**1. Introduction**

Lagoons are shallow bodies of water separated from larger bodies of water by barrier reefs, coral reefs, sandbars or other natural barriers such as shingle or rocks (**Figure 1**). Monitoring of lagoons is a regulatory requirement in Europe under the Water Framework Directive [1]. These requirements need to be interpreted alongside those of other directives such as the Nitrates Directive, Habitats Directive and the Marine Strategy Framework Directive and the EU strategy on adaptation to climate change [2, 3]. Implementation of these regulatory requirements has increased the focus on characterizing lagoon environments and in developing periodic and routine monitoring programmes (e.g. [4]), with government across the European Union having to reconsider their approach to lagoon monitoring.

#### **Figure 1.**

*Schematic diagram depicting different types of lagoon environments. (1) sandbar coastal lagoon; (2) river delta and tidal lagoon; (3) coastal coral reef lagoon; (4) archipelago's lagoon; (5) atoll coral reef lagoon. Modified from IAN image library, Tracey Saxby (ian.umces.edu/imagelibrary/).*

For example, Scotland's common standards for saline lagoon habitat monitoring were abandoned in 2008 as they were not considered to be fit for purpose and were not in accordance with these new regulatory requirements [3]. The development of periodic and routine monitoring programmes has required consideration of how to increase the spatial and temporal understanding of lagoon environments and has resulted in increased spatio-temporal coverage, resolution, larger data sets and more sophisticated data analysis approaches [3, 5].

The range of parameters that potentially could be monitored is wide and varied [4, 6]. **Table 1** summarizes the key parameters that are typically monitored to characterize lagoon environments [1]. These include biological, physico-chemical and hydromorphological parameters. Traditional monitoring methods rely on visual observation or direct manual measurements of these key parameters [1]. In general, such methods are highly time-consuming and costly. They can also require destructive sampling and are therefore limited in the spatial extent within which they can be implemented.

Remote sensing techniques based on satellite imagery have been used to overcome some of these limitations (e.g. [7–9]). Satellite imagery enables the monitoring of large extents. However, the resolution provided by satellite imagery is, in many cases, not sufficient to characterize a lagoon environment to the required level of detail. Information derived from satellite imagery cannot be used for physical measurements of water quality and does not enable characterization of the sub-surface properties of lagoons in the deepest areas.

Recent technological advances within the area of robotics, autonomous systems and machine learning have been identified as potential solutions to overcome the limitations mentioned above. Both robots and autonomous systems have been identified by the UK government as one of the eight great technologies [10] where the UK will be global leaders. Robots and autonomous systems that are able to monitor the environment independently of human control could revolutionize lagoon monitoring in the next decades. Such technologies have already been used in a diverse range of environments, with some authors reporting some applications in lagoons [11]. Both robots and autonomous systems require bespoke algorithms that enable them to carry out their tasks, from path planning during autonomous navigation to the analysis of the data collected. Machine learning methods enable the development and implementation of such algorithms. Machine learning techniques have already been successfully used in multiple environments to detect fish species automatically from imagery collected with underwater cameras [12] and to predict trophic status indicators in coastal lagoons [13].

**145**

**Table 1.**

*Autonomous Systems for the Environmental Characterization of Lagoons*

Biological Phytoplankton Changes in phytoplankton composition indicate changes in

[14, 15]

Other aquatic flora This includes floating (emergent) and submerged plants.

Habitat Habitat characterization focuses on the quality and

indexes are key parameters Fish Fish community composition (diversity and structure),

anthropogenic impact

evaporation processes pH An indicator of acidification and algal activity

Temperature Temperature measurements provide information about

Oxygen Oxygenation levels in lagoons are an indication of primary

Hydrology Hydrological characterization focuses on quantifying

Morphology Quantity, structure and substrate of the bed, depth

[16] and detailed bathymetry

Physico-chemical Salinity Salinity patterns provide information about the vertical

Hydromorphological Tidal range The tidal range is the difference in water level between

*Key parameters used for lagoon characterization based on the water framework directive [1].*

ingress-egress

the dynamics of the lagoon. Changes in nutrients, salinity or environmental stressors have an impact on the primary production. Key metrics look at the presence of harmful algal species, species configuration of assemblages, phytoplankton variation over time, growth and biomass

The key parameters used to describe other aquatic flora include community structure, taxonomic composition, abundance, coverage, diversity and species richness

diversity of the habitat present within the lagoon and surrounding areas. Key metrics include species composition, species coverage gain/loss, habitat alteration,

Abundance and diversity of macro-invertebrates are ecological indicators of water-level fluctuations and human pressures. Taxonomic composition, abundance, species richness, community structure and diversity

abundance and seasonality are the key parameters used to characterize fish communities in lagoons. Changes in these parameters are indicators of environmental change and

and horizontal stratification of water in the lagoon, tidal patterns and the rate of saline and fresh water

the temporal and spatial variation patterns in the lagoon and the occurrence of thermoclines. It also provides information about the influence of insolation and

production and general organic matter consumption

existing hydrological processes within lagoons. These include evaporation, insolation, internal circulation (saline and freshwater ingress-egress, groundwater), groundwater

variation and continuity and structure of the intertidal zone are key morphological parameters. More detailed characterizations look at the properties of the barrier, backbarrier stratigraphy, absence/presence of tidal inlet

input and mixing processes, amongst others

high tide and low tide. The tidal range is an indicator of the likely patterns of saline and fresh water ingress-egress

complexity, patchiness and stabilization [14]

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

Macroinvertebrates

**Parameter Description**


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

*Lagoon Environments around the World - A Scientific Perspective*

more sophisticated data analysis approaches [3, 5].

*Modified from IAN image library, Tracey Saxby (ian.umces.edu/imagelibrary/).*

the sub-surface properties of lagoons in the deepest areas.

trophic status indicators in coastal lagoons [13].

they can be implemented.

**Figure 1.**

For example, Scotland's common standards for saline lagoon habitat monitoring were abandoned in 2008 as they were not considered to be fit for purpose and were not in accordance with these new regulatory requirements [3]. The development of periodic and routine monitoring programmes has required consideration of how to increase the spatial and temporal understanding of lagoon environments and has resulted in increased spatio-temporal coverage, resolution, larger data sets and

*Schematic diagram depicting different types of lagoon environments. (1) sandbar coastal lagoon; (2) river delta and tidal lagoon; (3) coastal coral reef lagoon; (4) archipelago's lagoon; (5) atoll coral reef lagoon.* 

The range of parameters that potentially could be monitored is wide and varied

[4, 6]. **Table 1** summarizes the key parameters that are typically monitored to characterize lagoon environments [1]. These include biological, physico-chemical and hydromorphological parameters. Traditional monitoring methods rely on visual observation or direct manual measurements of these key parameters [1]. In general, such methods are highly time-consuming and costly. They can also require destructive sampling and are therefore limited in the spatial extent within which

Remote sensing techniques based on satellite imagery have been used to overcome some of these limitations (e.g. [7–9]). Satellite imagery enables the monitoring of large extents. However, the resolution provided by satellite imagery is, in many cases, not sufficient to characterize a lagoon environment to the required level of detail. Information derived from satellite imagery cannot be used for physical measurements of water quality and does not enable characterization of

Recent technological advances within the area of robotics, autonomous systems and machine learning have been identified as potential solutions to overcome the limitations mentioned above. Both robots and autonomous systems have been identified by the UK government as one of the eight great technologies [10] where the UK will be global leaders. Robots and autonomous systems that are able to monitor the environment independently of human control could revolutionize lagoon monitoring in the next decades. Such technologies have already been used in a diverse range of environments, with some authors reporting some applications in lagoons [11]. Both robots and autonomous systems require bespoke algorithms that enable them to carry out their tasks, from path planning during autonomous navigation to the analysis of the data collected. Machine learning methods enable the development and implementation of such algorithms. Machine learning techniques have already been successfully used in multiple environments to detect fish species automatically from imagery collected with underwater cameras [12] and to predict

**144**

**Table 1.**

*Key parameters used for lagoon characterization based on the water framework directive [1].*

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 practice.
