**1.1 Coastal applications for UAVs**

The affordability and capability of UAV surveys to rapidly produce spatially extensive and high-resolution terrain models has been a boon to geoscientific research over the past decade (e.g., [33]). The majority of which has used Structure from Motion (SfM) photogrammetry. This technique generates automatic tie points (ATPs) between overlapping images and triangulates their position to produce a 3-D point cloud [34, 35]. The utility of UAVs and SfM have been well documented; however, difficulties remain in accurately capturing areas of topographic complexity, water boundaries, and vegetation [33]. The latter two can be particularly problematic in coastal environments due to variability in tidal ranges and/or wave run up on the seaward boundary [36, 37] and in the density of seasonally intermittent or established vegetation across the beach and foredune [38–41]. Other common problems that arise in coastal environments include wet or low texture surfaces that limit image recognition [41], maximum operational wind conditions of � < 25 km/h [37, 42], and regulations that restrict UAV flights in the vicinity of pedestrians [37].

Due to these difficulties, the breadth of UAV research monitoring sandy coastlines has been relatively limited. Research testing the quality of SfM derived point clouds to those produced by more traditional methods such as terrestrial laser scanning (TLS) has found good agreement in areas with limited vegetation height or density [39, 41]. Furthermore, the geo-referencing of UAV point clouds using survey ground control points (GCPs) have been able to obtain centimeter scale error over non-vegetated surfaces [36, 38–40, 43]. However, the elevational error over vegetated surfaces can be an order of magnitude or higher, ranging from the 10–100-cm scale and is largely controlled by vegetation density and canopy height [41]. To date, only a small number of UAV coastal monitoring studies have quantified topographic or volumetric changes of beach-dune morphology in response to storm events [37], storm seasons and human management [38, 44], or annual cycles [40, 45].

55 km/h and gusts of up to 93 km/h were recorded at the Stanhope meteorological station (SMS), located 9 km east of BB. It must be noted that SMS records wind conditions at 3 m above the surface, or 7 m below standard meteorological recordings, and likely underrepresent the peak wind speeds associated with SD. Addition-

*Brackley Beach (A), Prince Edward Island National Park, extends 6 km in an east to west orientation and is backed by highly continuous foredune. Following the storm Dorian, a flattened beach profile (B), significant*

An integrated aerial LiDAR topographic and bathymetric (topobathy) survey was conducted at Prince Edward Island National Park between the 4th and 7th of July 2019. The LiDAR data was obtained through CBCL Limited with permissions from Parks Canada, as part of the Federal Transportation Risk Assessment Initiative. A Leica Chiroptera II dual sensor Topobathy LiDAR, utilizing an infrared (240 kHz) and green laser (35 kHz), seamlessly captured 291 m swaths of both the

ally, there are no offshore buoys on the north coast of PEI to record marine conditions associated with SD; however, significant wave heights of 7–8 m and a storm surge of 1.2 m was forecasted by the Dalcoast-HFX model [48]. Peak storm surge levels, coincident with a 0.8 m high tide recorded on the north coast of PEI at 2 a.m. on September 8th, are estimated to be 2 m above mean sea level (MSL). An initial site assessment of SD's impact on BB observed modification of the beachdune morphometry including a flattened beach profile (**Figure 3B**), scarping of the entire frontal slope (i.e., from the dune toe to dune crest) of the foredune at the eastern section of BB (**Figure 3C**), and a continuous 1–2 m scarp across the majority

of the mid and western sections of BB (**Figure 3D** and **E**).

*foredune erosion (C), a continuous scarp developed alongshore (D and E).*

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

*DOI: http://dx.doi.org/10.5772/intechopen.91459*

**2.1 Pre-SD baseline survey**

**71**

**Figure 3.**

UAV and SfM applications have displayed the potential to increase the efficiency and rapidity of coastal monitoring. In order for this potential to be fully realized, difficulties arising during data collection, processing, and analysis must be addressed when developing systematic and repeatable survey strategies. Recently, UAV coastal research have also used multi-spectral (e.g., [46]) and LiDAR sensors (e.g., [47]); however, this chapter will focus specifically on UAV and SfM systems. The remainder of this chapter will be an introduction to basic quality control measures, handling of survey and environmental uncertainties, and reporting of topographic and volumetric changes associated with a storm event. Furthermore, the persistent challenges of coastal monitoring with UAVs and prospective solutions will be discussed. Ultimately, the aim of this chapter is to present a repeatable methodology to confidently report topographic change and highlight future methodological advances that are still needed to improve upon current coastal monitoring strategies using UAVs.

### **2. Study site**

Brackley Beach (BB; 46°25<sup>0</sup> <sup>50</sup>″ N 63°11<sup>0</sup> <sup>50</sup>″ W) is a part of the Prince Edward Island National Park, located on the north shore of Prince Edward Island (PEI), Canada (**Figure 3A**). BB extends �6 km alongshore in an east to west orientation and is backed by a highly continuous foredune. On September 7th and 8th BB was impacted by the post-tropical storm Dorian (SD). Sustained N–NNE winds of up to *Monitoring Storm Impacts on Sandy Coastlines with UAVs DOI: http://dx.doi.org/10.5772/intechopen.91459*

#### **Figure 3.**

**1.1 Coastal applications for UAVs**

pedestrians [37].

[40, 45].

ing strategies using UAVs.

Brackley Beach (BB; 46°25<sup>0</sup>

**2. Study site**

**70**

The affordability and capability of UAV surveys to rapidly produce spatially extensive and high-resolution terrain models has been a boon to geoscientific research over the past decade (e.g., [33]). The majority of which has used Structure from Motion (SfM) photogrammetry. This technique generates automatic tie points (ATPs) between overlapping images and triangulates their position to produce a 3-D point cloud [34, 35]. The utility of UAVs and SfM have been well documented;

complexity, water boundaries, and vegetation [33]. The latter two can be particularly problematic in coastal environments due to variability in tidal ranges and/or wave run up on the seaward boundary [36, 37] and in the density of seasonally intermittent or established vegetation across the beach and foredune [38–41]. Other common problems that arise in coastal environments include wet or low texture surfaces that limit image recognition [41], maximum operational wind conditions of � < 25 km/h [37, 42], and regulations that restrict UAV flights in the vicinity of

Due to these difficulties, the breadth of UAV research monitoring sandy coastlines has been relatively limited. Research testing the quality of SfM derived point clouds to those produced by more traditional methods such as terrestrial laser scanning (TLS) has found good agreement in areas with limited vegetation height or density [39, 41]. Furthermore, the geo-referencing of UAV point clouds using survey ground control points (GCPs) have been able to obtain centimeter scale error over non-vegetated surfaces [36, 38–40, 43]. However, the elevational error over vegetated surfaces can be an order of magnitude or higher, ranging from the 10–100-cm scale and is largely controlled by vegetation density and canopy height [41]. To date, only a small number of UAV coastal monitoring studies have quantified topographic or volumetric changes of beach-dune morphology in response to storm events [37], storm seasons and human management [38, 44], or annual cycles

UAV and SfM applications have displayed the potential to increase the efficiency and rapidity of coastal monitoring. In order for this potential to be fully realized, difficulties arising during data collection, processing, and analysis must be

addressed when developing systematic and repeatable survey strategies. Recently, UAV coastal research have also used multi-spectral (e.g., [46]) and LiDAR sensors (e.g., [47]); however, this chapter will focus specifically on UAV and SfM systems. The remainder of this chapter will be an introduction to basic quality control measures, handling of survey and environmental uncertainties, and reporting of topographic and volumetric changes associated with a storm event. Furthermore, the persistent challenges of coastal monitoring with UAVs and prospective solutions will be discussed. Ultimately, the aim of this chapter is to present a repeatable methodology to confidently report topographic change and highlight future methodological advances that are still needed to improve upon current coastal monitor-

<sup>50</sup>″ N 63°11<sup>0</sup>

Island National Park, located on the north shore of Prince Edward Island (PEI), Canada (**Figure 3A**). BB extends �6 km alongshore in an east to west orientation and is backed by a highly continuous foredune. On September 7th and 8th BB was impacted by the post-tropical storm Dorian (SD). Sustained N–NNE winds of up to

<sup>50</sup>″ W) is a part of the Prince Edward

however, difficulties remain in accurately capturing areas of topographic

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

*Brackley Beach (A), Prince Edward Island National Park, extends 6 km in an east to west orientation and is backed by highly continuous foredune. Following the storm Dorian, a flattened beach profile (B), significant foredune erosion (C), a continuous scarp developed alongshore (D and E).*

55 km/h and gusts of up to 93 km/h were recorded at the Stanhope meteorological station (SMS), located 9 km east of BB. It must be noted that SMS records wind conditions at 3 m above the surface, or 7 m below standard meteorological recordings, and likely underrepresent the peak wind speeds associated with SD. Additionally, there are no offshore buoys on the north coast of PEI to record marine conditions associated with SD; however, significant wave heights of 7–8 m and a storm surge of 1.2 m was forecasted by the Dalcoast-HFX model [48]. Peak storm surge levels, coincident with a 0.8 m high tide recorded on the north coast of PEI at 2 a.m. on September 8th, are estimated to be 2 m above mean sea level (MSL). An initial site assessment of SD's impact on BB observed modification of the beachdune morphometry including a flattened beach profile (**Figure 3B**), scarping of the entire frontal slope (i.e., from the dune toe to dune crest) of the foredune at the eastern section of BB (**Figure 3C**), and a continuous 1–2 m scarp across the majority of the mid and western sections of BB (**Figure 3D** and **E**).

#### **2.1 Pre-SD baseline survey**

An integrated aerial LiDAR topographic and bathymetric (topobathy) survey was conducted at Prince Edward Island National Park between the 4th and 7th of July 2019. The LiDAR data was obtained through CBCL Limited with permissions from Parks Canada, as part of the Federal Transportation Risk Assessment Initiative. A Leica Chiroptera II dual sensor Topobathy LiDAR, utilizing an infrared (240 kHz) and green laser (35 kHz), seamlessly captured 291 m swaths of both the topographic surface and near shore bathymetry, up to 5 m water depth. The survey was flown at an altitude of 400 m above the ground surface and maintained a sampling density of 2.72 points per meter (p/m) for the bathymetric and 18.6 p/m for the topographic surfaces. Point clouds were then geo-referenced using a total of 141 ground control points with a root mean square error (RMSE) of 0.05 m for the vertical transformation. The point cloud data was then classified for surficial and supra-surficial elements (e.g., vegetation, water surface, etc.). Following classification of the point cloud, only bathymetric and topographic surface classes were maintained to produce a 1 m 1 m 'bare earth' or digital terrain model (DTM) that will serve as a pre-SD baseline for comparisons to post-SD UAV surveys.

removal if elevated error values are recorded. Common causes of error can include GPS survey (e.g., limited number of satellites or line of sight), pedestrian interference during UAV flights, or user identification error during post processing.

BB East) using the commercial SfM software Pix4D. Alternative SFM software including Agisoft Metashape [36, 39, 40, 42–45] and Fledermaus v-7 [38] have been previously used and described. The remainder of this section will focus on the general Pix4D workflow used in this study. Initially, a target number of 10,000 ATPs were generated from each overlapping image. Next, Automatic Aerial Triangulation (AAT), Bundle Block Adjustment (BBA), and camera calibration were optimized for all images. Uncalibrated cameras, a result of errors in the internal (e.g. vibrations) or external (e.g., position and orientation) camera parameters, were deactivated to remove potential topographic deformation during point cloud generation.

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

*DOI: http://dx.doi.org/10.5772/intechopen.91459*

Furthermore, flight lines on the periphery of the survey grid had difficulty finding a sufficient number of ATPs due to areas of homogenous vegetation (**Figure 4B**), selecting prominent features such as treetops from multiple angles (**Figure 4B**), or breaking waves in the nearshore (**Figure 4C**). As a result, severe over- or under-estimation of the surface can occur by misclassifying reference elevation values. In an attempt to reduce survey error, images were clipped to the primary area of interest (i.e., the beach-dune zone). This ensured an optimal number of ATPs present in each image and removed potential edge or 'bowl' effects that can distort the point cloud in areas away from the GCP locations [40]. Next, GCPs that were clearly visible in the flight images were zoomed into and marked at their centroid (**Figure 4D**) to ensure proper pixel selection. GCPs that were blurry (**Figure 4E**), warped, displayed limited contrast, or not entirely visible were not selected because un-proper pixel selection can also introduce error during geographic transformation. Marked GCPs were then used for geographic conversion resulting in a vertical RMSE of 0.028 and 0.021 for BB West and East, respectively.

**3.2 DSM and DTM generation and environmental uncertainty**

2.65 <sup>10</sup><sup>8</sup> points with a density of 2000 points per m<sup>2</sup>

resolution for direct comparisons to the pre-SD LiDAR DTM.

**73**

Following initial processing, a point cloud was generated and consisted of

classified using Pix4D's predefined class groups including ground, road surface, high vegetation, building, and human made objects, in order to improve DTM filtering. Point interpolation was completed using an inverse distance weighting (IDW) approach to generate a universal DSM (i.e., retaining all elevational classes) and filtered DTM (i.e., retaining only the ground elevation class) at a 1 m 1 m

Prior to measuring topographic changes between surfaces, an initial assessment of the quality of the LiDAR DTM and UAV DSM and DTM displayed significant irregularities (**Figure 5A**). For instance, the LiDAR DTM was generated using green laser that can penetrate up to 5 m depth and full wave form infrared laser that can penetrate the vegetation canopy. This provides a fully integrated bathymetric and topographic surface transitioning from the nearshore—to backshore zones (**Figure 5A**). SfM is not able to penetrate the water column and has difficulty measuring ground points in vegetated areas. As a result, the SfM DSM captures noise above the surface that is associated with wave breaking and run up in the nearshore and foreshore zones (**Figure 5A** and **B**). In the backshore, the vegetated crest and lee slope is overestimated on average by 0.5–1m(**Figure 5A**) and represents variability in vegetation density and canopy height (**Figure 5B**). Alternatively, the standard Pix4D DTM filtering method almost completely removed the foredune in areas that recorded significant scarping. **Figure 5A** shows

. The point cloud was

Images of all connected flight grids were collectively processed (i.e., BB West and
