*4.3. The Challenges and Broad Applications of the Methodological Framework*

A few challenges and limitations remain when using this proposed framework. Unfortunately, the remote sensing of kelp canopies in this framework are currently limited to those floating on or close to the surface, due to water's high-absorption of the near-infrared signal [115]. The detection of subsurface kelp forest canopy from aerial and satellite imagery remains difficult, is limited to shallow depths, generally necessitates clear waters and often requires the use of high-resolution imagery or hyperspectral data [66,120–122]. More work would be needed to expand these methods to subsurface kelp forests. Additionally, environmental impacts (e.g., tides and currents) are challenging to isolate because they largely differ based on location, species type, density and the time of imagery acquisition. We were able to minimize the impact that different environmental conditions have on imagery through criteria; however, we were unable to quantify or create correction factors for the impact of tides or currents. Of note, given our goal to produce highly accurate floating kelp forest maps, the approach suggested here is a supervised classification method and needs some expert knowledge to determine good training samples for the classifier in any given area. Moreover, when using the multi-satellite mapping framework, researchers should consider the species and density of kelp forests present within their region. In our case, floating kelp forests were generally dense, regardless of the forest size and species, thus conforming with the presented framework. For other regions where sparse kelp forests dominate or areas containing solely *Nereocystis* kelp forests, special attention should be given to the detection limits at different resolutions, the use of OBIA versus pixel-based classifications for very small sparse forests [25] and the ocean floor slope threshold.

The spatial resolution analysis and subsequent recommendations were conducted with rigorous methodological criteria; however, the analysis was limited to samples of imagery from three satellites. We acknowledge that the sample size is limited, and other satellite-associated variables beyond spatial resolution, including spectral resolution, signal-to-noise ratio, and satellite vicarious calibration, also play a role in detectability [25,123,124]. However, the resolution analysis allows for a conservative approach when drawing conclusions from the time series of floating kelp forests. Additional research in the future may include defining correction factors, similar to the tidal correction factor applied by [62], to minimize the effects of the different spatial resolutions. For this, we recommend multiple replicates of comparisons between satellite images collected at different spatial resolutions in similar conditions over the same location, within a short time frame. Furthermore, it is important to note that our unit of analysis was ~1 km segments. In the literature, the size of segments for kelp time series analyses vary substantially (e.g., 100 m in [38], 8 km in [43] and 1 km in [125]), and as such, special consideration should be given to the scale of future analyses. We advise that further explorations of the resolutions' impact on kelp detectability be made if the unit of analysis (segments length) significantly differs from the 1 km segment size presented in this study.

Many methods exist to detect floating kelp forests from satellite imagery; however, most focus on compiling a time series using a single type of sensor, which can limit either the spatial resolution of imagery available (i.e., 30.0 m Landsat imagery from 1984 onwards), or with the use of high-resolution satellite imagery the length of the time. The MESMA approach, used with Landsat imagery from 1984 onwards to detect large offshore forests of kelp in California [19,50,62,117], has been used to map *Macrocystis* in the southernmost part of Argentina [108] and *Nereocystis* off the coast of Northern California [91] and Oregon [52]. However, when this approach was used to map kelp forests on the Central Coast of BC, between 28% and 75% of kelp that was present in the shoreline areas was missed due to the medium-resolution of the Landsat imagery [43]. Recently, a similar method using 10.0 m Sentinel-2 data was created [65], and although this method uses higherresolution imagery, the Sentinel-2 data repository only dates back to 2015. In contrast, the methodology presented here enables trends to be understood with high-resolution data back to the early 2000s, and medium-resolution data back to the 1970s. The methods proposed by [25,46,50,65], and the one shown here, when integrated with the growing availability of higher-resolution imagery such as the Planetscope satellite series (available since 2018), will streamline the monitoring of floating kelp forests into the future. It will also continue to allow scientists to better understand large-scale trends in floating kelp forests in a time of unprecedented kelp forest loss, such as those documented in California [117,126], Baja California [12], Japan [127,128], Australia [9,10,129–132], Oman [133], Norway [134], Spain [135,136], Chile [137] and the Atlantic Coast of Canada [11].

#### **5. Conclusions**

Globally, threats to kelp forests are on the rise; however, locally, kelp forests show highly variable patterns of change [1,3]. This study highlights that with the advancement in Earth observation satellite technology, archived satellite imagery can be leveraged for the monitoring of crucial floating kelp forest ecosystems using medium-resolution imagery from the 1970s onwards, and more recently, using high-resolution imagery from the early 2000s onwards. The multi-satellite mapping framework allows for the creation of a floating kelp canopy area time series using medium- to high-resolution satellite imagery through standardized practices (i.e., the image quality criteria, geometric and Rayleigh correction) and adaptable image-to-image methods (i.e., band index/ratio selection and OBIA). We acknowledge that differing resolutions have an impact on kelp detection, and that when using this framework we suggest using ocean floor slope (removing areas of slope > 11.4%) as a metric to highlight areas of uncertainty in kelp detectability. Creating these long time series of floating kelp forests using the framework can facilitate the monitoring and protection of these important nearshore habitats from emerging threats. Additionally, when coupled with environmental driver data and/or climate prediction modelling, it can highlight the regions of risk and resilience of floating kelp forests globally.

**Author Contributions:** L.G., M.C. and M.H.-L. designed the methodological framework; L.G. carried out the compilation, the processing of data and the writing of the manuscript with guidance from S.B.S., M.C. and M.H.-L.; P.W. facilitated the provision of some of the high-resolution satellite imagery and provided revisions on the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** During this research L.G. was supported through a MITACS Accelerate internship with the Hakai Institute, as well as M.C.'s NSERC-DG.

**Data Availability Statement:** Data are available for research purposes upon request to the authors' institutions.

**Acknowledgments:** We thank the Hakai Institute for partially funding this work, as well as the Canadian Hydrographic Service, Transport Canada, the Department of Fisheries and Oceans Canada (in particular Joanne Lessard), Environment and Climate Change Canada, and ShoreZone, for providing satellite data and ground-truth data. A special thanks to Lynn Lee with Parks Canada and Stuart Crawford with the Council of the Haida Nation for assistance with field equipment in Haida Gwaii.

**Conflicts of Interest:** The authors declare no conflict of interest.
