# Nearshore Benthic Mapping in the Great Lakes:
## 4 Discussion  
The overall goal of this effort was to better understand airborne and satellite data inputs for identifying and classifying nearshore benthic substrate and associated habitat using a machine learning approach that could be easily repeated and applied in other shallow coastal environments. The study builds on foundational efforts to move towards a more integrated data approach, whereby data strengths and limitations for mapping nearshore benthic substrate and associated habitat, expressed through classification accuracy, were evaluated within the context of the CMECS classification hierarchy. The tiered approach allowed for iterative classification into higher levels of CMECS class detail in order to establish a \"point of no return\" or classification limit for data inputs, revealing which datasets are best suited for detecting substrate and biotic features at varying levels of specificity. This is important as we learn how to best piece different types of data together to create a regional picture at project-relevant resolutions, making sure to place them where they fit best and thus, matching end-goals or requirements with suitable data inputs in a strategic way. In that sense, various data inputs can be integrated in a complementary way to address a range of management or restoration needs, which is similar to a multi-sensor approach for restoration monitoring recommended for terrestrial applications suggested in [PERSON] and [PERSON] [53]. Yet, benthic mapping poses additional challenges that makes basin-wide or regional mapping more difficult than in terrestrial applications. One of these challenges is the lack of a consistent classification system for benthic mapping applications, making it difficult to compare results from existing studies, and thus leading to further difficulties with advancing associated technical methods. [PERSON] et al. [22] touches on this issue explaining that there was difficulty comparing their results to others due to the uniqueness of the employed classification systems. Regardless, attempts to compare results in a general sense were made, and for this study, although the system used in [PERSON] et al. [22] had 13 classes combining substrate and biotic features using SVM applied to high-resolution satellite imagery, our overall accuracy was higher (by as much as 15% for substrate and 19% for biotic components), which is not surprising given the fewer number of classes in our study. Other machine learning techniques evaluated in [PERSON] et al. [23] with hyperspectral data fusion using OBIA and resulting in uniquely developed 3- and 9-class habitat maps had overall accuracies that were closer to the ones achieved in our study, 88.5% and 83.25%, respectively, though their KCs tended to be higher compared to our Tier 1 values, averaging 0.75 and 0.72, respectively. Alternatively, [PERSON] et al. [49] applied SVM (among other approaches) to airborne hyperspectral imagery as well as high-resolution satellite imagery in combination with in situ measurements from ship-based transects to identify 3 classes (2 substrate and 1 biotic) in a shallow coastal environment and, similar to our study, found that SVM applied to airborne hyperspectral imagery resulted in the highest overall accuracy; however, their range in overall accuracy using airborne data inputs was greater (73.33-97.36%, averaging 88.69%) and as much as \(\sim\)2% higher than our highest overall accuracy. Despite generalized comparisons, without a consistent, standard classification system, such as in wetland and other habitats, evaluation and establishment of best practices with respect to data collection, processing, and analysis are hampered. The dissimilarity between benthic environments is noted in [PERSON] et al. (2018) and thus, specific or complex classification systems cannot be applied to other areas. Furthermore, [PERSON] et al.