Quantifying the effects of variable selection, spatial scale and spatial data quality in marine benthic habitat mapping

Mapping benthic habitats has become critical in many contexts like conservation and management. While marine habitat mapping methods strongly rely on tools and methods from geography and geomatics, habitat mapping practitioners with a background outside of these specialized areas do not always have a full understanding of the spatial concepts behind these tools and methods. This phenomenon is amplified when marine geomorphometry, the science used to quantify seafloor terrain characteristics, is integrated into the marine habitat mapping workflow. This dissertation reviews the use of spatial concepts in the field of marine benthic habitat mapping; many concepts are poorly understood or poorly implemented in the habitat mapping workflow, among which spatial scale and spatial data quality stand out as being of particular importance. While geomorphometry is commonly used in marine benthic habitat mapping, no framework existed to test which terrain attributes should be used as surrogates of species distribution, leading to an inability to compare results from different studies. This dissertation explores different options for terrain attribute selection and proposes an optimal combination that can be used as standard in all habitat mapping studies. This selection is then tested using two approaches to benthic habitat mapping and is shown to perform better than others. Bathymetric data, the primary input for marine geomorphometry analyses and one of the main data inputs for habitat mapping, are commonly impacted by data acquisition artefacts. Very little work has been done on trying to understand how these artefacts propagate throughout the habitat mapping workflow. The impact of artefacts on the bathymetry and its derived terrain attributes is described, and it is shown that artefacts modify the spatial and statistical distributions of depth and terrain attribute values. However, when these affected data are used in habitat mapping, their impact is not always predictable. Some artefacts were found to sometimes inflate measures of accuracy and performance and sometimes decrease them. Overall, habitat maps were shown to be very sensitive to the effects of variable selection, spatial scale and data quality, and as such have serious implications when they are used to inform decision-making, for instance in marine conservation and management. This dissertation raises awareness about these issues and highlights the need for careful integration of spatial data in habitat mapping practices

A review of marine geomorphometry, the quantitative study of the seafloor

Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acousticbased mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.peer-reviewe

Optimizing the scale of observation for intertidal habitat classification through multiscale analysis

Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications

Correction: Espriella, M.C.; Lecours, V. Optimizing the scale of observation for intertidal habitat classification through multiscale analysis. Drones 2022, 6, 140

In the original publication [1], there was a mistake in Figure 10 as published. The x-axis is mislabeled. The data and labels are mismatched. The corrected Figure 10 appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated

Characterising the ocean frontier : a review of marine geomorphometry

Geomorphometry, the science that quantitatively describes terrains, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using Geographic Information Systems (GIS) has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade, a suite of geomorphometric techniques have been applied (e.g. terrain attributes, feature extraction, automated classification) to investigate the characterisation of seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is nevertheless much common ground between terrestrial and marine geomorphology applications and it is important that, in developing the science and application of marine geomorphometry, we build on the lessons learned from terrestrial studies. We note, however, that not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four- dimensional nature of the marine environment causes its own issues, boosting the need for a dedicated scientific effort in marine geomorphometry. This contribution offers the first comprehensive review of marine geomorphometry to date. It addresses all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry.peer-reviewe

On the Art of Classification in Spatial Ecology: Fuzziness as an Alternative for Mapping Uncertainty

Classifications may be defined as the result of the process by which similar objects are recognized and categorized through the separation of elements of a system into groups of response (Everitt et al., 2011). This is done by submitting variables to a classifier, that first quantifies the similarity between samples according to a set of criteria and then regroups (or classifies) samples in order to maximize within-group similarity and minimize between-group similarity (Everitt et al., 2011). Classifications have become critical in many disciplines. In spatial ecology, for example, grouping locations with similar features may help the detection of areas driven by the same ecological processes and occupied by same species (Fortin and Dale, 2005; Elith et al., 2006), which can support conservation actions. In fact, classifications have been used with the aim of investigating the spatial distribution of target categories such as habitats (Coggan and Diesing, 2011), ecoregions (Fendereski et al., 2014), sediment classes (Hass et al., 2017), or biotopes (Schiele et al., 2015). Sometimes such classifications were found to act as surrogates for biodiversity in data-poor regions (e.g., Lucieer and Lucieer, 2009; Huang et al., 2012), some class being known for supporting higher biodiversity. Many of the traditional classification methods were developed in order to reduce system complexity (Fortin and Dale, 2005) by imposing discrete boundaries between elements of a system; it is easier for the human mind to simplify complex systems by identifying discrete patterns (Eysenck and Keane, 2010), and grouping similar elements together (Everitt et al., 2011). However, in natural environments, spatial and temporal transitions between elements of a system are often gradual (e.g., an intertidal flat transitioning from land to sea) (Farina, 2010). Those transitions may display distinct properties from those of the two elements they separate. Despite the particularities and importance of such transitions, they are often disregarded in ecological research (Foody, 2002), leading to the adoption of approaches that, by defining sharp boundaries, may fail to appropriately describe natural patterns and groups of a system. Such approaches have become the norm, despite the existence of approaches such as, fuzzy logic (Zadeh, 1965) and machine learning (Kuhn and Johnson, 2013) that are able to offer a more representative description of those natural transitional zones. In ecology for instance, machine learning approaces have gained some traction because of their ability to predict classes distribution (area-wide) in data-poor conditions (e.g., sparse punctual information) with a relative high performance and with no particular assumption in building the relationship between targetted classes and physical parameters (e.g., Barry and Elith, 2006; Brown et al., 2011; Fernández-Delgado et al., 2014). In the present contribution, we aim at highlighting the limitations associated with classification techniques that are based on Boolean logic (i.e., true/false) and that impose discrete boundaries to systems. We propose to shift practices toward techniques that learn from the system under study by adopting soft classification to support uncertainty evaluation

An ocean of possibilities : applications and challenges of marine geomorphometry

An increase in the use of geomorphometry in the marine environment has occurred in the last decade. This has been fueled by a dramatic increase in digital bathymetric data, which have become widely available as digital terrain models (DTM) at a variety of spatial resolutions. Despite many similarities, the nature of the input DTM is slightly different than terrestrial DTM. This gives rise to different sources of uncertainties in bathymetric data from various sources that will have particular implications for geomorphometric analysis. With this contribution, we aim to raise awareness of applications and challenges of marine geomorphometry.peer-reviewe

Remote sensing techniques for automated marine mammals detection : a review of methods and current challenges

Marine mammals are under pressure from multiple threats, such as global climate change, bycatch, and vessel collisions. In this context, more frequent and spatially extensive surveys for abundance and distribution studies are necessary to inform conservation efforts. Marine mammal surveys have been performed visually from land, ships, and aircraft. These methods can be costly, logistically challenging in remote locations, dangerous to researchers, and disturbing to the animals. The growing use of imagery from satellite and unoccupied aerial systems (UAS) can help address some of these challenges, complementing crewed surveys and allowing for more frequent and evenly distributed surveys, especially for remote locations. However, manual counts in satellite and UAS imagery remain time and labor intensive, but the automation of image analyses offers promising solutions. Here, we reviewed the literature for automated methods applied to detect marine mammals in satellite and UAS imagery. The performance of studies is quantitatively compared with metrics that evaluate false positives and false negatives from automated detection against manual counts of animals, which allows for a better assessment of the impact of miscounts in conservation contexts. In general, methods that relied solely on statistical differences in the spectral responses of animals and their surroundings performed worse than studies that used convolutional neural networks (CNN). Despite mixed results, CNN showed promise, and its use and evaluation should continue. Overall, while automation can reduce time and labor, more research is needed to improve the accuracy of automated counts. With the current state of knowledge, it is best to use semi-automated approaches that involve user revision of the output. These approaches currently enable the best tradeoff between time effort and detection accuracy. Based on our analysis, we identified thermal infrared UAS imagery as a future research avenue for marine mammal detection and also recommend the further exploration of object-based image analysis (OBIA). Our analysis also showed that past studies have focused on the automated detection of baleen whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar bears, sirenians, and mustelids

A multiscale approach to mapping seabed sediments

Benthic habitat maps, including maps of seabed sediments, have become critical spatialdecision support tools for marine ecological management and conservation. Despite the increasing recognition that environmental variables should be considered at multiple spatial scales, variables used in habitat mapping are often implemented at a single scale. The objective of this study was to evaluate the potential for using environmental variables at multiple scales for modelling and mapping seabed sediments. Sixteen environmental variables were derived from multibeam echosounder data collected near Qikiqtarjuaq, Nunavut, Canada at eight spatial scales ranging from 5 to 275 m, and were tested as predictor variables for modelling seabed sediment distributions. Using grain size data obtained from grab samples, we tested which scales of each predictor variable contributed most to sediment models. Results showed that the default scale was often not the best. Out of 129 potential scale dependent variables, 11 were selected to model the additive log-ratio of mud and sand at five different scales, and 15 were selected to model the additive log-ratio of gravel and sand, also at five different scales. Boosted Regression Tree models that explained between 46.4 and 56.3% of statistical deviance produced multiscale predictions of mud, sand, and gravel that were correlated with cross-validated test data (Spearman's ρmud = 0.77, ρsand = 0.71, ρgravel = 0.58). Predictions of individual size fractions were classified to produce a map of seabed sediments that is useful for marine spatial planning. Based on the scale-dependence of variables in this study, we concluded that spatial scale consideration is at least as important as variable selection in seabed mapping