2.2.4. Step 4: Quality Assessment

For the classification validation, in situ kelp data and historical survey data for the region were compiled. Ideally, ground-truth data should be collected at the time of satellite imagery acquisition [25]. However, if no ground-truth data are available, other forms of data can be used, such as past surveys showing the location of floating kelp forests [38,50,89], or expert knowledge based on reflectance values [35]. For our dataset, we compiled two forms of validation data: (i) in situ and (ii) archived data (Figure 4). The in situ data, including drone images, photoquadrats (camera mounted above a 1 m quadrat lowered to the seafloor), above water oblique photos from a boat, and remotely operated underwater vehicle footage, were acquired in August 2021, a day after PlanetScope imagery acquisition. Archived surveys were obtained, comprised of oblique photos from an aerial survey performed by Environment and Climate Change Canada (ECCC) in 2015, multiple years of SCUBA surveys (1990, 1994, 2007, 2012, 2017) from the Department of Fisheries and Oceans Canada (DFO) [90] and kelp shoreline classifications from an aerial survey conducted in 1997 by ShoreZone. All validation data were combined into a dataset of spatial points and classified as either floating kelp present or absent. Specifically, the DFO Scuba surveys and ShoreZone data were simplified from species specific data (*Macrocystis* and *Nereocystis*) to presence and absence. For the archived aerial images from ECCC, a random subset of images was visually assessed for presence or absence. All validation data were compared to the classification produced from imagery in the matching year to produce measurements of accuracy corresponding to users', producers' and global accuracy, meaning errors of commission (false-positives), omission (false-negatives) and overall accuracy [91]. No archived validation data were available during the years of acquisition for the three highest resolution satellites, so in order to validate products from the QuickBird-2, Geoeye-1 and Worldview-2 satellites, the ECCC oblique photos were used with the assumption that some errors would be associated with yearly variability.

#### 2.2.5. Resolution Analysis

Here, we evaluate the impact of spatial resolution on the detectability of floating kelp forests in satellite imagery at different scales. This analysis allowed us to define an independent variable (ocean floor slope) to be used as an indicator of kelp forest size, highlighting areas of uncertainty. The following steps were adopted in this analysis:

Step 1: Images from QuickBird (2.6 m), RapidEye (5.0 m) and Sentinel-2 (10.0 m), with their original spatial resolutions, were resampled using bilinear interpolation to the different resolutions matching the satellite database (6.0 m, 10.0 m, 20.0 m, 30.0 m, 60.0 m; Table 1) following [92,93]. Sentinel-2 was included in the analysis to address possible interpolation errors [94] associated with resampling high-resolution imagery from QuickBird-2 and RapidEye to 20.0 m, 30.0 m and 60.0 m resolutions. The original and resampled images were classified using the OBIA method described in Sections 2.2.2 and 2.2.3. To ensure that images from the same sensor remained comparable, we used the same areas to train the

classifier for each set of down-sampled images. After the classification of the resampled images, the overall detectability across the study region was measured as the total amount of floating canopy area (m2) detected in the downgraded resolution, divided by the total floating canopy area (m2) detected in the original image, and presented as the percentage of floating canopy area.

Step 2: A 1 km segment-based approach was used as the areal unit to evaluate the impact of resolution (see Figure 5 for the delineation of segments). Due to the complex bathymetry and presence of large offshore and nearshore floating kelp forests in our study area, ocean floor slope was used to delineate these segments, as adapted from [95]. To achieve segments that could extend kilometers offshore, segments were created in two categories, along the shoreline (ocean floor slope greater than 3%) and out across the low slope areas extending offshore (ocean floor slope of less than or equal to 3%), using 20 m bathymetry data from CHS [76]. These ocean floor slope categories were only used to construct the segments and were not used in further analyses.

Step 3: Ocean floor bathymetry often limits the size of kelp forests by reducing the available area to grow [46,96]; therefore, we assume it can be used as a proxy for kelp forest size. For example, in high slope areas, the bottom quickly becomes too deep, limiting the availability of light needed for kelp to establish and grow. In these conditions kelp only grows in narrow fringing forests, which are more difficult to detect in satellite imagery. Consequently, ocean floor slope was used to define areas where we would expect larger inaccuracies of the classification at different resolutions. However, first we tested the assumption that high slope areas support small fringing kelp forests [46].

For this, the relationship between ocean floor slope and kelp forest size was explored (classified from the original QuickBird-2, RapidEye and Sentinel-2 images and measured in m2). For each segment, we defined the mean ocean floor slope based on the 20 m bathymetry data from CHS [70], where a single kelp 'forest' was defined as a continuous patch of attached floating kelp where kelp objects in the classification were connected. Based on the relationship of floating kelp forest size and ocean floor slope, we divided the segments into two broad categories: low–mid slope areas (0–11.3%), which support large and small kelp forests, and high slope areas (11.4–37.0%), which support only small kelp forests. Next, floating canopy area percentage was compared between the two slope categories.

**Figure 4.** Examples of in situ and archived data for the accuracy assessment of the classification. The DFO SCUBA surveys are not shown.

**Figure 5.** A map of approximately 1 km segments categorized into two main groups based on segment mean slope: low–mid (0–11.3%) shown in greyscale and high (11.4–37.0%) shown in red.

#### **3. Results**

#### *3.1. Imagery Quality Assessment*

Out of hundreds of archived images examined across many different sources (Table 1), a total of 52 images (from 1973 to 2021) were selected after the quality assessment step. No good-quality images were found for a total of 12 years, including 1975, 1978–1981, 1983, 1987, 1993, 1995, 1996, 2003 and 2004. Landsat was the only freely available satellite imagery provider before 2004 and thus the preferred choice for imagery; in particular, Landsat-7's scan line corrector failure in 2003 [97] led to no available images for 2003 and 2004. For years following 2005, the preferential choice was for high-resolution imagery (2.0–20.0 m). Imagery from a single Spot 4 image (20.0 m) to numerous images from Sentinel-2 (10.0 m), QuickBird-2 (2.6 m), Geoeye-1 (1.8 m), Worldview (1.2 m and 1.8 m), PlanetScope (3.0 m resampled from 3.7 m) and RapidEye (5.0 m) satellites were compiled. In addition to the high-resolution satellite imagery, we included a single nadir RBG aerial image (0.5 m spatial resolution) from the Canadian Hydrographic Service in the dataset because of the lack of good-quality high-resolution satellite imagery in 2007.

The 52 archived images selected through the criteria were acquired in various conditions, leading to a range in quality scores (Table 3). The largest proportion of imagery (46%) were acquired during 'optimal' conditions for floating kelp forest mapping, followed by the second largest proportion (37%) acquired during 'good' conditions. Together, the 'good' and 'optimal' imagery account for 83% of the total imagery. Among the defined criterion, more often, high tides or the presence of glint and cloud in imagery led to lower scores than waves and haze (Table 3). Notably, in some years, no optimal low tide (<3 m above chart datum) imagery was available because of the high tidal exchange that occurs in Haida Gwaii (up to 7.8 m above chart datum) [98], leading to 33% of images ranked in the lowest category for tides (5.0 to 6.0 m). These images were still included in the time series dataset because floating kelp forests were readily visible upon inspection.


**Table 3.** A summary of image quality criteria where percent (%) is the proportion of the 52 images that fall into each category.
