*3.4. Resolution Analysis*

Figure 9 illustrates the relationship between pixel size and the mixing of the spectral signature of different features within a pixel. Specifically, within a pixel resolution (for instance, 30.0 by 30.0 m for Landsat-5), the kelp spectral signature is averaged with the spectral signature of other classes in close proximity (e.g., water), decreasing the ability to accurately map floating kelp forests as pixel size increases. In particular, as resolution decreases, floating kelp and water spectrum are mixed, and the reflectance in the nearinfrared wavelengths decrease (Figure 9G). At the sample location shown in Figure 9, the object-based classification can no longer differentiate the floating kelp signal from water at 60.0 m (Figure 9G).

Pixel mixing decreases the ability to correctly classify floating kelp when using medium-resolution imagery. We show that at downgraded resolution, images generally produced a floating canopy area within 9% of their image's original kelp forest area (Figure 10). For instance, at 6.0 m resolution, the mapped floating kelp canopy area is 93%, i.e., 7% lower than the mapped area at the original 2.6 m resolution. This can be assumed up to a certain downgraded resolution because the further an image is downgraded away from its original resolution, the more likely artifacts or errors from the interpolation methods may occur, such as blurring and edge halos [94]. This possible issue is minimized by avoiding data analysis of downgraded high- to medium-resolution, and instead, considering a downgraded Sentinel-2 image from 10.0 m (SE10) to 20.0 m, 30.0 m and 60.0 m (SE20, SE30 and SE60, respectively). In this case, the results show that the floating kelp canopy area remained almost unchanged when downgrading the medium-resolution Sentinel-2 image from 10.0 m to 20.0 m, 30.0 m and 60.0 m (Figure 10C).

**Figure 9.** *Cont*.

**Figure 9.** Clips of the same location of (**A**) the QuickBird-2 image (2.6 m) down sampled to (**B**) 6.0 m, (**C**) 10.0 m, (**D**) 20.0 m, (**E**) 30.0 m and (**F**) 60.0 m, with (**G**) showing the spectra measured at the sample location. Floating kelp forest classification is shown as a pink outline. Images are false color infrared showing land vegetation and seaweeds (including kelp) as red, rock/sand as light blue and water as dark blue to black.

**Figure 10.** The change in kelp area as a percentage of the kelp area (dark grey bars) derived from the original image's resolution (light grey bar) plotted by resolution for (**A**) QuickBird-2 (original resolution: 2.6 m), (**B**) RapidEye (original resolution: 5.0 m) and (**C**) Sentinel-2 (original resolution: 10.0 m).

Figure 11 shows the relationship between floating kelp forest size (produced from the three original images) and ocean floor slope. Generally, areas of low–mid slope (0–11.3%) were associated with both small (<17,000 m2) and large kelp forests (≥17,000 m2), whereas high slope (>11.4%) areas were only associated with small fringing kelp forests (<17,000 m2). The low–mid slope areas exhibited a lower percent difference (within 7%) of floating kelp forest area between the various imagery resolutions than the high slope areas (up to 50%) overall (Figure 12). In particular, the differences in floating canopy area in high slope regions were much more pronounced in the downgraded medium-resolution imagery (SE20, SE30 and SE60) than the high-resolution imagery (QB6, QB10, RE6 and RE10). These results allowed us to restrict the use of medium-resolution imagery to map floating kelp forests only in areas of mid–low slope, i.e., imagery resolution between

20.0 and 60.0 m is not recommended for high slope areas where fringing small kelp forests dominantly occur.

**Figure 11.** Kelp forest size (m2) by segment slope where blue represents all kelp forests found within the low–mid slope category (0–11.3%) and orange represents high slope area (11.4–37.0%).

**Figure 12.** The change in floating canopy area as a percentage of that derived from the original image plotted by resolution separated into (**A**,**C**,**D**) low–mid and (**B**,**D**,**F**) high slope categories for (**A**,**B**) QuickBird-2 (original resolution: 2.6 m), (**C**,**D**) RapidEye (original resolution: 5.0 m) and (**E**,**F**) Sentinel-2 (original resolution: 10.0 m).

#### **4. Discussion**

With advances in remote sensing technology, opportunities to map and monitor important ecosystems across large scales through time are increasing. The Landsat series offers the best tool to map floating kelp forests at a single resolution (30.0 m) back through time [13,19,46,62,63,99]. However, the ability to use medium-resolution imagery to accurately map changes in floating kelp canopy area through time remains difficult in regions with small fringing kelp forests, such as the Pacific Coast of Canada and Oregon [46,52]. Here, we developed a framework combining standardized practices and adaptable methods to produce a long time series of accurate maps of floating kelp forests from satellite imagery acquired at various spatial resolutions. We show that the ability to map floating kelp forests at different imagery resolutions can vary spatially based on ocean floor slope, and thus this metric can be used to highlight areas of uncertainty. Herein, we make a case for the workflow, discuss the impact of spatial resolution on kelp detection, summarize recommendations for researchers when using the multi-satellite mapping framework (Table 6) and more broadly consider the limitations and applications of the research.

**Table 6.** A summary of the recommendations outlined in the discussion for researchers applying the multi-satellite floating kelp mapping framework to create a long-term time series of kelp forest canopy area.

