*4.1. Methodological Framework: Standardization and Adaptability*

In remote sensing, Earth observation satellite data has become readily available, and users often face confusion when trying to determine which satellites to use to produce the best results for their given application [100]. In this paper, we highlighted some of the most well-known sensors for mapping floating kelp forests, such as the Landsat, SPOT and Sentinel-2 satellites (e.g., [19,46,62,65]), while also presenting some new cost-effective high-resolution options such as imagery from RapidEye, Worldview and PlanetScope satellites, which add valuable data into the kelp mapping field with their coverage since 2008, 2009 and 2018, respectively. In addition to the choice of satellites, having a specific set of criteria when choosing what images to use is crucial in minimizing the time and cost associated with building a time series (Table 6) [38,100,101]. We demonstrated one possible set of criteria that can be used to minimize errors associated with environmental conditions and the timing of satellite imagery acquisition. Of note, the criterion should always be selected based on the specific areas of interest. In particular, other factors that could lead to the erroneous classification of floating kelp forests in satellite imagery are land adjacency effects, high currents independent from tides, water turbidity and the presence of algal blooms [47]. Our analysis did not consider these because they were largely absent in our study region. However, in our case, clouds and higher amplitude tides often limited the availability of good quality images. As such, some mid- to high-tide images (3.0–5.0 m above chart datum) were included, based on a visual assessment of the imagery. A loss of floating canopy area up to 42% when comparing a 2.0 m tidal height difference was found on the Central Coast of BC [46]. In California, where *Macrocystis* kelp forms large offshore forests similar to those found within the study area, increases in tidal height of 1 m in UAV imagery reduced the floating canopy area from 15% to 30%, but was site dependent [102]. However, little to no difference was detected in Landsat satellite-derived kelp biomass measurements across a 2.0 m tidal difference, likely related to the coarse resolution [19,50]. Consequentially, researchers should be aware of the impact that tides can have on detection when determining their tide criterion and using this framework for time series analyses. The impact of tides can be site, species, density and kelp forest size dependent [102,103]. Upon visual comparison we found no major difference between tidal heights used in this study; however, more analyses are needed to understand and quantify the impact of tides in this region.

Once a good quality imagery database is created, users are faced with many inconsistent and complex approaches to correct systematic errors, such as atmospheric attenuation and geometric distortions in imagery [25,104]. In order to keep the workflow streamlined and easy to use, we propose a simple geometric and atmospheric correction method that can be applied to imagery from various sensors (Table 6). For georectification, we found that a simple first-order polynomial shift, which considers systematic and random distortions in images [44,67,105], properly addressed any geometric distortions present. There are numerous atmospheric correction methods that range from simple techniques like the Rayleigh correction method [63] to the more complex algorithms that need supplemental data, including atmospheric models and, ideally, in situ measurements [106,107]. Other researchers, for instance, have effectively used models such as the Fast line-ofsight Atmospheric Analysis of Hypercubes (FLAASH) [35] and the Atmospheric and Topographic Correction (ATCOR) [46]. Nonetheless, these methods can often under- or overcorrect values when parameters are not adequately chosen, making it challenging for non-remote sensing experts to use, and problematic when applied over large bodies of water [108–110]. When possible, imagery should be downloaded as already corrected products, such as in [52,53,62,65]. For example, the United States Geological Survey (USGS) provides Landsat Analysis Ready Data (ARD) products [111] and the Planet provides Surface Reflectance (SR) products [112]. However, when these products are not available, adhering to a simple method that only requires within-image information is recommended to prevent errors related to inconsistent methods or data input. We found that the adopted Rayleigh correction method resulted in similar floating kelp and water spectra as those from the literature [25,44,62]. More importantly, the shape of the floating kelp and water spectra from the corrected images were akin to those of the atmospherically corrected products.

Numerous band ratios and vegetative indices have been used to enhance floating kelp forests in satellite imagery [25]. Most notably, NDVI, which was initially used to detect healthy land vegetation [113], has been co-opted for floating kelp forests [19,25,46,48,50,52,53,63,114]. Based on the literature, NDVI has been effectively applied to Sentinel-2, Landsat and SPOT satellite imagery to differentiate kelp from other classes. Alternatively, based on M-statistic analysis, and similar to [25], we found that the NDVI with the green band (G-NDVI) instead of the red band performed better, most likely because less noise was visible in the green band than in the red band. Additionally, indices that included the red-edge band outperformed other indices, likely associated with the ability to detect slightly deeper kelp better than the near-infrared band [115]. Considering these factors, within an image pixel, there is likely a spectral signal mixture of submerged and floating kelp canopy with water; consequently, users should consider that the red-edge indices may produce a higher reflectance signal or slightly more kelp area than nonred-edge indices, depending on the properties of the kelp forest (percentage submerged, depth, size, density, species, object size). Although different band indices were chosen in our analysis, the overall accuracy of the kelp maps produced remained high across all satellites regardless of chosen index, indicating the viability of using different band indices to enhance the detection of floating kelp forests.

Among the many different forms of classification, a commonly used method, the Multiple Endmember Spectral Mixture Analysis (MESMA), has been effectively used for mapping kelp forests in Landsat imagery. The MESMA is a pixel-based approach, which linearly models the amount of kelp and seawater in each pixel using one kelp pixel endmember and multiple water pixel endmembers [19,19,50,62,99,116,117]. In comparison, the OBIA approach presented herein and first used by [25,38] to map kelp forests, is based on clustering pixels into objects before the classification. The advantages of using the OBIA approach proposed in this framework are related to less computational power, less consideration of imagery noise commonly found in pixel-based classifications, the ability to mimic the visual interpretation of features in an image, and the ability to scale object sizes to remain similar across different resolution imagery [25,83,84,84,85,118,119]. Most importantly, the feature space optimization tool allows for the classification to be optimized on a per image basis. With an OBIA approach, single kelp plants are not being detected, but the aggregates of plants floating at the surface, with the inclusion or some submerged kelp and water gaps between patches, depending on the size of objects selected by the user. This gives users the ability to define the best object size based on forests in their region and the resolution of imagery used. Additionally, when users are trying to detect very small and sparse fringing forests in high-resolution imagery, that result in single pixels needing to be classified as kelp, a pixel-based classification has shown to outperform OBIA [25]. In this case, users should be cognizant of the limitations of OBIA and should test the performance of pixel-based methods described in [25].

Across all sensors, the multi-satellite mapping framework resulted in high overall global accuracy (from 88% to 94%) when compared to the range (from 59% to 94%) documented in the literature [25,38,46,52,66]. It is important to note that different sources of validation data were used to evaluate the classification results, including field observations concurrent with imagery acquisition, data acquired from airplane and SCUBA surveys, matched for the same year, and some not matching the same year. However, for using all the different data sources, expert knowledge was always embedded prior to the accuracy assessment to minimize the use of erroneous classification outputs when comparing with validation data. Generally, the errors of omission and commission showed that most errors occurred at medium-resolutions where sparse, and narrow fringing forests along steep shorelines were misclassified as water or omitted due to the coarse resolution low water mask, similar to [46]. This indicates that the relationship between imagery spatial resolution and floating canopy area has to be considered to highlight mapped areas with high uncertainties.

## *4.2. The Impact of Resolution and Drawing Appropriate Conclusions*

The framework presented here incorporated the analysis of a large range of satellite imagery with spatial resolutions ranging from 2.5 to 60.0 m (except for the one aerial image with a resolution of 0.5 m). Generally, although the imagery resolutions differed by one order of magnitude, the mapped floating canopy area at the regional level did not largely differ among resolutions. However, at a finer spatial scale, we found that the floating canopy area mapped in high slope areas (associated with fringing kelp forests) were more impacted at coarser resolutions, indicating that these areas are prone to higher classification errors. As such, we propose the addition of a new parameter, ocean floor slope, to define the limitations of mapping floating kelp forests from different resolution imagery. Particularly, the comparison between floating kelp forest size and slope showed that high slope areas support small kelp forests, leading to more uncertainty when mapping with mediumresolution imagery (up to 50%). Based on these factors, we suggest that regions with slopes higher than 11.4% should either be mapped only with the high-resolution imagery or excluded from comparisons between high-resolution and medium-resolution imagery (Table 6). Additionally, for the time series of floating kelp forest change, we recommend that users consider that a certain percentage of differences among years can be attributed to errors due to resolution, and not be attributed to true changes in floating canopy area. Within our region, we suggest that changes up to 7% (with high slope areas removed from the analysis) be taken into consideration when comparing area from imagery at different resolutions (Table 6).
