of new dune vegetation at the last sampling time (December 2021).
Figure 2. Changes in the sand nourishment site on Lagoon Beach (see Figure 1 for specific location) from before to after sand translocation. The drone-based orthomosaic images show the removal of the dilapidated seawall (April 2021) and the growth of new dune vegetation at the last sampling time (December 2021).
Drones 07 00600 g002
Drones 07 00600 g003
Figure 3. Mean (S.E.) beach height, width and slope in the north (dark blue), middle (light blue) and nourishment (orange) areas of Lagoon Beach form before (plain bars) to after (stippled bars) the translocation of sand. The figures show the results of PERMANOVAs and post hoc tests comparing areas before and after the translocation of sand (*, p < 0.05, ns, not significant).
Figure 3. Mean (S.E.) beach height, width and slope in the north (dark blue), middle (light blue) and nourishment (orange) areas of Lagoon Beach form before (plain bars) to after (stippled bars) the translocation of sand. The figures show the results of PERMANOVAs and post hoc tests comparing areas before and after the translocation of sand (*, p < 0.05, ns, not significant).
Drones 07 00600 g003
Table 1. p-values from PERMANOVA analyses comparing beach characteristics from before and after the translocation of sand. Significant effects (p ≤ 0.05) are highlighted in bold.
Table 1. p-values from PERMANOVA analyses comparing beach characteristics from before and after the translocation of sand. Significant effects (p ≤ 0.05) are highlighted in bold.
Dune HeightBeach WidthBeach Slope
dfppp
Before vs. During = B.A.10.14<0.010.09
Sites = SI2<0.01<0.010.44
CI = North/Middle vs. Nourishment1<0.01<0.010.75
BA × SI20.140.170.51
BA × CI1<0.050.160.48
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kelaher, B.P.; Pappagallo, T.; Litchfield, S.; Fellowes, T.E. Drone-Based Monitoring to Remotely Assess a Beach Nourishment Program on Lord Howe Island. Drones 2023, 7, 600. https://doi.org/10.3390/drones7100600

AMA Style

Kelaher BP, Pappagallo T, Litchfield S, Fellowes TE. Drone-Based Monitoring to Remotely Assess a Beach Nourishment Program on Lord Howe Island. Drones. 2023; 7(10):600. https://doi.org/10.3390/drones7100600

Chicago/Turabian Style

Kelaher, Brendan P., Tommaso Pappagallo, Sebastian Litchfield, and Thomas E. Fellowes. 2023. "Drone-Based Monitoring to Remotely Assess a Beach Nourishment Program on Lord Howe Island" Drones 7, no. 10: 600. https://doi.org/10.3390/drones7100600

Article Metrics

Back to TopTop