# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Biological Features  
Phytoplankton biomass, as the foundation of the aquatic food pyramid, is a classic example of a biological feature that has been targeted in oceanographic research using an AUV. It plays an important role in marine ecology and the ocean carbon cycle. Driving rapid CO\({}_{2}\) sequestration, it generates harmful conditions for other organisms in the form of harmful algal bloom [24]. This biological feature is distinctive from other physical or chemical features (hydrothermal vents or oil plumes) in that it does not have a single source location to feed the bloom. Rather, it is driven by the essential ingredients for survival or growth, such as the amount of nutrients or light intensity. There is a zone where a good balance between light and nutrients provides a favourable habitat, such as in a thermocline region [25]. This can be the key proxy to search for a phytoplankton bloom. These blooms can be attributed to upwelling leading to nutrient flux [26], propagating internal waves by pressure and density gradients [27] and anthropogenic events [28; 29]. There are various ways of using in-situ measurements to detect and classify certain types of algae. Table 5 lists the sensors for detecting biological blooms reported in [7]. In some case studies, the correlations between bio-signature and physical parameters, such as temperature, are also sought [30]. Different efforts have been be made to find and determine the presence of marine microorganisms by either directly using a fluorescence sensor or indirectly chasing a physical parameter or seeking their breeding ground. [PERSON] et al. [31] extensively reviewed and synthesised detection methodologies for harmful algal blooms. An adaptive triggering method [4] was developed in order to capture the most desirable water samples within a thin phytoplankton layer using the Dorado AUV equipped with 10 water samplers, named Gulpers. Their triggering method was demonstrated during a 2009 AUV mission in Monterey Bay, CA, USA. Adopting a vertical yo-yo transecting the plankton layer, their algorithm consists of a pair of crossings for detection and triggering, respectively. During the first complete crossing, the peak value is measured and saved as a threshold. In the subsequent crossing, the Gulper is ready to be triggered as soon as the saved peak (threshold) is met. The assumption underlying this is that those \begin{table}
\begin{tabular}{c c} \hline
**Detection Method for Marine Microorganisms** & **References** \\ \hline Nutrient monitors & [32] \\ Antibody probes & [33] \\ Flow cytometry & [34; 35; 36] \\ Chlorophyll in vivo fluorescence & [37] \\ Nucleotide probes & [38] \\ Quantitative polymerase chain reaction (PCR) & [39; 40] \\ Microarray chip technology & [41; 42] \\ Electrospray ionisation mass spectrometry & [43; 44] \\ \hline \end{tabular}
\end{table} 
Table 5: In-situ sensors to detect microorganisms in biological blooms. two penetrated points have a similar peak value. This approach was further developed and applied for mapping a subsurface oil plume, which will be discussed in Section 3.3. [PERSON] et al. [45] devised a maximum detection method using a pinwheel survey pattern in the horizontal plane to track the centre of a phytoplankton plume. In the vertical plane, the vehicle was programmed to perform yo-yo manoeuvring, drawing a sawtooth profile through the water column in each swath. This method is particularly effective to track a moving phytoplankton plume. [PERSON] et al. [24] developed a detection and advection model to predict the occurrence and transport of algal bloom patches. By detecting the intense biogeochemical hotspots, they ultimately aimed to select the most desirable trajectory for AUV sampling. Near real-time data in terms of sea state (temperature, salinity and surface height) was acquired from the sensors of the Southern California Coastal Ocean Observing System (SCCOOS) and the Central and Northern California Ocean Observing System (CeNCOOS) to explain the physical characteristics of a detected bloom.