**1. Introduction**

Tropical shallow hard coral reef ecosystems provide numerous and valuable services to local socio-economies, such as fish and seafood provisioning, coastal protection, or wealthy recreational activities [1]. These services have been estimated to support more than 500 million people worldwide [2]. Even though coral reefs cover only 0.1% of the oceans, they host 25% of all marine identified species [3]. However, anthropocenic changes, embodied by both sea level, sea temperature and sea acidification rises and also sedimentation related to watershed deforestation and land claiming, are strongly threatening these pivotal ecosystems [4].

The protection and sustainable management of these ecosystems requires us to adopt an integrated view of the seamless land- and seascape at a high spatial resolution, adequate to meet local stakeholders' expectations [5]. Even if the global, thus coarse (>1 m pixel size), products are insightful for assessing coral reef trends, the very high spatial (i.e., <1 m pixel size) mapping of the land use land cover (LULC) and the sea use sea cover (SUSC) constitutes a fitting response to needs of local users, managers and decisionmakers. Either passive or active, airborne imagery can successfully provide some spectrospatial combinations able to generate coastal topography and bathymetry using unmanned airborne vehicles [6,7], and map sub-metre LULC and SUSC using hyper-/multi-spectral camera [8] or multi-spectral light detection and ranging (LiDAR) system [9]. However, the

**Citation:** Collin, A.; Andel, M.; Lecchini, D.; Claudet, J. Mapping Sub-Metre 3D Land-Sea Coral Reefscapes Using Superspectral WorldView-3 Satellite Stereoimagery. *Oceans* **2021**, *2*, 315–329. https:// doi.org/10.3390/oceans2020018

Academic Editor: Rupert Ormond

Received: 29 October 2020 Accepted: 25 March 2021 Published: 2 April 2021

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**Copyright:** © 2021 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/).

manned or unmanned airborne limitations, tied to the elevation-specific flight planning and the relatively small surveyed area, impede their utilization for regional mapping [10]. The sub-metre spaceborne imagery has emerged as a tool of interest given its capability to capture large extents with a very high spatial resolution, despite its purchase cost [11]. Around 2000, IKONOS (1999) and QuickBird-2 (2001) became the first satellite sensors collecting imagery at 1 m pixel size across regional scales. These civilian and commercial pioneers were followed by sub-metre United States WorldView-1, -2, -3, -4 (2007, 2009, 2014, 2016), GeoEye-1 (2008), SkySat series (2013–2017), French Pleiades-1A and -1B (2011 and 2012), Korean Kompsat-3 and -3A (2012 and 2015), United Kingdom TripleSat (2015), and Chinese Gaofen-2 (2014), Jilin-1 (2015), Superview-1 (2018) [12]. In addition to their spatial resolution capability inherent to the panchromatic band, most of these sensors acquire four spectral bands: the visible (VIS) blue, green, red (BGR), and the optical near-infrared (NIR). Three outliers thereupon appear: the panchromatic WorldView-1, the optical 8-band WorldView-2, and the optical+mid-infrared (MIR) 16-band WorldView-3. The WorldView-2 improved the bathymetry mapping [5], the coral cover and health mapping [13,14], and the seamless LULC/SUSC mapping [11]. The WorldView-3 augmented the bathymetry [15], mineral [16], hydrocarbon [17], lithological [18], salt marsh [19], tropical forest [20], coral reef [8], and even urban plastic [21] mapping.

Furthermore, the spaceborne sub-metre LULC mapping was significantly enhanced by the (tri-)stereo-acquisition of the same scene, offering the opportunity to produce seamless land-sea digital surface models (DSMs), using the photogrammetry for land and the ratio transform for sea [22]. Horizontal and vertical accuracies of the land DSM-derived stereo-Pleiades-1 have been quantified at 0.53 and 0.65 m, respectively [12]. The addition of the topographic band to the spectral information has been shown to significantly improve spaceborne sub-metre LULC mapping [12]. Even if the novelty of the latter work relied on the sole use of a spaceborne stereo-imagery, the bathymetry and the SUSC mapping were not examined. An integration of the terrestrial and marine DSM into the spaceborne sub-metre spectral dataset was elsewhere useful in mapping the seamless coral reefscape in Japan using Google Earth imagery [23], but it was not derived from a sole spaceborne by-product. To our knowledge, a unique study has focused on the land-sea coral reefscape mapping using a sole spaceborne sub-metre stereo-imagery [24].

Despite the use of the WorldView-3 stereo-imagery to produce land-sea DSM, the authors had not previously investigated the added value of the 16-band superspectral dataset to map LULC and SUSC, simultaneously. In this paper, we innovatively propose to classify sub-metre LULC and SUSC of a coral reefscape using a sole spaceborne stereoimagery, from which the topographic, bathymetric and superspectral information are derived. The scene studied was acquired over the complex coral reefscape of Moorea Island (French Polynesia, South Pacific) using a WorldView-3 stereo-imagery (Figure 1). The chosen area exhibits representative eight LULC and five SUSC classes, and encompasses steep volcanic vegetated watersheds, flat rural coastal areas, and a reef-dominated lagoon. An set of five issues will be considered: (1) the added value of the Coastal and yellow bands to the basic BGR classification accuracy; (2) the added value of the Red Edge (RE), NIR1 and NIR2 bands to the basic BGR classification accuracy; (3) the added value of the MIR bands to the basic BGR classification accuracy; (4) the influence of the topobathymetry (i.e., land-sea DSM) on the basic, visible, optical and optical+MIR datasets' classification performance; and (5) all four previous questions considered at the class-level.

**Figure 1.** Natural-coloured WorldView-3 imagery (0.3 m × 0.3 m, 3017 × 5937 pixels) of the study area on Moorea Island (French Polynesia). (**a**) The red and green spheres represent 32 topographic and 35 bathymetric calibration/validation datasets; (**b**) the array of 105 multi-colour rectangles represents 78,000 pixels of 13 habitats, each one composed by 3000 calibration and 3000 validation pixels.
