# Nearshore Benthic Mapping in the Great Lakes:
## 1 Introduction
Often classification techniques make use of multiple data inputs, fusing various passive (electro-optical imagery) and active (lidar) to help increase map accuracy and detection capabilities [8; 27]. All of these techniques have had varying degrees of success and accuracy for mapping benthic substrate and habitat with most applications related to mapping the distribution of coral reef, SAV, and macroalgae [7; 12]. In the Great Lakes specifically, the use of passive and active remote sensing data for nearshore benthic mapping applications have focused on such topics as monitoring the movement of legacy mine tailings and impacts to local fisheries [28], habitat monitoring around National Lakeshores [29; 30], marine sanctuary planning [31], and SAV mapping, such as nuisance algae _Cladophora_[32]. While no one data type and analytical approach works well in all areas of the Great Lakes, the convergence of increasing amounts of remotely sensed data with improvements to methods is opening opportunities to develop more frequent large-scale nearshore benthic mapping at project-relevant spatial resolutions. To that end, the current trend is moving towards integrating various available data, maximizing their individual strengths based on location and environmental condition, as well as establishing semi-automated or automated analytical methods that can be consistently applied for more comparable and repeatable map results [22]. While the studies referenced thus far have shown success in that direction, the goal of basin-wide nearshore benthic mapping is still considered premature, with data and technical approaches requiring refinement and vetting by the user community (researchers, coastal managers, etc.). In particular, the need to understand the character of the nearshore benthic environment in the Great Lakes rises from management topics related to recreation, sport fishing, shoreline erosion, beach maintenance, infrastructure improvements, ecosystem restoration, and beneficial re-use of dredged material. Decisions made without adequate inventory or characterization of coastal habitats (terrestrial and benthic), information about erodibility of the lake-bed substrate, or knowledge of the interplay between the shoreline and the nearshore benthic environment under different lake levels and weather scenarios, can negatively impact coastal habitat, shoreline change rates, lake-bed down cutting, and longshore sediment transport regimes. Decision making is further confounded by a multitude of private, local, state, and federal jurisdictions. Methods that enhance or facilitate lake-bed classification, in a consistent manner, and across the Great Lakes are therefore needed to facilitate cross-jurisdiction collaboration and problem solving. As such, the goals of this study are to build on existing approaches to:  
1. Evaluate the integration and role of high-fidelity data (airborne imagery and lidar, satellite imagery, in situ observations, etc.) using machine learning (Support Vector Machine) to identify nearshore benthic substrate and associated habitat and to better understand their implications with respect to the Coastal and Marine Ecological Classification Standard (CMECS) classification system hierarchy;
2. Develop a semi-automated, repeatable approach that could be applied in other shallow coastal environments using a case study in southwest Lake Michigan, USA.