A Spatially Explicit Comparison of Quantitative and Categorical Modelling Approaches for Mapping Seabed Sediments Using Random Forest

Abstract

Seabed sediment composition is an important component of benthic habitat and there are many approaches for producing maps that convey sediment information to marine managers. Random Forest is a popular statistical method for thematic seabed sediment mapping using both categorical and quantitative supervised modelling approaches. This study compares the performance and qualities of these Random Forest approaches to predict the distribution of fine-grained sediments from grab samples as one component of a multi-model map of sediment classes in Frobisher Bay, Nunavut, Canada. The second component predicts the presence of coarse substrates from underwater video. Spatial and non-spatial cross-validations were conducted to evaluate the performance of categorical and quantitative Random Forest models and maps were compared to determine differences in predictions. While both approaches seemed highly accurate, the non-spatial cross-validation suggested greater accuracy using the categorical approach. Using a spatial cross-validation, there was little difference between approaches—both showed poor extrapolative performance. Spatial cross-validation methods also suggested evidence of overfitting in the coarse sediment model caused by the spatial dependence of transect samples. The quantitative modelling approach was able to predict rare and unsampled sediment classes but the flexibility of probabilistic predictions from the categorical approach allowed for tuning to maximize extrapolative performance. Results demonstrate that the apparent accuracies of these models failed to convey important differences between map predictions and that spatially explicit evaluation strategies may be necessary for evaluating extrapolative performance. Differentiating extrapolative from interpolative prediction can aid in selecting appropriate modelling methods.

Harmonizing Multi-Source Sonar Backscatter Datasets for Seabed Mapping Using Bulk Shift Approaches

“…In a geological or biological mapping context, independent analysis of the datasets and post hoc combination of results is an effective solution when the number of backscatter datasets is relatively low, and each has been adequately ground-truthed [26], but this becomes problematic when ground-truth locations are unevenly or sparsely distributed over the backscatter layers. Multi-source backscatter harmonization can address some of these issues, but it has previously been performed ad hoc (e.g., [9,25,28]). Here, we present the first investigation, to our knowledge, into standardized and repeatable methods for multi-source backscatter harmonization for seabed mapping.…”

“…Additionally, MBES record measurements on acoustic reflectivity, referred to as "backscatter", which can be used to derive information regarding the nature of the seafloor [3], such as volume heterogeneity (e.g., sediment grain size, distribution, and geological layering) and interface characteristics (e.g., substrate, roughness, bedforms; [4,5]). Therefore, the MBES signal provides both qualitative and quantitative information on seafloor environmental characteristics, and it has been commonly used to delineate and map surficial geology (e.g., [6][7][8][9]). As an extension of this application, relationships between benthic species and physical seafloor characteristics have allowed backscatter to be utilized as a tool for describing benthic habitat.…”

“…The overlap between surveys provides a common area at which to compare backscatter intensities and derive statistical relationships between datasets. Predictive surficial geology and habitat modelling generally rely on continuous-coverage environmental data layers to produce map products, and a single harmonized backscatter mosaic is therefore often desirable (e.g., [9,28]). An uneven distribution of ground truth over separate backscatter datasets inhibits the production of full-coverage habitat maps and enhances uncertainty in the map products [26].…”

Limitations of Predicting Substrate Classes on a Sedimentary Complex but Morphologically Simple Seabed

“…In this exercise scientists were tasked with creating a thematic map for the site. However, considering the limited success, an alternate approach may have been to generate predictions as class-specific probability maps or quantitative sediment composition maps [31,93,94]. This could be achieved by generating maps in two stages.…”

Nearshore Sandbar Classification of Sabaudia (Italy) with LiDAR Data: The FHyL Approach

“…The bathymetric position index (BPI), representing the marine version of the Topographic Position Index (TPI) introduced by [58], has been applied to several sedimentary [59], geomorphological [60] and seafloor habitat mapping [61] studies. The BPI has been recently used for benthic habitat mapping studies [62][63][64][65][66].…”