**1. Introduction**

Mangroves are highly productive ecosystems, which dominate the intertidal zone of tropical and subtropical coasts. Mangroves fulfil numerous ecological functions (habitats,

**Citation:** Jaud, M.; Sicot, G.; Brunier, G.; Michaud, E.; Le Dantec, N.; Ammann, J.; Grandjean, P.; Launeau, P.; Thouzeau, G.; Fleury, J.; et al. Easily Implemented Methods of Radiometric Corrections for Hyperspectral–UAV—Application to Guianese Equatorial Mudbanks Colonized by Pioneer Mangroves. *Remote Sens.* **2021**, *13*, 4792. https:// doi.org/10.3390/rs13234792

Academic Editor: Junshi Xia

Received: 8 September 2021 Accepted: 24 November 2021 Published: 26 November 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/).

breeding grounds, nursery, carbon sink, water filtration, sediment retention) [1]. Mangroves grow on fine sand to silty sediments in areas protected from high-energy wave action, mainly found in depositional coastal environments, such as the deltaic, lagoon, or mudflat systems. However, these ecosystems are more and more threatened by global changes, which include anthropogenic pressures (i.e., pollution, urbanization, fisheries, aquaculture . . . ) as well as climate change (i.e., increase of temperatures and sea level, high-intensity cyclones ... ) [2,3]. This is even truer on the coasts of countries with rapidly increasing human demography. Management and restoration of these ecosystems has become highly necessary. An initial step toward adequate management is monitoring, which can be done using different techniques. Remote sensing appears to be a valuable approach for field observations given the practical difficulties to access and carry out in situ measurements in those complex and dynamical systems (for reviews, see, for example, [4–6]). Indeed, remote sensing offers synoptic information, allowing the detection, classification, or mapping of mangroves and the monitoring of their spatial organization and temporal evolution. In Amazon-influenced coastal areas, as in large tropical deltaic coasts, mangroves grow over vast intertidal mudflats. Thus, mangroves ecosystems cannot be characterized only through the spatial coverage of mangrove trees. Indeed, they also include a variety of geomorphological forms, such as the creeks, ridges, runnels, sediment platforms, and depressions—that can be observed from aerial view—and, depending on pixel resolution of the imaging sensors, the spatial coverage and density of the trees and the age of the forests. This spatial heterogeneity of habitats increases the complexity in signal processing of remote sensing data, concerning the identification and quantification of the trees and intertidal benthic constituents.

Over the last decades, remote sensing has undergone major developments resulting from a combination of technological progress in platforms, sensors, data processing, and data availability. Among all the possible remote sensing issues (e.g., meteorology, military applications, cartography, topography, oceanography, geology, natural hazards, etc.), several applications make use of the radiometric properties of the scene, such as computation of classification indices, spectral unmixing, or radiative transfer modelling [7]. Indeed, each substrate has a specific spectral signature (i.e., the reflectance as a function of wavelength), which can be used for material identification or classification. This requires a high spectral fidelity and the measurement of a wide spectrum at high resolution, which are only offered by hyperspectral sensors [8]. Until then, hyperspectral data were mainly collected from airborne or satellite platforms, as the Hyperion satellite imaging spectrometer, with a spatial resolution from about 50 cm for low altitude (<1500 m) airborne surveys [9] to dozens of meters for high altitude surveys [10]. VNIR (visible and near infrared) hyperspectral sensors provide hundreds of continuous spectral bands between 400 nm and 1100 nm. Such a spectral richness allows accurate mapping and classification of complex environments, such as vegetation and ground features. The development of airborne sensors and high spatial resolution hyperspectral images meets a large audience in environmental research and particularly in forestry. Numerous studies over a significant range of forest types, using various classification algorithms, have emerged during this decade [11–14]. That underlines the applicability and potential of hyperspectral images for mapping vegetation over various spatial footprints and spatial resolutions. Hyperspectral monitoring of mangrove forests has mostly been developed since the early 2000s from aircraft vessels along the southeastern coast of the USA [15], the Indian coast [16–18], the Australian coast [12], and the southeastern coast of Asia [19–21].

Considering their ability to provide quick and cost-effective observations with great flexibility in survey planning, the use of unmanned aerial vehicles (UAVs) or drones has boomed over the last decade. Because of limited payload, small UAVs are mostly equipped with RGB or VNIR multispectral cameras. Nevertheless, drone-based hyperspectral sensing solutions also arose in the last few years [22]. These hyperspectral–UAV systems can now complement airborne and satellite approaches for hyperspectral imaging and bridge the gap in resolution and spatial coverage between remote data and ground-based measurements. In flying at low altitude (below 150 m), UAVs offer the opportunity to collect very high spatial resolution data, capturing a larger number of details. Furthermore, hyperspectral– UAV systems also allow a great flexibility regarding devices configuration and flight plan adjustments or times at which surveys are carried out. Surveys of mangrove forests by drones equipped with a hyperspectral camera mark a recent turning point in terms of image resolution and survey repeatability. The studies involving hyperspectral–UAV surveys of mangrove forests mostly took place on the southeastern coast of China [19,23,24]. To our knowledge, there has been no previous study, involving a hyperspectral–UAV camera, conducted over mangrove forests in South America and French Guiana.

These drone platforms offer interesting capabilities, provided one has adequate algorithms for georeferencing and radiometric corrections. Several studies refer mainly to helicopters or fixed-wing UAVs, designed to support large and heavy (>5 kg) payloads [25–28]. However, helicopters generate high frequency vibrations and require specially trained operators. Fixed-wing platforms provide long and smooth flights but are wide UAVs, which require a large area, suitable for take-off and landing. On the contrary, multi-rotor UAVs have a shorter autonomy and generate high-frequency vibrations; however, being able to take-off and land vertically and fly at a steady altitude, they are more suited to field areas of few hectares [22]. Usually considered as "mini-UAVs" (<20 kg), they support lighter payloads (<5 kg) [29–31]. Stuart et al. [22] propose a review of relatively low-cost, field-deployable hyperspectral devices, particularly UAV-based devices, for environmental monitoring. These drone-based technologies include point-based spectrometers, push broom sensors, and, more recently, hyperspectral frame cameras. These systems differ in their spatial coverage, in the tradeoff between spatial and spectral resolutions, and in how easy image reconstruction and georeferencing are with their use. Dedicated preprocessing methods usually have to be developed for data georeferencing and radiometric corrections. Specific algorithms for radiometric corrections also need to be elaborated, given that classical models for geometric, atmospheric, and radiometric corrections are not suited to UAV data, considering the flight height and temporal and spatial scales of UAV surveys [8,29,32–34].

The electromagnetic radiation received by the sensor is referred to as the spectral radiance. In reality, the sensor records raw digital numbers (DN). In many studies, the parameter of interest is the ratio of upwelling radiation in a given direction toward the sensor (radiance) to downwelling radiation (irradiance), known as remote sensing reflectance. The latter is a key parameter, sometimes denoted as the spectral signature, which theoretically allows a description of the nature of the studied surface, independently of the sensor, viewing geometry, sun azimuth, elevation, or the weather conditions. Thus, radiometric corrections, consisting in converting the DN recorded by the sensor into ground reflectance values, can rely on different approaches, often requiring the acquisition of complementary field data. This process is necessary for data interpretation or diachronic comparisons. The geometric, radiometric, and spectral properties of the instruments can be partly characterized and calibrated through laboratory tests, but this requires having a dedicated test bed [34]. Regarding radiometric corrections, Saari et al. [32] proposed a drone equipped with downwelling and upwelling irradiance sensors to record illumination conditions during the flight. However, adding embedded sensors increases the payload, which is generally the main limit of UAV systems [29,34]. Another approach consists of using additional systems on the ground, especially white reference panels, to convert the sensor's digital number (DN) to reflectance [8,33–35].

Drone monitoring has proven particularly useful as a non-destructive data acquisition technic in dynamic and complex coastal and estuarine systems where ground-based field surveys are very difficult, especially for monitoring purposes in unconsolidated temperate mudflats [36] and along the French Guiana (FG) littoral zone, for sandy beaches and mangrove-colonized mudbanks [37,38]. Belonging to the largest mudflat in the world, the French Guianese coastline, dominated by mangroves, is indeed experiencing extremely rapid morphological changes in response to the large amounts of Amazonian sedimentary

inputs [39]. Mangrove ecosystems development or disappearance accompanies the alternation of Amazonian mudbanks accretion and erosion phases along the north coast of South America between the Amazon and Orinoco Rivers [40,41]. At a regional scale, geomorphology changes rapidly and becomes favorable to the development of different mangrove stand ages [40,41] and specific benthic biodiversity, which in turn modify the geomorphological evolution of the mudbanks. Indeed, as the mud consolidates, the substrate elevation increases and the flooding time during each tidal cycle decreases. As results of such dynamics, biological processes within sediments are intense, enhanced by both benthic biofilm development and bioturbation by crabs [37,42]. In these geographical areas, the benthic biofilm is as important as the mangrove trees in terms of carbon biomass and as a source of organic matter for the coastal food webs and regional biogeochemical cycles [43–46].

In this natural and complex context, it is necessary to explore the spatial and spectral richness of hyperspectral–UAV data. A reliable analysis of these data requires a first step of radiometric pre-processing. The present study describes and implements a radiometric correction method on UAV in situ data, collected along a gradient of pioneer mangroves in French Guiana, using the Hyper-DRELIO drone. Radiometric corrections here encompass calibration and in situ standardization. The method we propose is easy to implement, without adding embedded sensors and with limited additional equipment.

#### **2. Study Area and Survey Setup**

The field campaign took place in the northwestern part of French Guiana (Awala-Yalimapo; Figure 1a,b), during the dry season in September 2018. Besides the natural dynamics of mudbanks, this region is submitted to additional anthropogenic pressure following polder erosion, making the area unstable [47]. Preliminary results showed that mangroves colonized the consolidated part of the mudbank in 2015 and the oldest trees were about 3 years old at the time of our field measurements campaign. The northwestern part of the mudbank was characterized by unconsolidated bare mud.

**Figure 1.** (**a**) Localization of the study area in Awala-Yalimapo in the western part of French Guiana. (**b**) UAV flight plans above the study area, designed to capture the various mud facies (from bare mud to mangrove stages). (**c**) Diagram of a survey setup and the physical variables to be measured.

Tides at this location are semidiurnal in the study area with high spring tide ranges up to 3.5 m and a mean tidal range of 1.68 m (https://maree.shom.fr for "Les Hattes" site—access on 1 April 2020).

Taking advantage of the flexibility of drone-based systems, we tailored the survey to examine benthic biofilm development in relation to the tidal cycle, carrying out the flights at low tide during spring tide. As depicted in Figure 1c, hyperspectral drone surveys were synchronized with in situ measurements for radiometric correction and validation purposes.

#### **3. Materials and Methods**

#### *3.1. Hyperspectral UAV*

The integration of the light push broom hyperspectral sensor onboard the multirotor UAV, called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations), is described in Jaud et al. (2018). The system is composed of an electric octocopter platform (Figure 2a,b), an imaging module, and a navigation module, synchronized via CPU (central processing unit) timestamps. To complement this system, a ground segment allows sensor parametrization, data quality control during the flight, and flight parameter control.

**Figure 2.** (**a**,**b**) Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations) platform, unmanned aerial vehicle (UAV) for hyperspectral imagery. IMU: inertial motion unit; RTK GNSS: real time kinematic global navigation satellite system. (**c**) White Spectralon panel (reflectivity: 99%). (**d**) Grey Spectralon panel (reflectivity: 20%).

> The drone has a diameter of 1.2 m, weighs 13.4 kg, and can handle a payload of 5 kg (including batteries, cables, navigation modules, and imagery modules). Considering the duration of ascent and descent phases and a safety cushion, the programmed flight plan duration must not exceed 7–8 min [30]. The onboard flight control system of the drone is composed of a global navigation satellite system (GNSS) and an autopilot, run by DJI® iOSD® software. The navigation module, which measures position and orientation during the flight, is composed of a dual-antenna RTK (real time kinematics) GNSS receiver with a baseline of 85 cm and an Ekinox-D® (SBG System®) inertial motion unit (IMU). The hyperspectral camera, a Micro-Hyperspec® VNIR (Headwall®), is a push broom (or line-scanning) system, collecting reflected light through an image slit. The principle of operation of this camera relies on holographic diffraction, using gratings and mirrors to split monochromatic light into 250 spectral bands, ranging between 400 nm and 1000 nm, with 1.85 nm of spectral resolution. The manufacturer carried out a wavelength calibration beforehand, in order to determine the correspondence relationship between imaging spectrometer probe elements and the central wavelength. On the CCD sensor matrix, rows collect spatial, across-track information and columns record the spectral content of the signal. Values for each element of the matrix are expressed as 12-bit DN (i.e., values between 0 and 4096). The camera is equipped with a fixed focus lens, focused to infinity. Aperture and sensor gain G were adjusted before the flight, depending on the illumination

conditions, in order to avoid saturation of the CCD cells. The integration time remained fixed during the flight, which required that there is no major variation of the illumination conditions during the flight (around 12 min duration).

The acquisition frame rate was parametrized to 50 Hz, which is compatible with a UAV speed of around 3–4 m/s (close to the lower boundary for drone stability). At 50 m above ground level, the configuration chosen for this study, the swath was 45 m wide and the across-track ground resolution was 4.5 cm. With a speed of 3 m/s, the along-track ground sampling was about 6 cm.

A line-by-line geo-registration procedure was proposed in [30] for geometrical preprocessing of hyperspectral data. The accuracy of this direct georeferencing method is on the order of 1 m for a flight at 50 m of altitude. The quality of the georeferencing process is limited by several factors: mechanical stability of the platform, the timing accuracy, and more particularly, the resolution and accuracy of the proprioceptive sensors (GNSS receivers and IMU). This geo-registration to realign and geo-reference the push broom data is non-intrusive and preserves fragile substrates, such as mud. Ground control points would have allowed a more accurate estimation of the geo-registration error, but their installation would have been very destructive to the substrate.

With such characteristics, the Hyper-DRELIO system is adequate to cover areas of approximately 10,000 m2 and to study objects ranging from 10 centimeters to several meters. The main advantage of the drone is that it can perform repeated overflights over an area that changes over time (such as the mudflat during a tidal cycle). However, push broom technology is not suitable for imaging moving objects, such as waves or animals. In addition, the drone platform is dependent on weather conditions and flights cannot be performed in rain or strong winds. Cloudy skies are the preferred conditions to avoid sun glint effects; however, all flights during the mission took place in sunny conditions.
