**2.1 Background**

In the last decade, the uptake of robotics and autonomous systems (RAS) for environmental monitoring has increased significantly. The low cost and availability of some of the technologies in the market have facilitated the integration of RAS solutions within the environmental sector. Perhaps the most significant uptake of RAS relates to unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). UAVs are small aircraft controlled remotely (i.e. with no human pilot on board). When equipped with specific sensors, they enable on-demand and generally high-resolution data collection. This overcomes some of the limitations of more traditional remote sensing methods such as satellites. Their capabilities also enable the collection of information under low cloud cover, thus increasing the operational window for environmental monitoring.

A wide range of sensors are currently available in the market for integration on existing off-the-shelf platforms (**Figure 2**). These sensors include multispectral, thermal, hyper-spectral and high-resolution red, green and blue (RGB) cameras and water quality probes. RGB cameras are the most accessible and therefore currently the most used sensor for environmental monitoring. However, recent advances in sensor miniaturization (e.g. [17]) facilitate the integration of combined sensors on a single platform, enabling RGB imagery to be coupled with other sources of information.

#### **2.2 Unmanned aerial vehicles**

Within the context of lagoon characterization, UAVs have been used to assess the preferred locations and distribution at a fine scale of blacktip reef sharks and pink whiprays within a coral lagoon and reef systems off French Polynesia (Morea) [11]. This study focused on the assessment of the differences in species presence along reef habitats such as fringing, channels and sandflats. Density estimates of both species were estimated from the video footage recorded with a GoPro Hero 3+ Silver Edition camera fitted to a DJI Phantom II UAV quadcopter. The study highlighted the usefulness of UAVs to detect statistically significant differences in species densities across lagoon habitats [11].

**147**

*Autonomous Systems for the Environmental Characterization of Lagoons*

UAVs have also been used to make water surface elevation (i.e. orthometric water height above mean sea level) and bathymetry observations in lagoons of the Yucatan Peninsula (Mexico) [18]. In Ref. [18], the authors used a DJI hexacopter Spreading Wings S900 UAV equipped with an RGB high-resolution camera (Sony DSC-RX100) and lower-resolution fish-eye lens Eken H9 camera to characterize water surface elevation. The UAV was enabled to control a tethered sonar sensor (Deeper Smart Sonar PRO + Deeper, UAB, Vilnius, Lithuania) able to map the bathymetry of the lagoons. The information thus gathered enabled the estimation of water depth. The authors reported the technology to be accurate and fit for purpose, with errors less than 7 cm for water surface elevation estimation and less than 3.8% of the actual water depth. The study also highlighted the flexibility and low cost of the technology and its capacity to monitor remote areas that are difficult

*Schematic diagram showing an array of sensors that can be integrated to drone platforms [i.e. red, green and blue (RGB) camera, multispectral camera, thermal camera, hyper-spectral camera, laser scanner,* 

Lally et al. [19] reviewed the latest advances in UAV technology (platforms, payload and probe integration) for water sample capture and physico-chemical analysis. The potential of UAVs to gather water samples in lagoons is still unexplored. To date and to the authors' knowledge, only a few examples exist of this application of the technology [19] but none within lagoon environments. Multiple limitations still curtail the uptake of the technology and include water samples are too small to be representative, restrictive drone technology, low rate of sample collection and low reliability [19]. For the technology to be transferable and cost-effective for lagoon characterization, a range of enhancements are required such as increased payload capability, increased battery endurance, beyond visual line of sight operation and

It is evident that the use of the technology for water sample collection would be of benefit to managers and conservationists alike, especially within a regulatory context where water quality assessment of such ecosystems is required on a regular basis. In England, for example, there are 52 coastal saline lagoons defined in Special Protection Areas or Special Areas of Conservation, with an additional 28 lagoonal water bodies identified under the Water Framework Directive [6]. All these lagoons and lagoonal water bodies require monitoring, assessment and reporting of the

*DOI: http://dx.doi.org/10.5772/intechopen.90405*

to access by human operators.

*conductivity-temperature-depth probe].*

**Figure 2.**

real-time physico-chemical measurement [19].

**2.3 Autonomous underwater vehicles, ROVs and on-water platforms**

*Autonomous Systems for the Environmental Characterization of Lagoons DOI: http://dx.doi.org/10.5772/intechopen.90405*

#### **Figure 2.**

*Lagoon Environments around the World - A Scientific Perspective*

The aim of this chapter is to review applications of recent technological advances

within the context of lagoon environmental monitoring and define the implications for future remote sensing-based monitoring of these environments and the associated management strategies. In particular, this chapter reviews reported uses of robotics and autonomous systems for the characterization of lagoon ecosystems. It also highlights future applications of such technology and interprets the findings within the context of lagoon management and protection. The first section highlights how unmanned aerial vehicles, autonomous underwater vehicles and autonomous on-water platforms have been used to enhance existing lagoon environment monitoring practices. The second section describes the implications of the use of such technology for survey design, their potential to provide continuous information in time and space and the need for tailored data processing methods. The last section identifies some of the advantages and limitations of these remote sensing monitoring methods within the context of environmental management and current

In the last decade, the uptake of robotics and autonomous systems (RAS) for environmental monitoring has increased significantly. The low cost and availability of some of the technologies in the market have facilitated the integration of RAS solutions within the environmental sector. Perhaps the most significant uptake of RAS relates to unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). UAVs are small aircraft controlled remotely (i.e. with no human pilot on board). When equipped with specific sensors, they enable on-demand and generally high-resolution data collection. This overcomes some of the limitations of more traditional remote sensing methods such as satellites. Their capabilities also enable the collection of information under low cloud cover, thus increasing the

A wide range of sensors are currently available in the market for integration on existing off-the-shelf platforms (**Figure 2**). These sensors include multispectral, thermal, hyper-spectral and high-resolution red, green and blue (RGB) cameras and water quality probes. RGB cameras are the most accessible and therefore currently the most used sensor for environmental monitoring. However, recent advances in sensor miniaturization (e.g. [17]) facilitate the integration of combined sensors on a single platform, enabling RGB imagery to be coupled with other

Within the context of lagoon characterization, UAVs have been used to assess the preferred locations and distribution at a fine scale of blacktip reef sharks and pink whiprays within a coral lagoon and reef systems off French Polynesia (Morea) [11]. This study focused on the assessment of the differences in species presence along reef habitats such as fringing, channels and sandflats. Density estimates of both species were estimated from the video footage recorded with a GoPro Hero 3+ Silver Edition camera fitted to a DJI Phantom II UAV quadcopter. The study highlighted the usefulness of UAVs to detect statistically significant differences in species densi-

**146**

practice.

**2.1 Background**

sources of information.

**2.2 Unmanned aerial vehicles**

ties across lagoon habitats [11].

**2. Robots and autonomous systems**

operational window for environmental monitoring.

*Schematic diagram showing an array of sensors that can be integrated to drone platforms [i.e. red, green and blue (RGB) camera, multispectral camera, thermal camera, hyper-spectral camera, laser scanner, conductivity-temperature-depth probe].*

UAVs have also been used to make water surface elevation (i.e. orthometric water height above mean sea level) and bathymetry observations in lagoons of the Yucatan Peninsula (Mexico) [18]. In Ref. [18], the authors used a DJI hexacopter Spreading Wings S900 UAV equipped with an RGB high-resolution camera (Sony DSC-RX100) and lower-resolution fish-eye lens Eken H9 camera to characterize water surface elevation. The UAV was enabled to control a tethered sonar sensor (Deeper Smart Sonar PRO + Deeper, UAB, Vilnius, Lithuania) able to map the bathymetry of the lagoons. The information thus gathered enabled the estimation of water depth. The authors reported the technology to be accurate and fit for purpose, with errors less than 7 cm for water surface elevation estimation and less than 3.8% of the actual water depth. The study also highlighted the flexibility and low cost of the technology and its capacity to monitor remote areas that are difficult to access by human operators.

Lally et al. [19] reviewed the latest advances in UAV technology (platforms, payload and probe integration) for water sample capture and physico-chemical analysis. The potential of UAVs to gather water samples in lagoons is still unexplored. To date and to the authors' knowledge, only a few examples exist of this application of the technology [19] but none within lagoon environments. Multiple limitations still curtail the uptake of the technology and include water samples are too small to be representative, restrictive drone technology, low rate of sample collection and low reliability [19]. For the technology to be transferable and cost-effective for lagoon characterization, a range of enhancements are required such as increased payload capability, increased battery endurance, beyond visual line of sight operation and real-time physico-chemical measurement [19].
