Coastal environments are broadly used by humans to support infrastructure, provide ecosystem services, and supply areas for housing and recreation (de Groot et al., 2012; Mehvar et al., 2018). These uses can be impacted by natural system dynamics such as erosion and sediment transport resulting from storms and changing water levels (Zhang et al., 2004; Phillips and Jones, 2006). Effective coastal management must therefore balance these human uses with coastal changes as well as develop proactive strategies that help build resilience to future changes (Gopalakrishnan et al., 2016; Dastgheib et al., 2018; Masselink and Lazarus, 2019; Molino et al., 2020; Rumson et al., 2020). Developing effective coastal management and coastal resilience strategies rely on high spatio-temporal resolution coastal morphology data (Nichols et al., 2019; Rumson et al., 2020; Huang et al., 2021). These data are needed to assess vulnerability to existing infrastructure (Bove et al., 2020), delineate property ownership (Morton and Speed, 1998), design and evaluate shoreline management actions such as armoring or beach nourishment (Gares et al., 2006), and track general coastal morphology changes (Turner et al., 2016; Theuerkauf et al., 2021). While the need and utility for such datasets is well articulated both in the scientific literature as well as in community and beach master plans, the complexity of gathering, analyzing, and implementing the data can create a barrier for communities who lack the personnel or financial resources to collect these data. Citizen science-based mapping programs that utilize new technologies such as unoccupied aerial systems (UAS) present an opportunity to broaden participation in data-driven management to such communities, however, the accuracy and utility of such datasets must be critically evaluated.

Aerial imagery and digital elevation models (DEMs) are two of the primary datasets coastal managers can utilize to document coastal changes (Huang et al., 2021). Prior to the recent proliferation of UAS, aerial imagery was primarily acquired by occupied aircrafts and satellites. Unfortunately, low-cost or publicly available satellite imagery such as that from the Sentinel-2 satellite may lack the desired resolution (e.g., 10 m GSD; Sentinel-2 Data Access and Products Fact Sheet, 2015) necessary for coastal management, while the high cost of occupied aircraft imagery limits the collection frequency (typically annually). Throughout most of the 20th century, coastal elevation data were collected along profiles using the rod and level technique (Emery, 1961). As more advanced methods such as differential Real-Time Kinematic GPS (RTK-GPS) deployed through backpack or ATV surveys became established in the 1990s, researchers have been able to frequently generate DEMs over large spatial extents (Harley et al., 2011; Lee et al., 2013). RTK-GPS surveys require interpolation between mapped transects however, limiting accuracy over complex topography (e.g., Theuerkauf and Rodriguez, 2012). With the advent of airborne LiDAR, researchers can collect elevation models at high resolutions over large and complex areas (Kidner et al., 2004; Jakubowski et al., 2013), although high cost also limits its use for frequent surveys. Terrestrial LiDAR can be deployed more frequently (O’Dea et al., 2019) but is limited by the spatial extent of the surveys. UAS represent the most recent advance in coastal surveying, as such technology are capable of generating aerial imagery and DEMs through a single survey and are more cost and time-efficient to deploy than previous survey methods (Mancini et al., 2013; Turner et al., 2016; Nikolakopoulos et al., 2019).

Geospatial datasets generated from UAS are derived by implementing photogrammetric structure-from-motion (SFM) algorithms on a series of overlapping offset images collected by UAS. These algorithms match common features in overlapping images to create a network of tie points used to reconstruct objects in 3-dimensional space (Westoby et al., 2012; James et al., 2017). This network of tie points can then be used to generate data products including DEMs and aerial imagery comprised of a mosaic of the original drone images (known as an orthomosaic). These data products are initially created in an arbitrary coordinate system however and require additional information to be accurately georeferenced, which is necessary to compare these data products to existing datasets. Georeferencing of UAS-SFM surveys is commonly conducted using ground control points (GCPs). In most cases, these points are physical markers with a high contrast checkered pattern placed around the scene and surveyed directly prior to the flight using RTK-GPS. The user identifies the location and coordinates of the GCPs in the images which allows the SFM software to optimize the estimates of the camera position, orientation, and internal parameters. This process defines a coordinate system and scale of the tie point network, as well as reducing systematic errors across the site (James et al., 2017). In addition to GCPs, check points (CPs) are surveyed and input into the SFM software in the same manner as GCPs but are not used in the error reduction and georeferencing process. Instead, the CPs are used as independent reference points to estimate the accuracy of the scene reconstruction at a given location.

While GCPs can greatly increase the accuracy of UAS-SFM derived data products, GCP effectiveness largely depends on their quantity and distribution throughout the site. As a result, there is a growing body of literature providing guidance on the ideal placement characteristics of GCPs. For sites less than 40 ha, studies have found that maximum achievable horizontal (XY) and vertical (Z) root mean square errors (RMSE) of 0.02–0.05 m and 0.04–0.10 m were reached with 5–20 and 9–20 GCPs respectively (Agüera-Vega et al., 2017; Martínez-Carricondo et al., 2018; Oniga et al., 2018; Manfreda et al., 2019; Meinen and Robinson, 2020; Yu et al., 2020; Zimmerman et al., 2020; Santana et al., 2021). These recommendations are likely site specific however, as environmental variables such as surface texture and morphology may effect survey accuracy (Westoby et al., 2012; Eltner et al., 2015). Studies evaluating the spatial distribution of GCPs found that at a minimum, GCPs should be placed around the perimeter of the site to minimize error (Martínez-Carricondo et al., 2018). This error can then be further decreased, especially in the vertical direction by evenly distributing GCPS throughout the site (Martínez-Carricondo et al., 2018; Santana et al., 2021). Zimmerman et al. (2020) tested distributions in a coastal environment and found GCPs placed at the corners of the rectangular study area, with adequate cross shore coverage, and at the minimum and maximum elevations minimized errors throughout the site. Conversely, studies have found that GCPs without adequate coverage can lead to inflated errors (Martínez-Carricondo et al., 2018; Santana et al., 2021). For example, concentrating all GCPs in the center or in one corner of the site.

The ease of UAS surveys, as well as the growing body of literature supporting their accuracy, have led to widespread use of UAS for coastal monitoring. They are now frequently implemented for scientific and engineering studies (Casella et al., 2020) and have recently been introduced as a tool for coastal monitoring by citizen scientists (e.g., Pucino et al., 2021; Theuerkauf et al., 2022). These citizen science programs train volunteer UAS pilots to periodically survey coastal areas in their communities. The image sets they collect are sent to institutions with the capacity and expertise to process the imagery into data products that can be used by the communities for coastal management. While clearly beneficial for scientists and communities alike, these networks pose a challenge to traditional GCP collection as citizen scientists are not equipped with RTK-GPS and cannot survey GCPs immediately prior to their flights.

Pucino et al. (2021) addresses this challenge by equipping each citizen scientist pilot with a set of “Smart GCPs” which have a high accuracy differential GPS system built into the survey marker. While effective, these “Smart GCPs” are costly and require additional effort on behalf of the citizen scientists to effectively deploy, retrieve, and maintain. Theuerkauf et al. (2022) addresses this challenge by utilizing existing infrastructure such as corners of park benches, parking stripes, manhole covers, or other static objects as GCPs (Figure 1A). These naturally occurring ground control points or NGCPs, can be surveyed at the onset of a monitoring program and used in lieu of traditional GCPs for the extent of the monitoring period. This method reduces project cost and survey effort compared to “Smart GCPs”, thus allowing for broader participation in the monitoring network for communities across a range of socio-economic settings. The accuracy of sites surveyed with NGCPs must be critically evaluated however, as the opportunities for NGCP placement are often restricted to stable upland structures, failing to adequately cover the dynamic coastal area where monitoring is needed. As a result, opportunities to place these NGCPs may drastically differ from the quantity and/or distribution of GCPs recommended in the literature, therefore presenting a challenge for deriving accurate data products for coastal research and management.

Given the challenges with NGCPs placement, the purposes of this study are to: 1) Evaluate the ability of NGCPs to generate high accuracy data through an accuracy assessment of 20 coastal sites ranging in size and morphology and 2) Contribute to the literature on optimal GCP quantity and distribution by developing best practices for placing NGCPs in coastal environments through a case study in the Great Lakes. Through these investigations, we seek to demonstrate the utility of NGCPs for citizen science surveys and provide guidance for future UAS citizen science monitoring networks utilizing NGCPs.

UAS surveys deployed by citizen scientists represent an opportunity to expand high quality coastal data to small, resource limited communities. The ability to georeference these sites using NGCPs reduces survey cost and effort, however the use of NGCPs may introduce errors reducing the utility of the derived data products for coastal management. This study determined that for almost all sites, using NGCPs produced vertical and horizontal errors comparable or slightly higher than errors obtained using traditional GCPs in coastal environment. The inclusion of 20 sites in this study gives further confidence to these findings, as a range of coastal morphologies and opportunities for NGCP placement were evaluated. At these accuracies, the data products generated from these citizen scientist networks can be used to support data driven management activities in resource limited communities.

The analysis conducted in this study to determine the optimal quantity and distribution of NGCPs to minimize RMSE of surveys also helps to support the expansion of the citizen science-UAS coastal monitoring programs. Through this analysis it was found that the accuracy of sites is only partially dependent on these placement characteristics. The effect of the quantity of NGCPs throughout sites can be modeled with Eqs. 3, 4 although the low R ² squared values of these regressions indicate they should not be solely relied on to determine site error for a given quantity of NGCPs. In fact, the optimum achieved RMSE using all NGCPs available at a site can be achieved with as few as three NGCPs in some cases, highlighting the influence of other factors apart from NGCP quantity on check point RMSE (Figure 9). Furthermore, the relationship between NGCP quantity and RMSE is not consistent and appears to be partially influenced by site morphology. At bluff sites, for example, no additional change in accuracy was achieved with greater than five to six NGCPs, and in the case of one site (Bluff-1), more than three NGCPs counterintuitively resulted in an RMSE increase. In order to minimize error, our findings support previous work (Martínez-Carricondo et al., 2018; Zimmerman et al., 2020; Santana et al., 2021) that indicates NGCPs should be placed around the perimeter of the site in the broadest distribution possible in both the horizontal and vertical directions. In several cases notable deviations from this pattern were observed however, with the broadest distributions of NGCPs resulting in the higher errors compared to cases with more clustered NGCPs. The relationships between NGCP placement and data accuracy established during this study may not be applicable for stretches of coastline larger than our study sites (i.e., >250 m) and additional study is needed to evaluate the potential for scaling up these patterns.

While these findings together provide basic recommendations about optimal NGCP quantity and distribution, it is clear there are other factors impacting site accuracy that cannot be described solely through these NGCP placement characteristics. One possible factor is the varying ability of NGCPs to be identified in the UAS images. Unlike traditional GCPs placed on identical checkered markers, objects used as NGCPs are placed on a variety of structures, leading to differences in image coordinate marker accuracy among the points. For example, at a 0.015 m GSD, misplacing the NGCP by two pixels could result in up to a 0.03 m variation in NGCP image coordinate marker accuracy. It is therefore possible that certain combinations of NGCPs contain points with higher or lower image coordinate marker accuracy, leading to the variations in CP error among different NGCP combinations.

Taking these findings into account, best practices for minimizing errors across sites surveyed by citizen scientists and georeferenced using NGCPs are presented. If both horizontal and vertical accuracy is desired, surveying at least seven NGCPs is recommended and will likely produce an Z RMSE of less than 0.075 m and XY RMSE less than 0.030 m. If only horizontal accuracy is required, the quantity of NGCPs can be reduced to four to obtain an RMSE less than 0.045 m. Surveying additional NGCPs is recommended however, as this may further decrease both horizontal and vertical accuracy as well as provide a buffer if any NGCPs are disturbed.

Any NGCPs used should be as broadly distributed across the site as possible in both the horizontal and vertical directions, but only if they can be sufficiently identified in photos. In other words, broadly placed NGCPs that are difficult to accurately identify may not be as useful as more clustered NGCPs that are easier to identify. Additionally, adding an abundance of NGCPs clustered in one portion of the survey area is discouraged, especially if this area is geographically dissimilar from the rest of the site, for example, at the top of a bluff.

Of the NGCPs used in this study, it is evident that consistent identification is easiest for those NGCPs with high color contrast placed on flat ground, for example, the corner of a white crosswalk on black asphalt. Conversely, placement of NGCPs on elevated geometric edges such as the top corner of a fence post or corner of a stair step should be avoided. In these cases, the varying angle of the object relative to the camera position will make it challenging to consistently identify the point. If placing points on geometric edges is unavoidable, structures with high color contrast between the vertical and horizontal surfaces of the structure are recommended. Without this contrast, when the vertical edge of the structure faces the camera, the geometric structure of the object can be difficult to resolve making NGCP identification inconsistent. If need be, shadows can be utilized as contrast considering time of day of the surveys. For example, if a citizen scientist expects to be conducting surveys in the morning with the Sun in the southeast, the northwest corner of the structure is the optimal choice for an NGCP, allowing a shadow to be cast across the vertical side of the structure, increasing contrast.

Placing an abundance of easy to identify NGCPs widely distributed throughout the survey site is often not possible. Therefore, an accuracy assessment using CPs placed throughout the site at the onset of a project should be conducted by the supporting institution for all citizen science UAS monitoring efforts. Without an accuracy assessment, the undocumented spatial errors of the data products could result in misleading data and less effective, cost-efficient, or even counterproductive coastal management actions. Conducting such an accuracy assessment will also allow a research team to update the check point errors across the site, should some NGCPs be destroyed. In this case, the georeferencing process can be repeated excluding the destroyed NGCPs, allowing researchers to determine the resulting change in accuracy and if a replacement effort for the lost NGCPs is necessary.

Challenges to georeferencing sites using NGCPs stem largely from sites either with bluff morphology, backshore NGCP placement only, or when NGCPs are destroyed. In these cases, vertical error may be higher than desired, potentially limiting their use for coastal management actions requiring accurate vertical elevation data such as volumetric calculations or delineating elevation-based jurisdictional boundaries (e.g., elevation-based ordinary high-water mark). However, in these cases our results indicate that horizontal error largely remains at levels comparable to the primary sites, allowing the data products from these challenging sites to be used for actions requiring only horizontal accuracy. For example, in many coastal states, erosion rates are used to evaluate erosion hazards associated with new and existing infrastructure (EGLE (2021), NYS-DEC). These rates are typically determined by comparing the horizontal position of a coastal erosion line over time, only requiring accuracy in the horizontal dimension. Additionally at bluff sites, these vertical inaccuracies can be reduced with the inclusion of even a single GCP placed at the bottom of the bluff. While this may not be possible using NGCPs, a single RTK enabled “smart GCP” marker could be used similar to those in Pucino et al. (2021) if access to the base of the bluff is possible for citizen scientists. While the inclusion of “smart GCPs” increases project cost and citizen science effort if used exclusively, a single smart GCP used for this purpose may provide a middle ground for maximizing accuracy while minimizing cost and effort.

While in these challenging cases the absolute accuracy of the data products may not be optimal, the fact that a consistent set of NGCPs are being used may facilitate high relative accuracy ideal for multitemporal change detection (Eltner et al., 2015). Future research is needed to fully understand the persistence of errors over time however, which can be effected by varying environmental conditions such as lighting, texture, and morphology change in response to sediment movement or coastal development. (Westoby et al., 2012; Eltner et al., 2015). Additionally, prior literature has found that check point error increases with greater distance to the nearest GCP (Gindraux et al., 2017; Sanz-Ablanedo et al., 2018). Therefore, if a coastal area expands, a previously conducted accuracy assessment may fail to account for increased errors at the water’s edge. Such expansion is common in freshwater environments such as the Great Lakes due to fluctuating water levels, or in marine environments where sediment supply outpaces sea level rise (Quinn, 2002; Hanrahan et al., 2010; van IJzendoorn et al., 2021). In most marine environments however, predicted coastal recession due to global sea level rise (Leatherman et al., 2000; Nicholls and Cazenave, 2010) may decrease the distance from NGCPs to the water’s edge, reducing check point errors at the shoreline.

In this study, the accuracy of UAS-SFM models georeferenced using NGCPs was evaluated at 20 coastal sites. At these sites, the use of NGCPs generally produced errors similar to those observed using traditional GCPs in similar environments. Higher vertical errors are observed however when NGCP placement is limited either by the morphology of the site or when previously surveyed NGCPs are moved or destroyed, reducing the ability of the derived data products to be used in management decisions requiring accurate elevations. In these cases, higher horizontal error was not observed thus preserving their utility for management decisions that only require horizontal accuracy. A detailed investigation on the optimal quantity and distribution of NGCPs needed to minimize error revealed that at most sites seven NGCPs were needed to consistently achieve vertical accuracies of less than 0.075 m Z RMSE and 0.030 m XY RMSE. If only horizontal accuracy is required is required, in most sites four NGCPs can be used to consistently obtain less than 0.045 m XY RMSE. In some sites however, the optimal accuracy can be obtained with far fewer GCPs, while in other sites additional NGCPs may increase error if they are restricted to areas morphologically dissimilar from the rest of the site. Generally, the broadest distribution of NGCPs both in the horizontal and vertical directions produced the lowest errors. Several notable exceptions were observed however were clustered NGCPs produced lower errors than broadly distributed NGCPS. While these findings together provide general guidelines to the optimal NGCP placement, the exceptions to these guidelines indicate that the accuracy of a site cannot be described purely through the quantity and distribution of NGCPs. Conducting an accuracy assessment of sites surveyed using NGCPs is therefore essential, as the lack of such an assessment can obscure errors throughout the site, leading to misinformed and less effective management decisions. The use of NGCPs in citizen-science based UAS coastal monitoring networks reduce survey cost and effort which facilitates the participation of smaller, resource limited communities in the networks. With the high-quality data generated from these studies, these communities are afforded the opportunity to conduct effective and timely data-driven management decisions.