**7. Conclusions and future directions**

Our study has revealed a paradoxical immunomodulatory effect of AQ and PS extracts isolated from Ontario-grown *Panax quinquefolius*. This activity may be relevant to clinical application involving bacterial infection and toxemia. Challenges in development of botanical polysaccharides for clinical application include lack of methodology for structure identification and characterization, isolation and purification, and product quality control, has been recognized [11]. Our research will focus on the application of new methodologies to characterize the polysaccharide structures from ginseng and to elucidate the structurebiological activity relationship.


**Acknowledgements**

We would like to thank Lakshmi Narayanan Gopalan for his assistance in the preparation of figures. The salary support for this work was provided by Indo-Finland research grant (IV4SP-FinSynBio-Synthetic Biology), funded by Department of Biotechnology, Government of India (Grant number BT/IN/ Finland/29/MN/2013).

*Role of Novel Drug Delivery Vehicles in Nanobiomedicine*

tating high-throughput selection of aptamers.

high-affinity Ang2 aptamers with Kd <30 nM.

just one round of positive selection followed by HTS [96].

stages and only individual stages in a pipeline are partially automated, independent of each other [86]. The advantage of unit-automation is that it keeps a highly open architecture of the pipeline allowing individual modules to be easily modified or

Unlike the discovery of small molecule-based drugs in industry, where automation is a norm, development of novel aptamers has happened in individual labs in an academic research setting. For truly industrializing the aptamer discovery process, it is imperative to develop high-throughput strategies for aptamer development at different levels, *viz.*, aptamer selection process, sequencing, and characterization of aptamers involving affinity and specificity studies toward its target. Here we discuss about the high-throughput technologies which have been developed facili-

The first attempt to automate aptamer selection process was performed by the group of Ellington [82] followed by some other improvements [88–92]. It brought down the selection time from weeks or months to days. Automation of the SELEX process increased the throughput of selections, but shifted the bottleneck within a

In 2011 the group of T.M. Soh developed a Quantitative Parallel Aptamer Selection System (QPASS), which integrates microfluidic aptamer selection (M-SELEX) and next-generation sequencing (NGS) with in situ synthesized aptamer arrays, enabling simultaneous measurement of affinity and specificity of several 1000 candidate aptamers in parallel. Major advantage of this high-throughput platform is that it automated the characterization, i.e., finding the affinity and specificity of aptamers which is a major bottleneck in aptamer selection, since traditionally the affinity and specificity of aptamers is measured individually in a serial manner [93]. This group used angiopoietin-2 (Ang2) as a target and performed four rounds of M-SELEX followed by next-generation sequencing to obtain enriched aptamer sequences. Next, using aptamer arrays they simultaneously measured dissociation constant (*Kd*) of ∼1000 aptamer candidates in parallel, identifying six

Development of NGS platform in the last decade has revolutionized the aptamer

Since the inception of aptamer technology, this field has significantly evolved

with its foray into a wide variety of applications, including targeted delivery. Aptamers caught the attention of researchers due to their unique advantages for use in therapeutics in nanomedicine due to their small size that gives higher penetration in tissues as compared to antibodies, ease of synthesis, high specificity and affinity that is comparable with antibodies and ease of chemical modifications that allowed

selection process. The Schroeder group in 2010 made the initial attempt in this direction as they combined genomic SELEX with high-throughput sequencing for the identification of genomic aptamers [94]. The same year, Soh group (2010) integrated microfluidic SELEX with high-throughput sequencing to obtain novel aptamers against platelet-derived growth factor BB (PDGF-BB) protein in just three rounds of selection, which traditionally takes 12–20 rounds [95]. Advantage of HTS is that it can track the copy number and enrichment fold of more than 10 million individual aptamer sequences through multiple rounds of selection. This enables the identification of high-affinity aptamers without the need of fully converging the aptamer pool to a small number of sequences, which is required for cloningbased selection method. Similarly, Hoon et al. obtained high-affinity aptamers after

exchanged; hence, the pipeline can be easily extended or modified [87].

selection pipeline toward the identification and evaluation side.

**14**

**10. Conclusion**


**Progress in the Valorization of Fruit and Vegetable Wastes: Active Packaging, Biocomposites, By-Products, and Innovative Technologies Used for Bioactive Compound Extraction**

**Mohd Salahuddin Mohd Basri 1,2,3,\* , Nor Nadiah Abdul Karim Shah 1,2 , Alifdalino Sulaiman 1,2 , Intan Syafinaz Mohamed Amin Tawakkal 1,2, Mohd Zuhair Mohd Nor 1,2 , Siti Hajar Ariffin 1,2 , Nur Hamizah Abdul Ghani 1 and Faiqa Shazeaa Mohd Salleh 1**

**Abstract:** According to the Food Wastage Footprint and Climate Change Report, about 15% of all fruits and 25% of all vegetables are wasted at the base of the food production chain. The significant losses and wastes in the fresh and processing industries is becoming a serious environmental issue, mainly due to the microbial degradation impacts. There has been a recent surge in research and innovation related to food, packaging, and pharmaceutical applications to address these problems. The underutilized wastes (seed, skin, rind, and pomace) potentially present good sources of valuable bioactive compounds, including functional nutrients, amylopectin, phytochemicals, vitamins, enzymes, dietary fibers, and oils. Fruit and vegetable wastes (FVW) are rich in nutrients and extra nutritional compounds that contribute to the development of animal feed, bioactive ingredients, and ethanol production. In the development of active packaging films, pectin and other biopolymers are commonly used. In addition, the most recent research studies dealing with FVW have enhanced the physical, mechanical, antioxidant, and antimicrobial properties of packaging and biocomposite systems. Innovative technologies that can be used for sensitive bioactive compound extraction and fortification will be crucial in valorizing FVW completely; thus, this article aims to report the progress made in terms of the valorization of FVW and to emphasize the applications of FVW in active packaging and biocomposites, their by-products, and the innovative technologies (both thermal and non-thermal) that can be used for bioactive compounds extraction.

**Keywords:** fruit waste; vegetable waste; waste valorization; bioactive compound; active packaging; biocomposites; by-product; extraction; thermal processing; non-thermal processing


**5. Conclusions**

Review of the literature indicates that research on producing asphalt concrete mixes with only fine or coarse RAP particles has been scarce. The main goal of this study was to evaluate the effect of fine and coarse RAP fractions on performance of the high RAP content asphalt concrete mixes. Accordingly, three mixes were designed and investigated in this study, including a control HMA prepared with virgin materials, a Fine RAP mix (FRM) with 35% percent RAP, and a Coarse RAP mix (CRM) with 54% RAP. The total binder content considering the different contributions from the RAP particles was maintained the same for all the mixes. The following conclusions can be drawn based on the results of this study:

Black curve and white curve assumptions for the RAP particles were explored and it was concluded that the black rock assumption, which is commonly used, cannot be representative of the RAP contribution to the total binder content and the skeleton of the mix. The actual gradation lies somewhere in between these two extreme cases.

Overall, the CRM mix, exhibited acceptable performance with respect to rutting, fatigue, and thermal cracking. It can be concluded that the RAP particle size can have a more significant effect on the mix performance than the RAP content. RAP content should be considered along with other important parameters such as RAP particle size and gradation, recycled binder ratio, and RAP binder content.

**Author Contributions:** S.S.S. conceived of the presented idea. A.C. and H.B. developed the theory and verified the analytical methods and supervised the project. S.S.S. wrote the original draft; and P.T. wrote, reviewed, and edited the manuscript and verified the analysis. All authors discussed the results and contributed to the final manuscript.

**Funding:** This research was funded by The Pavements and Bituminous Materials Laboratory (LCMB).

**Acknowledgments:** This work was supported by The Pavements and Bituminous Materials Laboratory (LCMB) and the Centre for Pavement and Transportation Technology (CPATT). The authors would like to thank the companies in Quebec that provided us with the materials for the project.

**Conflicts of Interest:** The authors declare no conflict of interest.


**4. Conclusion**

In this chapter we explained about the significance of superstrate on antenna performance at millimeter wave frequencies. It is found that the size of the superstrate is critical, which is not the case in lower frequencies. Also we studied the antenna performance with different source of excitation. It is noted that superstrate technology is very good for a single patch and also for small array but not that much good for big arrays. As a conclusion, it is found that superstrate aperture antenna element / antenna array is a good candidate for wideband, high gain and high efficiency antenna design in millimetre wave range. Moreover it is easy to integrate with electronics by placing the feed on backside of the substrate where the electronic components are integrated, and the radiating aperture and superstrate (i.e. the radiation part) are on the other side.

### **5. References**

120 Wireless Communications and Networks – Recent Advances

It is noted from the study that single/small array superstrate antenna technology is very good for high gain, wide bandwidth and high efficiency, but is not suggestive for big arrays

**3. Comparison of superstrate slot coupled antenna with superstrate aperture**

Table VI gives the comparison of the superstrate aperture antenna element or antenna array presented with slot coupled superstrate antenna element or antenna array. It is clear that superstrate aperture antenna element / antenna array give broad S11 bandwidth of 15% / 13.3% with better efficiency as compared to superstrate slot coupled antenna element / antenna array. Also the antenna size is smaller compared to the other in both the cases. I.e. superstrate aperture antenna element or antenna array gives sufficient bandwidth, gain and

> Maximum gain (measured)

Aperture 15% 13.1 79% 6 mm x 13.5 mm x

slot coupled 6.8% 14.6 76% 10 mm x 10mm x

Aperture 13.3% 16.6 74% 13 mm x 20 mm, x

slot coupled 6.7% 16 63% 16 mm x 16 mm x

Table VI. Comparison between superstrate aperture s antenna and slot coupled superstrate

In this chapter we explained about the significance of superstrate on antenna performance at millimeter wave frequencies. It is found that the size of the superstrate is critical, which is not the case in lower frequencies. Also we studied the antenna performance with different source of excitation. It is noted that superstrate technology is very good for a single patch and also for small array but not that much good for big arrays. As a conclusion, it is found that superstrate aperture antenna element / antenna array is a good candidate for wideband, high gain and high efficiency antenna design in millimetre wave range. Moreover it is easy to integrate with electronics by placing the feed on backside of the substrate where the electronic components are integrated, and the radiating aperture and

Efficiency Estimated η

Size

3.48 mm

3.48 mm

3.48 mm

3.48 mm

because of the gain go up is not upto the point but is good in terms of efficiency.

**antenna**

efficiency for 60 GHz applications.

Return loss bandwidth (measured)

superstrate (i.e. the radiation part) are on the other side.

Antenna

Single superstrate

Single superstrate

2x2 superstrate

2x2 superstrate

**4. Conclusion**

antenna explained in section 2.


*Edited by Serdar Küçük*

Robotic technology has increasingly been preferred by the medical professionals since they have been used for several clinical applications. Medical robots are preferred since they present better results compared to traditional methods such as smaller incision, higher accuracy, and lesser recovery time. Medical robots can be divided into three progressive generations. The first-generation robots were originally industrial robots that had been modified for performing medical applications in orthopedics, neurosurgery, radiology, and radiotherapy in the 1980s. The second-generation robots have been especially developed for executing surgical operations in the 1990s. After the 2000s, the third-generation medical robots have been designed for performing difficult surgical and medical operations. From the first approved surgical robot AESOP to the current da Vinci Surgical System, there have been several different kinds of surgical robots produced until now. Although the history of surgical robots is very short compared to the history of surgery, thousands of surgical robots have been installed in hospitals worldwide, and hundreds of thousands of people have been treated by these surgical robots. Nowadays, the achievements of the surgical robotics amaze both medical professionals and the patients. It is noteworthy to follow up on the evolution of surgical robotics in the future.

Photo by wir0man / iStock


Acknowledgements

3. n1 � person1; …; np � personp n o

necessary resources for their operation

Natural Hazards - Risk, Exposure, Response, and Resilience

by application of rules from scheme R [12, 13].

solved by application of the introduced techniques.

necessary resources

5. n1 � dev1; …; nq � devq

previous elements of STS

In general case

8. Conclusion

will occur.

260

, representing amounts of different types of

�≥1, (67)

persons, which may stay alive after NHI, because they would be supplied by

4. n1 � tech1; …; ns f g � techs , representing amounts of the types of technical systems, which may operate after NHI, because resource base of STS contains all

� �, representing amounts of the types of separate devices, which do not enter any technical system from the previous set, but may operate separately because they may be supplied by necessary resources

6. n1 � res1; …; nt f g � rest , representing amounts of resources, which would remain in the resource base of STS after all the rest RB would be attached to all

so the only TMS, representing the final variant of distribution of the resources, remained in the RB after NHI, would be selected by application of some additional conditions. This task may be easily done by the use of filtering multigrammars (FMG); each FMG S ¼ , v0, R, F . along with kernel v0 and scheme R contains filter F, joining conditions, which provide selection of terminal multisets, generated

General case of the distributed STS is not more complicated and may be easily

Proposed multiset-based framework for the assessment of resilience of distributed sociotechnical systems to natural hazards provides flexible and sufficiently easy representation of knowledge about DSTS operation, understood as resource production, relocation, and consumption. Criterial base, introduced in this paper, may be effectively applicable in a posteriori as well as in a priori mode, i.e., for detection of "weak places" in DSTS and their strengthening, not waiting, when NH

As it was said higher, analytical capabilities of the described framework may be extended by implanting universal time scale into the basic knowledge representation, i.e., into multiset grammars and their various modifications. Such extension

implemented by DSTS in normal state as well as by DSTS, partly destroyed by NHI, and estimation of time periods, necessary for production of various amounts of resources in both cases. This approach makes possible also precise solution of different problems, concerning DSTS recovery [25, 26], on the unified background of resource-based techniques. The main tool for such work is the aforementioned temporal multiset grammars, which will be described in the following publications.

would provide full description of dynamics of manufacturing processes,

VS�1 � � �

The author is grateful to Prof. Fred Roberts and Prof. Don Saari for their useful discussions and to Prof. Alexey Gvishiani for his permanent support.


**Abstract**

The paper presents results obtained by the author for wing-body interference. The lift-curve slopes of the wing-body combinations are considered. A 2D potential model for cross-flow around the fuselage and a discrete vortex method (DVM) are used. Flat wings of various forms and the circular and elliptical cross sections of the fuselage are considered. It was found that the value of the lift-curve slopes of the wing-body combinations may exceed the same value for an isolated wing. An experimental and theoretical data obtained by other authors earlier confirm this result. Investigations to optimize the wing-body combination were carried within the framework of the proposed model. It was revealed that the maximums of the lift-curve slopes for the optimal midwing configuration with elliptical cross-section body had a sufficiently large relative width (more than 30% of the span wing). The advantage of the wing-fuselage combination with a circular cross section over an isolated wing for wing aspect ratio greater than 6 can reach 7.5% at the relative diameter of fuselage equal to approximately 0.2.

**Keywords:** wing-fuselage combination, lift-curve slope, discrete vortex method, 2D potential cross-flow model, optimization


**4.1 Alongshore variability**

Alongshore, the eastern extent of the BB survey recorded the highest magnitude topographic change. This is particularly evident in areas that experienced erosion of the full-frontal section of the foredune (**Figure 7A** and **B**). In these locations, the scarp line forms at or behind the former crest line, leading to slope failure,

#### **Figure 7.**

*A topographic change profile sampled from Brackley Beach (BB) east was measured from the pre-storm Dorian (SD) LiDAR DTM and and post-SD UAV DSM (A) BB east experienced significant scarping that eroded the frontal section of the foredune and lead to large volumetric losses (A and B). Aerial UAV images from pre-SD (C) and post-SD (D) show the removal of the stoss slope and formation of a steep scarp at the former crestline. Images from pre-SD (C) and post-SD (D).*

significant downcutting and volumetric loss (**Figure 7A** and **B**)*.* Only a small proportion of this eroded sediment was deposited on the upper beach surface suggesting that the majority of the sediment was moved further seaward, beyond the detectable range of our SfM DSM. Pre-SD, UAV images from July 2019 (**Figure 7C**) show a sparsely vegetated stoss slope extending 10 m seaward of the dune crest. Post-SD, UAV images from September 2019 show the stoss slope has been entirely eroded leaving behind a steep scarp face formed at the crest line (**Figure 7B** and **D**). The foredunes in this area consistently displayed stoss slopes exceeding the angle of repose for dry sand >34° and appeared to be temporarily maintained by surface moisture post-SD. Additional slumping of the surface is expected and may lead to further instability and reduction of dune height at the eastern extent of BB East.

The mid and western sections of BB recorded a continuous 1–2 m scarp at the base of the stoss slope, resulting in lower volumetric losses from the foredune in these areas (**Figure 8A** and **B**). At BB West, the transition between erosion and deposition occurs much closer to the dune toe, compared to BB east. Also, sediment deposition in this section of BB (**Figure 8A** and **B**) contained a high proportion of the volume eroded from the foredune (**Figure 8A**). In July 2019, prior to SD, the stoss slope of the foredune typically extended 13 m or more seaward of the crest (**Figure 8C**). Sediment accumulation was aided by the increased seaward extent of

> the vegetation during the summer months (**Figure 8C**) and promoted the intermittent growth of embryo dunes seaward of the established foredune (e.g., **Figure 8A** and **C**). Following SD in September 2019, the scarp line has eroded into the lower stoss slope and has removed any alongshore embryo dunes present pre-SD

> *Alongshore variability of antecedent (i.e., prior to the storm event) morphometry, measured form the LiDAR bathymetric and topographic survey at Brackley Beach (BB; A) east and BB west (B). BB east (A) displayed a higher sloping near shore, narrower beach (Bw) and dune width (Dw*Þ*, lower dune height (Dh*Þ*, higher slope* ð Þ *Ds , and lower D*ð Þ*v than BB west (B). These antecedent beach-dune metrics indicate that BB east was more*

*vulnerable to storm erosion that resulted in higher magnitude scarping in this area of BB.*

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

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

The variability of storm impacts observed at BB were likely controlled by the antecedent morphology that existed prior to SD. From the LiDAR DTM, BB east was observed to have a higher nearshore slope and closer inner bar structure, in comparison to BB west (**Figure 9A** and **B**). This could have led to higher wave energy and erosive potential at BB East during SD, and stronger counter current moving sediment further seaward post-SD (e.g., **Figure 7A** and **8A**). Beach width ð Þ *Bw* at BB East was also significantly narrower than BB West and likely would have increased the exposure of the foredune to wave run up, despite a lower storm surge level relative to the dune toe (**Figure 9A** and **B**). In comparison to BB West, a combination of foredune metrics including narrower dune width ð Þ *Dw* , shorter dune heights ð Þ *Dh* , higher dune slopes ð Þ *Ds* , and lower dune volumes ð Þ *Dv* indicate that BB East was more vulnerable to storm induced erosion (**Figure 9A** and **B**). Foredunes that displayed a narrower *Dw* and high *Ds* at BB East experienced the highest magnitude of erosion resulting from major slope failures. While the foredune at BB West appeared to only lose a small proportion of the frontal *Dv*, a significant reduction of *Dv* at BB East could lead to further lowering of the foredune

making this area of BB increasingly vulnerable to future storm events.

UAV monitoring has allowed for the rapid assessment of Brackley Beach (BB), 11 days following Storm Dorian (SD). Projected 7–8 m significant wave heights and 1.2 m storm surge levels associated with SD resulted in a highly erosive post storm

(**Figure 8D**).

**Figure 9.**

**5. Discussion**

**79**

#### **Figure 8.**

*A topographic change profile sampled from Brackley Beach (BB) west was measured from the pre-storm Dorian (SD) LiDAR DTM and and post-SD UAV DSM (A). BB west experienced scarping at the base of the foredune and removal of embryo dunes (A and B). Aerial UAV images from pre-SD (C) and post-SD (D) show the removal of seaward dune deposits leaving behind a low continuous scarp alongshore.*

*Monitoring Storm Impacts on Sandy Coastlines with UAVs DOI: http://dx.doi.org/10.5772/intechopen.91459*

#### **Figure 9.**

significant downcutting and volumetric loss (**Figure 7A** and **B**)*.* Only a small pro-

(**Figure 7C**) show a sparsely vegetated stoss slope extending 10 m seaward of the dune crest. Post-SD, UAV images from September 2019 show the stoss slope has been entirely eroded leaving behind a steep scarp face formed at the crest line (**Figure 7B** and **D**). The foredunes in this area consistently displayed stoss slopes exceeding the angle of repose for dry sand >34° and appeared to be temporarily maintained by surface moisture post-SD. Additional slumping of the surface is expected and may lead to further instability and reduction of dune height at the

The mid and western sections of BB recorded a continuous 1–2 m scarp at the base of the stoss slope, resulting in lower volumetric losses from the foredune in these areas (**Figure 8A** and **B**). At BB West, the transition between erosion and deposition occurs much closer to the dune toe, compared to BB east. Also, sediment deposition in this section of BB (**Figure 8A** and **B**) contained a high proportion of the volume eroded from the foredune (**Figure 8A**). In July 2019, prior to SD, the stoss slope of the foredune typically extended 13 m or more seaward of the crest (**Figure 8C**). Sediment accumulation was aided by the increased seaward extent of

*A topographic change profile sampled from Brackley Beach (BB) west was measured from the pre-storm Dorian (SD) LiDAR DTM and and post-SD UAV DSM (A). BB west experienced scarping at the base of the foredune and removal of embryo dunes (A and B). Aerial UAV images from pre-SD (C) and post-SD (D) show the*

*removal of seaward dune deposits leaving behind a low continuous scarp alongshore.*

portion of this eroded sediment was deposited on the upper beach surface suggesting that the majority of the sediment was moved further seaward, beyond the detectable range of our SfM DSM. Pre-SD, UAV images from July 2019

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

eastern extent of BB East.

**Figure 8.**

**78**

*Alongshore variability of antecedent (i.e., prior to the storm event) morphometry, measured form the LiDAR bathymetric and topographic survey at Brackley Beach (BB; A) east and BB west (B). BB east (A) displayed a higher sloping near shore, narrower beach (Bw) and dune width (Dw*Þ*, lower dune height (Dh*Þ*, higher slope* ð Þ *Ds , and lower D*ð Þ*v than BB west (B). These antecedent beach-dune metrics indicate that BB east was more vulnerable to storm erosion that resulted in higher magnitude scarping in this area of BB.*

the vegetation during the summer months (**Figure 8C**) and promoted the intermittent growth of embryo dunes seaward of the established foredune (e.g., **Figure 8A** and **C**). Following SD in September 2019, the scarp line has eroded into the lower stoss slope and has removed any alongshore embryo dunes present pre-SD (**Figure 8D**).

The variability of storm impacts observed at BB were likely controlled by the antecedent morphology that existed prior to SD. From the LiDAR DTM, BB east was observed to have a higher nearshore slope and closer inner bar structure, in comparison to BB west (**Figure 9A** and **B**). This could have led to higher wave energy and erosive potential at BB East during SD, and stronger counter current moving sediment further seaward post-SD (e.g., **Figure 7A** and **8A**). Beach width ð Þ *Bw* at BB East was also significantly narrower than BB West and likely would have increased the exposure of the foredune to wave run up, despite a lower storm surge level relative to the dune toe (**Figure 9A** and **B**). In comparison to BB West, a combination of foredune metrics including narrower dune width ð Þ *Dw* , shorter dune heights ð Þ *Dh* , higher dune slopes ð Þ *Ds* , and lower dune volumes ð Þ *Dv* indicate that BB East was more vulnerable to storm induced erosion (**Figure 9A** and **B**). Foredunes that displayed a narrower *Dw* and high *Ds* at BB East experienced the highest magnitude of erosion resulting from major slope failures. While the foredune at BB West appeared to only lose a small proportion of the frontal *Dv*, a significant reduction of *Dv* at BB East could lead to further lowering of the foredune making this area of BB increasingly vulnerable to future storm events.

### **5. Discussion**

UAV monitoring has allowed for the rapid assessment of Brackley Beach (BB), 11 days following Storm Dorian (SD). Projected 7–8 m significant wave heights and 1.2 m storm surge levels associated with SD resulted in a highly erosive post storm

surface, the majority of which occurred at the beach-dune boundary (**Figure 6A** and **B**). A total of 11,000 m3 of erosion was recorded between a pre-SD LiDAR and post-SD UAV survey (**Table 1**). This included the complete removal of embryo dune deposits and the formation of a continuous foredune scarp in the middle and western sections of BB (**Figure 8A** and **B**). At BB East, significant slope failure and high magnitude erosion of the frontal section of the foredune was observed (**Figure 7A** and **B**).

artifacts (e.g., vegetation). This chapter has attempted to address some of these common challenges using basic quality control measures and handling of data uncertainties. In doing so, we were able to confidently quantify alongshore topographic and volumetric changes resulting from a storm event. Survey uncertainties in our post-storm measurements were mitigated due the implementation of quality controls to produce high accuracy (i.e., low RMSE) surface models from UAV surveys. Furthermore, environmental uncertainties were reduced in large part by the wave induced removal of vegetation from the beach dune boundary post-SD. While vegetation uncertainty did not significantly constrain our post storm measurements of the dune scarp, subsequent dune recovery studies will need to develop new strategies to handle uncertainty introduced by vegetation recolonization of the

The quality controls measures, discussed in Section 3, partially include data processing techniques that are unique to the Pix4D software. It is currently beyond the scope of this chapter and expertise of the authors to provide a comprehensive review of different SfM software. Therefore, the remainder of this discussion will

UAV SfM surveys have proven to be highly accurate with RMSE values, typically <1o cm [36, 38–40, 43]. This level of accuracy is capable of monitoring topographic adjustments associated with storm impacts. However, the uncertainty of topographic change measurements should be reported especially when changes approach the same magnitude of the survey error. While the RMSE values are commonly reported, interpretation of how these values influence uncertainty in topographic change measurements are not. To address survey error, a previous study used RMSE as a threshold to monitor topographic change [44]. In this study, the RMSE value was 16 cm and only positive or negative values in exceedance of �16 cm were reported. This method is effective at filtering data that could represent no change, but there still could be a high level of uncertainty associated with low magnitude changes. For instance, 17 cm of deposition recorded from a 1 m � 1 m pixel would result in a volume change (*ΔV*) and volume change uncertainty (*ΔVU*)

the range of uncertainty. This indicates that interpreting the significance of low magnitude topographic changes should be done carefully and only after survey

Results presented in this chapter limited survey uncertainty by applying a topographic change threshold ð Þ *CL*95% that approximates to a minimum detectable range of 1.96 times the propagated RMSE value. Applying *CL*95% effectively increased the confidence in *ΔV* measurements by filtering out small magnitude changes that are disproportionately responsible for high levels of *ΔVU*. For example, 42% of the total observable area of post-SD BB was classified as a zone of elevational uncertainty but, once removed, this only resulted in a change of total *ΔV* reported by 3% (**Table 1**). Furthermore, the ratio between *ΔV* and *ΔVU* dropped from 49 to 23% prior to- and after the*CL*95% threshold was applied. It is important to note that *ΔV* below the *CL*95% threshold, or within the zone of uncertainty, may represent actual change; however, to increase confidence in reported *ΔV* values it is important to systematically address survey uncertainty. This chapter has demonstrated that thresholding can be an effective approach to reduce topographic change uncertainties, but the most effective approach remains the quality of repeatable survey strategies and data processing (e.g., discussed in Section 3). For coastal studies, maintaining a high level of survey accuracy limits the areal extent and vertical range

uncertainty has been reported and adequately addressed.

, respectively. Or in other words, 94% of the *ΔV* would be within

focus on addressing survey and environmental uncertainties.

**5.1 Survey and environmental uncertainties**

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

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

backshore zone.

0.17 m3 � 0.16 m3

**81**

Despite a highly erosive post storm surface, a follow up field investigation in November 2019, or 2 months post-SD, observed the that the initial stages of dune recovery were already taking place. Evidence of the widespread remobilization of beach sediment by eolian processes was observed in the accumulation of dry ripple laden sediment deposits at beach-dune boundary, both across and alongshore (**Figure 10A**). This has resulted in the initial deposition of sediment into the dune scarp (**Figure 10B** and **C**) and ramps forming in areas of higher magnitude deposition (**Figure 10D**). Monitoring storm impacts can reveal initial topographic adjustments resulting from a single event; however, subsequent beach-dune responses can provide a broader understanding on the resiliency of sandy coastlines. Given the low cost, rapidity, and high resolution of UAV SfM surveys, researchers will increasingly have access to high resolution geo-spatial data sets to continuously monitor both short- and longer-term coastal dynamics.

Although UAV SfM research has increased significantly over the last few years, studies that have reported topographic or volumetric change from beach-dune systems are still limited [37, 38, 40, 44, 45]. In part, this has been due to the inherent difficulties in data collection, post-processing, and handling of survey

#### **Figure 10.**

*Initial dune recovery was observed during a follow up field site assessment in November 2019, or 2 months following storm Dorian (SD). Alonshore, dry sediment has begun to accumulate at the beach-dune boundary (A). This has resulted in the initial accumulation of sediment at the dune scarp (B and C) and ramp development in areas of higher magnitude deposition (D).*

*Monitoring Storm Impacts on Sandy Coastlines with UAVs DOI: http://dx.doi.org/10.5772/intechopen.91459*

surface, the majority of which occurred at the beach-dune boundary (**Figure 6A** and **B**). A total of 11,000 m3 of erosion was recorded between a pre-SD LiDAR and post-SD UAV survey (**Table 1**). This included the complete removal of embryo dune deposits and the formation of a continuous foredune scarp in the middle and western sections of BB (**Figure 8A** and **B**). At BB East, significant slope failure and

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

high magnitude erosion of the frontal section of the foredune was observed

monitor both short- and longer-term coastal dynamics.

Despite a highly erosive post storm surface, a follow up field investigation in November 2019, or 2 months post-SD, observed the that the initial stages of dune recovery were already taking place. Evidence of the widespread remobilization of beach sediment by eolian processes was observed in the accumulation of dry ripple laden sediment deposits at beach-dune boundary, both across and alongshore (**Figure 10A**). This has resulted in the initial deposition of sediment into the dune scarp (**Figure 10B** and **C**) and ramps forming in areas of higher magnitude deposition (**Figure 10D**). Monitoring storm impacts can reveal initial topographic adjustments resulting from a single event; however, subsequent beach-dune responses can provide a broader understanding on the resiliency of sandy coastlines. Given the low cost, rapidity, and high resolution of UAV SfM surveys, researchers will increasingly have access to high resolution geo-spatial data sets to continuously

Although UAV SfM research has increased significantly over the last few years,

studies that have reported topographic or volumetric change from beach-dune systems are still limited [37, 38, 40, 44, 45]. In part, this has been due to the inherent difficulties in data collection, post-processing, and handling of survey

*Initial dune recovery was observed during a follow up field site assessment in November 2019, or 2 months following storm Dorian (SD). Alonshore, dry sediment has begun to accumulate at the beach-dune boundary (A). This has resulted in the initial accumulation of sediment at the dune scarp (B and C) and ramp*

(**Figure 7A** and **B**).

**Figure 10.**

**80**

*development in areas of higher magnitude deposition (D).*

artifacts (e.g., vegetation). This chapter has attempted to address some of these common challenges using basic quality control measures and handling of data uncertainties. In doing so, we were able to confidently quantify alongshore topographic and volumetric changes resulting from a storm event. Survey uncertainties in our post-storm measurements were mitigated due the implementation of quality controls to produce high accuracy (i.e., low RMSE) surface models from UAV surveys. Furthermore, environmental uncertainties were reduced in large part by the wave induced removal of vegetation from the beach dune boundary post-SD. While vegetation uncertainty did not significantly constrain our post storm measurements of the dune scarp, subsequent dune recovery studies will need to develop new strategies to handle uncertainty introduced by vegetation recolonization of the backshore zone.

The quality controls measures, discussed in Section 3, partially include data processing techniques that are unique to the Pix4D software. It is currently beyond the scope of this chapter and expertise of the authors to provide a comprehensive review of different SfM software. Therefore, the remainder of this discussion will focus on addressing survey and environmental uncertainties.

#### **5.1 Survey and environmental uncertainties**

UAV SfM surveys have proven to be highly accurate with RMSE values, typically <1o cm [36, 38–40, 43]. This level of accuracy is capable of monitoring topographic adjustments associated with storm impacts. However, the uncertainty of topographic change measurements should be reported especially when changes approach the same magnitude of the survey error. While the RMSE values are commonly reported, interpretation of how these values influence uncertainty in topographic change measurements are not. To address survey error, a previous study used RMSE as a threshold to monitor topographic change [44]. In this study, the RMSE value was 16 cm and only positive or negative values in exceedance of �16 cm were reported. This method is effective at filtering data that could represent no change, but there still could be a high level of uncertainty associated with low magnitude changes. For instance, 17 cm of deposition recorded from a 1 m � 1 m pixel would result in a volume change (*ΔV*) and volume change uncertainty (*ΔVU*) 0.17 m3 � 0.16 m3 , respectively. Or in other words, 94% of the *ΔV* would be within the range of uncertainty. This indicates that interpreting the significance of low magnitude topographic changes should be done carefully and only after survey uncertainty has been reported and adequately addressed.

Results presented in this chapter limited survey uncertainty by applying a topographic change threshold ð Þ *CL*95% that approximates to a minimum detectable range of 1.96 times the propagated RMSE value. Applying *CL*95% effectively increased the confidence in *ΔV* measurements by filtering out small magnitude changes that are disproportionately responsible for high levels of *ΔVU*. For example, 42% of the total observable area of post-SD BB was classified as a zone of elevational uncertainty but, once removed, this only resulted in a change of total *ΔV* reported by 3% (**Table 1**). Furthermore, the ratio between *ΔV* and *ΔVU* dropped from 49 to 23% prior to- and after the*CL*95% threshold was applied. It is important to note that *ΔV* below the *CL*95% threshold, or within the zone of uncertainty, may represent actual change; however, to increase confidence in reported *ΔV* values it is important to systematically address survey uncertainty. This chapter has demonstrated that thresholding can be an effective approach to reduce topographic change uncertainties, but the most effective approach remains the quality of repeatable survey strategies and data processing (e.g., discussed in Section 3). For coastal studies, maintaining a high level of survey accuracy limits the areal extent and vertical range of elevational uncertainty and will allow for low magnitude and morphologically significant topographic adjustments (e.g., dune recovery) to be observed through time.

Environmental uncertainties within UAV SfM data also pose a significant challenge as they are space and time dependent. Images taken at the water boundary often captures breaking waves, wave run up, or low contrast moist surfaces in the nearshore and foreshore zones. This can lead to insufficiencies in ATP generation during SfM processing [36, 37, 41] and inconsistencies on the seaward extent in which repeat topographic change measurements are taken. Previously, mean sea level (MSL; [45]) and the wet-dry line [44] have been identified as the observable extent of coastal DSMs. These provide a reasonable estimate of the average annual water line or the water line at a particular time but can become inconsistent when monitoring spatially and/or temporally extensive surveys.

For instance, 2.5 km of post-SD BB was surveyed over a two-day period with approximately 4 h of surveying time per day. Considering the 6 h semi-diurnal (i.e., two high and low) tidal cycle at BB, variability of the across shore extent of the wetdry line was captured during our alongshore survey. Beyond daily cycles, monthly and annual cycles effect the magnitude of the tidal range and can result in significant variability of the water line boundary during repeat surveys. This may introduce uncertainty when quantifying multi-temporal sediment budgets (e.g., *ΔV*) and beach metrics (e.g., beach width) as these values may be over- or underestimated depending on unique spatiotemporal tidal patterns captured while surveying. Alternatively, this chapter used the HHW line as consistent elevation that is typically above the intertidal foreshore zone regardless of tide cycles and ranges. While this approach is conservative in constricting the seaward survey extent, it provides a repeatable methodology to limit environmental uncertainty at the water line boundary.

At the beach-dune boundary, perhaps the biggest challenge in producing repeat UAV surveys in sandy coastal systems is the presence and handling of vegetation. Vegetation can directly lead to over-estimation of surface elevation values in SfM DSMs or point clouds [41]. Furthermore, any topographic change values that have not removed vegetation would be inaccurate as they could represent either a change in vegetation density or canopy height at the seasonal scale or loss of vegetation following a disturbance [38]. Post-SD, elevated storm surge removed vegetation seaward of the scarp along BB and did not have a significant influence our ability to measure topographic change; however, following storm events or seasons, vegetation recolonization could constrain subsequent monitoring surveys during periods of high growth rates.

the point cloud was completed in Pix4D and consisted of classifying and removing supra-surficial features and produce a 'bare earth' DTM. The standard Pix4D vegetation class, 'high vegetation', may be suitable for other prominent vegetation types (e.g., trees) but was not designed for and, therefore, unable to adequately classify and remove low profile vegetation that typically grows on the backshore (e.g., *Ab*;

*An across profile and vegetation density, measured from aerial UAV photos taken at Brackley Beach (BB) in July 2019, show variable density at the seaward extent followed by rapid increase in density from the mid stoss slope to dune crest (A). The UAV SfM point cloud generated for this same section of BB, show variability in bare sand and vegetated points captured in the point cloud as you move landward from the sparsely vegetated*

Vegetation filtering can be improved by applying algorithms that have been specifically designed for removing beach grasses. For example, a recent study [41] applied a vegetation filtering method originally designed to filter *Ab* from a TLS derived point cloud [51]. Results of this study show that sparsely vegetated

foredunes can be effectively filtered from relatively low density SfM derived point

vegetation densities, relative to higher density point clouds (i.e., 2000 points per

) produced from a TLS sensor. Vegetation is often intermittent at the beach-dune boundary, suggesting that point cloud filtering approaches, especially when applied to higher density SfM point clouds (e.g., **Figure 11B**), could accurately monitor low magnitude topographic changes associated dune building and recovery. In this regard, further studies are needed to test the capability of different vegetation filtering algorithms to remove variant patterns of backshore vegetation growth. Handling of vegetation remains problematic and, without using a suitable filtering method or quantifying the additional uncertainty that it may introduce, should not

), but do not perform as well in areas of higher

**Figure 11B**).

**Figure 11.**

m2

**83**

clouds (i.e., 100 points per m2

*beach-dune boundary to highly vegetated back dune zone (B).*

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

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

be included in topographic change measurements.

A UAV vegetation monitoring study at BB from July 2019 sampled vegetation density, almost entirely low profile (i.e., 50–60 cm) *Ammophila breviligulata* (*Ab*)*,* both across- and alongshore during the peak growing season [53]. Across shore (i.e., from the shoreline to the back dune zone), vegetation density tended to fluctuate between the vegetation line and dune toe, followed by a rapid increase in density up the stoss slope, before reaching 100% density near the crest (**Figure 11A**). Alongshore, the overall density between the shoreline and dune crest, sampled at every 10 m along BB, ranged from 10 to 60% (**Figure 11B**). Taking into account uncertainty introduced with the fluctuating water line, only a narrow band landward of the foreshore and seaward of the vegetation line would consistently be observable without error introduced by spatially and seasonally variant vegetation density patterns.

A high resolution SfM derived point cloud of 2000 points per m2 was produced from a preliminary UAV survey during July 2019 (**Figure 11B**). The high vegetation density during the peak growing season is evident in the backshore; however, a large number of 'bare earth' points are also visible (**Figure 11B**). An attempt to filter *Monitoring Storm Impacts on Sandy Coastlines with UAVs DOI: http://dx.doi.org/10.5772/intechopen.91459*

#### **Figure 11.**

of elevational uncertainty and will allow for low magnitude and morphologically significant topographic adjustments (e.g., dune recovery) to be observed through time. Environmental uncertainties within UAV SfM data also pose a significant challenge as they are space and time dependent. Images taken at the water boundary often captures breaking waves, wave run up, or low contrast moist surfaces in the nearshore and foreshore zones. This can lead to insufficiencies in ATP generation during SfM processing [36, 37, 41] and inconsistencies on the seaward extent in which repeat topographic change measurements are taken. Previously, mean sea level (MSL; [45]) and the wet-dry line [44] have been identified as the observable extent of coastal DSMs. These provide a reasonable estimate of the average annual water line or the water line at a particular time but can become inconsistent when

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

For instance, 2.5 km of post-SD BB was surveyed over a two-day period with approximately 4 h of surveying time per day. Considering the 6 h semi-diurnal (i.e., two high and low) tidal cycle at BB, variability of the across shore extent of the wetdry line was captured during our alongshore survey. Beyond daily cycles, monthly and annual cycles effect the magnitude of the tidal range and can result in significant variability of the water line boundary during repeat surveys. This may introduce uncertainty when quantifying multi-temporal sediment budgets (e.g., *ΔV*) and beach metrics (e.g., beach width) as these values may be over- or underestimated depending on unique spatiotemporal tidal patterns captured while surveying. Alternatively, this chapter used the HHW line as consistent elevation that is typically above the intertidal foreshore zone regardless of tide cycles and ranges. While this approach is conservative in constricting the seaward survey extent, it provides a repeatable methodology to limit environmental uncertainty at the water

At the beach-dune boundary, perhaps the biggest challenge in producing repeat UAV surveys in sandy coastal systems is the presence and handling of vegetation. Vegetation can directly lead to over-estimation of surface elevation values in SfM DSMs or point clouds [41]. Furthermore, any topographic change values that have not removed vegetation would be inaccurate as they could represent either a change in vegetation density or canopy height at the seasonal scale or loss of vegetation following a disturbance [38]. Post-SD, elevated storm surge removed vegetation seaward of the scarp along BB and did not have a significant influence our ability to measure topographic change; however, following storm events or seasons, vegetation recolonization could constrain subsequent monitoring surveys during periods

A UAV vegetation monitoring study at BB from July 2019 sampled vegetation density, almost entirely low profile (i.e., 50–60 cm) *Ammophila breviligulata* (*Ab*)*,* both across- and alongshore during the peak growing season [53]. Across shore (i.e., from the shoreline to the back dune zone), vegetation density tended to fluctuate between the vegetation line and dune toe, followed by a rapid increase in

(**Figure 11A**). Alongshore, the overall density between the shoreline and dune crest, sampled at every 10 m along BB, ranged from 10 to 60% (**Figure 11B**). Taking into account uncertainty introduced with the fluctuating water line, only a narrow band landward of the foreshore and seaward of the vegetation line would consistently be observable without error introduced by spatially and seasonally variant vegetation

A high resolution SfM derived point cloud of 2000 points per m2 was produced from a preliminary UAV survey during July 2019 (**Figure 11B**). The high vegetation density during the peak growing season is evident in the backshore; however, a large number of 'bare earth' points are also visible (**Figure 11B**). An attempt to filter

density up the stoss slope, before reaching 100% density near the crest

monitoring spatially and/or temporally extensive surveys.

line boundary.

of high growth rates.

density patterns.

**82**

*An across profile and vegetation density, measured from aerial UAV photos taken at Brackley Beach (BB) in July 2019, show variable density at the seaward extent followed by rapid increase in density from the mid stoss slope to dune crest (A). The UAV SfM point cloud generated for this same section of BB, show variability in bare sand and vegetated points captured in the point cloud as you move landward from the sparsely vegetated beach-dune boundary to highly vegetated back dune zone (B).*

the point cloud was completed in Pix4D and consisted of classifying and removing supra-surficial features and produce a 'bare earth' DTM. The standard Pix4D vegetation class, 'high vegetation', may be suitable for other prominent vegetation types (e.g., trees) but was not designed for and, therefore, unable to adequately classify and remove low profile vegetation that typically grows on the backshore (e.g., *Ab*; **Figure 11B**).

Vegetation filtering can be improved by applying algorithms that have been specifically designed for removing beach grasses. For example, a recent study [41] applied a vegetation filtering method originally designed to filter *Ab* from a TLS derived point cloud [51]. Results of this study show that sparsely vegetated foredunes can be effectively filtered from relatively low density SfM derived point clouds (i.e., 100 points per m2 ), but do not perform as well in areas of higher vegetation densities, relative to higher density point clouds (i.e., 2000 points per m2 ) produced from a TLS sensor. Vegetation is often intermittent at the beach-dune boundary, suggesting that point cloud filtering approaches, especially when applied to higher density SfM point clouds (e.g., **Figure 11B**), could accurately monitor low magnitude topographic changes associated dune building and recovery. In this regard, further studies are needed to test the capability of different vegetation filtering algorithms to remove variant patterns of backshore vegetation growth. Handling of vegetation remains problematic and, without using a suitable filtering method or quantifying the additional uncertainty that it may introduce, should not be included in topographic change measurements.

The density and variety of vegetation increases at BB moving from the crest landward. As a result, a significant reduction in the 'bare earth' points become problematic in these areas (**Figure 11B**). Vegetation filtering then becomes a less viable option. Although, these locations are less prone to low magnitude topographic changes, quantifying dune metrics (e.g., *Dh* or *Dv*) measured from UAV surveys would likely be inflated. Highly vegetated back dune areas are typically stable as they are protected from the wave, tidal, and wind processes that are actively shaping the seaward zones. These locations are likely to remain a 'zone of uncertainty' in UAV SfM surveys without significant vegetation removal or burial through blowout development, breaching, or overwash events.

**Acknowledgements**

**Author details**

**85**

Alex Smith\*, Brianna Lunardi, Elizabeth George and Chris Houser

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

School of the Environment, University of Windsor, Windsor, Ontario, Canada

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

The authors would like to thank Prince Edward Island National Park for permitting our field work at Brackley Beach, logistical assistance, and supplying of equipment. We would also like to thank CBCL Limited for supplying the baseline LiDAR survey used in this chapter. Finally, a special thank you to Phillipe Wernette

and Jacob Lehner for their invaluable field assistance.

*Monitoring Storm Impacts on Sandy Coastlines with UAVs*

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

Therefore, current UAV SfM applications for monitoring repeat topographic changes occurring in coastal systems are likely to be constrained to the backshore. The accuracy of UAV SfM data on unvegetated surfaces has been demonstrated and there is significant potential to increase the accuracy of sparsely vegetated foredune slopes by applying filtering algorithms (e.g., [41]). It is evident that challenges still remain in resolving water and vegetation boundaries; however, the benefits of UAVs are also clear as they provide an accessible cost-effective method to produce high resolution and spatially extensive surveys of sandy coastal systems. The number of UAV SfM monitoring studies in coastal systems are likely to increase in the coming years, but in order for these studies to confidently report topographic adjustments between the beach-dune boundary, addressing data uncertainties and improvements in vegetation filtering methods are needed.


**9. Recommendations**

The recommendations are outlined as follows;

**49**

**Author details**

Shakira Ghazanfar

Islamabad, Pakistan

*Understanding the Mechanism of Action of Indigenous Target Probiotic Yeast: Linking…*

Functional Genomics and Bioinformatics, National Institute of Genomics and Agriculture Biotechnology (NIGAB), National Agricultural Research Centre,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

*DOI: http://dx.doi.org/10.5772/intechopen.95822*

*Understanding the Mechanism of Action of Indigenous Target Probiotic Yeast: Linking… DOI: http://dx.doi.org/10.5772/intechopen.95822*