**1. Introduction**

Kelp, brown algae in the order Laminariales, are dominant habitat-forming organisms found in cool waters across approximately one-third of the Earth's coastlines [1–3]. Kelp create extensive aquatic forests that provide shelter and food for ecologically and economically important species [1,4,5]. Additionally, kelp provide myriad ecosystem goods and services, such as fisheries production, nutrient cycling and carbon removal, estimated at USD 684 billion per year worldwide [6,7]. However, climate change, overfishing, pollution and increasing harvest threaten the health and persistence of kelp forests globally [1,5,8]. Recent work highlights the negative impacts of heatwaves on kelp forests [9–14] and the loss of key predators, such as sea otters and sea stars, leading to overgrazing-induced regime shifts from kelp forests to urchin barrens [6,15–17].

Kelp forests are dynamic by nature and show high interannual variability [18–20]. Considering that kelp forests are threatened globally, but are highly variable through time, it is important to establish long-term time series to understand how kelp forests are responding to environmental conditions in a time of global change [5,10,20]. Historically, kelp forest research is based on physical field data collection, such as surveys by boats, snorkeling, or SCUBA (self-contained underwater breathing apparatus) diving [5,8,18,21–23]. These survey techniques generally cover small areas and are difficult to maintain long-term because of intensive labor requirements and high operating costs.

**Citation:** Gendall, L.; Schroeder, S.B.; Wills, P.; Hessing-Lewis, M.; Costa, M. A Multi-Satellite Mapping Framework for Floating Kelp Forests. *Remote Sens.* **2023**, *15*, 1276. https:// doi.org/10.3390/rs15051276

Academic Editors: Junshi Xia, Dar Roberts and Simona Niculescu

Received: 1 December 2022 Revised: 11 February 2023 Accepted: 12 February 2023 Published: 25 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Furthermore, these techniques remain logistically difficult due to the seasonality and interannual variability of kelp forests and their extensive distribution along complex remote coastlines with highly variable and sometimes extreme conditions, such as the Pacific Coast of Canada [8,14,24,25].

Specifically on the Pacific Coast of Canada (British Columbia, BC), floating canopyforming kelp, *Nereocystis luetkeana* and *Macrocystis pyrifera*, support a variety of commercially, recreationally and culturally important species [26,27]. Kelp forests with floating canopies are produced by kelp that grow from the bottom of the ocean up to the surface, which then aggregate in beds, henceforth referred to as floating kelp forests [28]. Only a few local areas of floating kelp forests on the BC coast have been mapped at singular time points by aerial surveys [23,29–31,31–36]. Some local-scale studies have measured kelp forests through time, but show variable patterns of change [17,23,37–40]. This highlights the need for large-scale, long-term monitoring initiatives to understand threats and assess floating kelp forest dynamics. In other areas of the Pacific Coast, some successful aerial surveys have quantified floating kelp forest trends [41–43], but these aerial surveys remain operationally cost prohibitive at the scale of the BC coast.

With the enhancement of satellite imagery technology, the ability to monitor floating kelp forests has dramatically improved, specifically with the increasing availability of high-resolution (≤10.0 m) satellite imagery in the 21st century. Differences in the spectral properties of floating kelp and water allow multispectral satellite sensors to distinguish kelp canopies at or near the surface of the ocean, due mainly to kelp's high reflectance in the near-infrared range of the electromagnetic spectrum [25,44]. Many different methods of classification have been applied for mapping floating kelp forests, including manual, pixel-based (supervised, unsupervised, thresholds, spectral unmixing) and object-oriented approaches (see summary by [24]). However, no standardized practices for mapping have been developed and broadly accepted in the literature, making the monitoring of floating kelp forests at large-scales difficult for non-remote sensing experts [8].

Multiple factors influence accuracy when using satellite imagery to map floating kelp forests, such as glint, clouds, tide, bathymetry, coastline morphology, shadow, currents, waves, phytoplankton blooms and adjacency impacts [25,45–47]. In particular, many of these challenges increase in severity from south to north along the West Coast of North America, such as increasing cloud cover, tidal amplitude and coastal complexity [47]. Considering these challenges, the mapping of floating kelp forests using satellite imagery has been largely developed in California, where extensive offshore *Macrocystis* kelp forests are mapped using medium spatial resolution satellites, including Landsat and SPOT imagery from the 1980s onwards (e.g., [13,19,20,48–50]). Several studies have adopted the methods developed in California to map floating kelp forests in other areas of the world, e.g., South Africa [51], Oregon [52], the Falkland Islands [53] and Canada [46]. However, using medium-resolution satellites to map floating kelp forests in BC remains challenging, due to the presence of small fringing kelp forests and the high topographical complexity of the BC shoreline [25,46].

Over the last 50 years, the spatial resolution of Earth observation satellite imagery has rapidly evolved from 80 m to submeter resolutions. Even though satellite data (archived and new) at different spatial resolutions are available globally, no large-scale, long-term study has taken advantage of data from multiple sensors to reconstruct floating kelp forest trends. Here, we present a methodological framework for mapping floating kelp forests from archived medium- to high-resolution satellite imagery, using an object-oriented analysis approach. We discuss the advantages and limitations of combining these data to reconstruct trends. Specifically, the impact of using satellite imagery acquired at different spatial resolutions to detect floating kelp forests are explored, and suggestions for drawing appropriate conclusions when using multiple sensors, are described. Here, we use a test site that supports both small fringing and large kelp forests located on the East Coast of Haida Gwaii, BC, Canada, as a case study to develop a multi-satellite mapping framework for detecting floating canopy of kelp forests. We focus on the methodological framework for creating this time series, not the time series analysis [54]. This framework will contribute to advancements in the remote sensing of floating kelp forests, not only in BC, Canada, but will allow for trends to be understood in remote regions and ultimately help inform effective management strategies for the protection and longevity of floating kelp forest ecosystems globally.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The test site for defining the multi-satellite mapping framework is located in Haida Gwaii on the West Coast of Canada, in the unceded territory of the Haida Nation; whose relationship to the land and sea long predates colonial settlement and still exists to this day [27,55]. Haida Gwaii is a large archipelago with a complex coastline of approximately 4660 km, situated in the Northeast Pacific Ocean (Figure 1A,B [56,57]). Specifically, the study site spans roughly 800 km<sup>2</sup> on the Northeast Coast of Moresby Island, just west of Hecate Strait (Figure 1C). Both dominant floating canopy-forming kelp species, *Macrocystis* and *Nereocystis*, are found in this region. *Macrocystis* grows year round and *Nereocystis* is a perennial species; however, peak biomass occurs in the summer to early fall for both species [22,58,59]. Haida highlight this region's importance for the harvest of *Macrocystis* kelp for the spawn on kelp herring roe fishery, but remark on the significant decline of kelp forests in recent history [60]. In this region, the complex bathymetry supports dense kelp forests of various sizes, from small fringing forests to large offshore forests that span kilometers. Large areas are characterized by very gradual sloping ocean floors, supporting some of the most extensive kelp forests found in BC, which are easily detectable with Landsat imagery of 60.0 m (resampled from 80.0 m) spatial resolution, or better (Figure 1E,F). In contrast, this region also includes smaller, less detectable kelp forests that grow in narrow fringing beds along the steep slopping coastline (Figure 1F). These fringing kelp forests are generally characteristic of kelp forests found across the remainder of the BC coast [38,46,47,61]. This range in kelp forest size makes this region an ideal area to define a framework for using different resolution satellites to map floating canopy area through time.

#### *2.2. Methodological Framework*

The framework is a workflow that allows researchers to compile robust temporal datasets of floating kelp canopy area through the evolution of medium- to high-resolution satellites. The workflow consists of four main steps, including: (1) imagery compilation and quality assessment; (2) preprocessing and enhancements; (3) object-oriented image classification; and (4) an accuracy assessment (Figure 2). To compare floating canopy area derived from multiple satellites, we analyzed kelp's detectability at different spatial resolutions.

**Figure 1.** An overview of: (**A**) the Northwest Coast of North America; (**B**) the location of Haida Gwaii in reference to the British Columbia coast; and (**C**) the Cumshewa Inlet study area. The study site includes: (**D**) large offshore; (**E**) large nearshore; and (**F**) small fringing nearshore kelp forests. Image source: (**D**,**E**) Lianna Gendall; (**F**) Environment and Climate Change Canada.

**Figure 2.** The workflow of the methodological framework.
