Present day ocean observation still builds on experiences from experiments such as AOSN-I/-II, where dominant practices have gradually shifted from ship-based ocean sampling to remote and robotic sensing using satellites, drifting floats, numerical ocean modeling, and autonomous underwater vehicles (AUVs). Oceanographic sensing prac-tices have transformed from platform-based schemes towards networks of heterogeneous sensor nodes (Curtin et al., 1993)(see Fig. 1.2), measuring and sharing information across a range of different scales; this has prompted the need for effective allocation and coordination of these resources. Access to synoptic marine data sources such as ocean models and remote sensing can augment this process. However, the accuracy of ocean models is not at a level capable of making accurate predictions of these processes at scales sufficient for definite representation (Lermusiaux, 2006), and typical weather conditions make the use of remote sensing limited. As we will see, these information sources must therefore be used side-by-side with statistical tools and models, both to extract the best
1.2. Adaptive Sampling of the Water Column
possible information from the available data and provide the ability to generalize, model, and learn on the basis of prior and current data. The coastal ocean and the upper water column are domains where the need for more sophisticated robotic sampling approaches is critical. Being highly relevant to marine life and bio diversity, the study of the processes in the water column has broad ecological, scientific, and social-economical significance.
Coastal influences from bathymetry, river discharge, land run-off, and complex oceanic circulation also cause additional heterogeneity and spatio-temporal variation that can only be adequately studied using in-situ assets. Determining what resources to deploy and when and where to sample, is therefore an essential question facing scientists in this domain. The fundamental non-deterministic nature of the ocean makes inference about sampling a task that cannot be fully determined prior to data collection –ex-situ.
Regular grid (“lawnmower”) or single-line surveys are still in use, but as ocean conditions imply both incomplete information and high uncertainty, such strategies often result in sub-optimal survey designs. These approaches are gradually being substituted by more effective sampling designs that can exploit background information, and adapt to changing conditions and observations (Frolov et al., 2014).
The potential for utilizing adaptive sampling approaches in the water column is significant. To illustrate this more clearly, examine Fig. 1.3a, which depicts a survey of a biomass layer distributed in the water column with an AUV. Let’s imagine that we want to measure a property inherent to this biomass that implies placing the AUV within this layer. Starting with a non-adaptive strategy, a natural approach would be to base the survey depth on an average value calculated from prior data. Since this depth is dependent on conditions that may or may not be true for the current survey, the possibility of placing the AUV inside the biomass layer is low. Fig. 1.3a shows such a case, where a non-adaptive sampling plan (in red) ends up not measuring the biomass at all, except for a small region. Given the data from this survey, the conclusion might be that “there are little or no significant biomass in this area”. In contrast, let’s now imagine we use an adaptive strategy, capable of including information such as, “you are outside the patch→change the depth”; or rather, “you are inside the patch→keep the current depth”. Incorporation of this type of information can lead to sampling plans such as the blue line in Fig. 1.3a, clearly superior in terms of placing the AUV at a more beneficial depth, on which a more correct conclusion about the biomass distribution could be drawn. This illustrates what can be achieved by exploiting information gathered in the field, as well as how different conclusions relate to sampling bias.
Adaptive sampling schemes also enable opportunistic behavior. Fig. 1.3b shows this aspect by presenting a case where two different distributions (i.e. yellow, green) are of interest. The AUV can in this context, if one or the other were to appear, choose to map either one of the distributions. Alt. A may be a good idea, as the AUV already has data from the other distribution, or if the previous distribution has more relevance, Alt. B may be the best option. Despite being a conceptual example, Fig. 1.3a and 1.3b clearly highlight the advantages of adaptation.
But there are some challenges. For the upper water column the bio-physical processes (interaction between biological parameters and physical forces in the ocean) being studied are often not directly measurable (e.g. phytoplankton via Chlorophylla, photosynthesis via
Adaptive
biomass Non-adaptive
(a) Adaptive vs. non-adaptive sampling.
Alt. A Opportunity
Alt. B
(b) Opportunistic behavior.
AUV speed > process speed
(c) Temporal constraints must be addressed to avoid undersampling.
sampling resolution high res.
low res.
(d) Sampling resolution should be high enough to resolve the process characteristics.
Figure 1.3: Adaptive (data-driven) sampling of the upper water column showing the poten-tial of using adaptive sampling and the associated spatio-temporal considerations, i.e. the influence of relative speed and spatial resolution on sampling efficiency and perception.
fluorometery, etc.), as sensors often only observe proxy variables, which means indirect in-formation must be used to decide how to proceed with future sampling. Processes can also be highly dynamic, interacting on multiple scales, and even be episodic; this makes obser-vation and mapping a considerable challenge. The sensor-carrying platform can itself try to quantify this and learn the distributional characteristics, but this can be both difficult and time consuming. Accordingly, it is important to identify the correct spatial and temporal scales at which to adequately sample the process of interest. Usually for water column sur-veys the platform is following a yoyo (sawtooth) pattern, as the vertical direction is more heterogeneous (i.e., contains more variability), compared to the horizontal. The main rea-son for this is stratification from gravitational pull, leading to a “layered” structure in the water column, with increased horizontal correlation. Although this undulating pattern is more efficient at resolving details in the water column, the speed over ground is reduced