# Nearshore Benthic Mapping in the Great Lakes:
## 1 Introduction
For example, integrated airborne sensor suites such as the U.S. Army Corps of Engineers' (USACE) Coastal Zone Mapping and Imaging Lidar (CZMIL) System, operated under the National Coastal Mapping Program (NCMP) by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), routinely collects high-resolution imagery (<1 m) and topobathymetric lidar for coastal areas of the U.S., including the Great Lakes [9]. Additionally, moderate- and high-resolution satellite multispectral sensors, such as the European Space Agency's Sentinel-2, offers increased spatial coverage at relatively high spatial (10-20 m) and spectral resolutions that help to overcome cloud cover limitations and increased data availability over coastal areas with theadded benefit of broad, public access [10]. Other examples of higher-resolution commercial satellite multispectral sensors that can be used for coastal mapping applications include Maxar WorldView-2 and -3 (~1.2-1.8 m), Airbus Pleiades, and Planet SuperDove satellite constellations [11]. Together, these data help to address the spatial data coverage and resolution gaps; yet, challenges with water column optical properties can confound their use in detailed substrate and habitat mapping [7; 12]. The second major advancement in the use of remote sensing for benthic mapping is the enhancement of system hardware and software, leading to improved analytical methods for extracting sea- or lake-bed characteristics. The primary limitations for discriminating different benthic types from imagery is due to the following: spectral heterogeneity of the bottom, depth and water column properties and associated absorption and scattering of light, and the radiometric capabilities of the sensor [7; 12; 13]. For those reasons, the ability to distinguish benthic types varies across aquatic systems, depths, and water conditions. [PERSON] et al. [7] provides an extensive review of the evolution of remote sensing technology to map shallow water systems (benthic habitat and bathymetry <20 m in depth). They explain that to help overcome the effect of the water column and obtain information about benthic characteristics, there are empirical, semi-analytical, and analytical processing techniques that can be employed [12]. In general, empirical methods can be site-specific and require appropriately-timed, in situ measurements in the form of ground truth, while more recent analytical, or physics-based inversion, modeling techniques may be less in situ data-dependent but are more computationally complex and reliant upon atmospheric correction of the imagery [7; 14]. Inversion-optimization and non-linear optimization techniques established by [PERSON] et al. [15] and [PERSON] et al. [16] continue to be refined and improved [17; 18] to help separate benthic target signals from the interference with and optical properties of the water column, especially in complex coastal and inland waterbodies. Even more numerous than the approaches to overcome water column complexities are the methods with which to identify benthic substrate and habitats from input data (imagery, lidar depth, in situ samples, etc.). The classification of input data can either be unsupervised (no training or ground truth samples) or supervised (manually or semi-automated input of training or ground truth samples). Such methods are often used in remote sensing and can also be directly integrated with empirical, semi-analytical, and analytical approaches for bathymetry and benthic characterization. In general, there are a wide variety of supervised classification techniques used to characterize benthic features, such as traditional pixel-based classifiers like Maximum Likelihood [19; 20], machine learning algorithms like Support Vector Machine or Random Forest [21; 22], Object-Based Image Analysis (OBIA), a knowledge-based classification approach [23; 24], and ensemble classification approaches [23; 25; 26].