**1. Introduction**

Salt marshes are ecological transition zones where marine and terrestrial ecosystems interact [1]. These ecosystems are characterized by a unique and highly specific assemblage of plants and animals [2] and high primary production, with the plant species being a crucial component of the system dynamics [3]. They offer numerous recognized ecosystem services; highlights among them are the services of coastal protection and blue carbon sink [4–7].

Tidal salt marsh vegetation is typically halophyte and has to tolerate regular periods of immersion/emergence, salinity and anoxia [8,9]. To adapt to these stresses, these plants

**Citation:** Curcio, A.C.; Barbero, L.; Peralta, G. UAV-Hyperspectral Imaging to Estimate Species Distribution in Salt Marshes: A Case Study in the Cadiz Bay (SW Spain). *Remote Sens.* **2023**, *15*, 1419. https:// doi.org/10.3390/rs15051419

Academic Editors: Kenji Omasa, Shan Lu and Jie Wang

Received: 27 January 2023 Revised: 25 February 2023 Accepted: 28 February 2023 Published: 2 March 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/).

have developed unique morphological, anatomical, and physiological characteristics [10,11]. The distribution of the salt marsh plant species follows a typical zonation pattern along the elevation gradient [12,13]. This elevation gradient includes gradients in salinity, redox potential, soil N content, soil clay content, and soil organic matter [2]. However, elevation seems a major determinant for the establishment of all of them.

Unfortunately, increasing human populations have caused an extensive loss, degradation, and fragmentation of coastal ecosystems worldwide [14]. The main anthropogenic pressures on salt marshes include changes in hydrological and salinity regimes, physical deterioration or removal of coastal features, and urbanisation [15–17]. However, the main concern nowadays in any coastal ecosystem is the survival of the particular ecosystem in a climate change scenario [18]. Although there are numerous examples of modelling these responses in the literature [19–22], a major modelling limitation is still the low availability of adequate datasets. Remote sensing (RS) techniques are changing this scenario with the provision of high-resolution spatial data that will support a new generation of computer models [18].

Sea level rise is probably the major threat to tidal salt marshes [18]. Changes in sea level are equivalent to changes in elevation. Therefore, our capacity for monitoring changes in elevation and plant species distribution is going to be key for developing early warning management plans. RS techniques are a straightforward and cost-effective way to extract information since they provide recurring datasets in short time scales at affordable prices. Maps and assessments of coastal habitats have both benefited greatly from the use of RS techniques [1,23]. For example, the loss and degradation of salt marshes have been successfully evaluated by combining long-term LANDSAT imagery and numerical modelling [24]. Sentinel-2 and Landsat archives proved to be useful tools for tracking long-term salt marsh extent dynamics [25]. More recently, deep learning models, powered by Sentinel imagery, have improved the mapping of low and high salt marsh land cover in South Carolina coastal wetlands [26].

Differences in the biophysical properties of salt marsh plants generate spectral differences that can be detected using hyperspectral (HS) data [27–29]. However, airborne or satellite-based HS imaging has a spatial resolution (meter to tens of meters) that is probably not adequate to identify species distribution due to the considerable spatial heterogeneity in salt marshes [25,30]. Previous works on HS images from the EO-1 Hyperion satellite (30 m pixels) concluded that 30 m is insufficient spatial resolution to accurately distinguish between species with spectral similarities, such as *Sporobolus maritimus* (Curtis) P.M.Peterson & Saarela (previously named *Spartina maritima* (Curtis) Fernald) and *Sarcocornia* spp. A.J.Scott [31]. Combinations of Quickbird images (2.4 m resolution in the multispectral mode) with high spectral data from Hyperion (242 narrow bands and 30 m pixel) have been probed to map different salt-marsh species with acceptable accuracies in classification [32]. Pléiades images provide a robust and consistent global identification of the salt marsh zone. However, the application of its multispectral (MS) 2 m spatial resolution images proved to be insufficient for early assessment of the *Spartina anglica* C.E. Hubb. (currently *Sporobolus anglicus* (C.E. Hubb.) P.M.Peterson & Saarela) invasion, mainly due to the small size of the patches [33].

Nowadays, most of the RS techniques have developed integrable sensors into unmanned aerial vehicles (UAVs). For the intertidal zone, UAVs that fly up to 120 m altitude are suitable to identify spatial heterogeneity in microtopography, canopy height or greenness [34–36]. In addition, UAVs offer significant operational flexibility and minimal costs [34,37], allowing flight dates to be tailored. Therefore, UAVs may provide the necessary spatial and temporal resolution for mapping species distribution and their temporal changes. High-resolution RGB cameras integrated into UAVs have been previously employed in salt marsh environments. Farris et al. [38] used UAV-LiDAR to track the salt marsh shoreline, while Yan et al. [39] used UAVs to examine environmental factors influencing the ecological response of *Spartina alterniflora* Loisel. (currently *Sporobolus alterniflorus* (Loisel.) P.M.Peterson & Saarela). UAV-multispectral (UAV-MS) technology has

also shown utility in calculating indices of plant diversity and species richness in wetland communities [40]. Villoslada et al. [41] have shown that maps created from UAV-MS images provide useful data for managing plant communities and assessing the effects of climate change on coastal meadows. However, achieving a high-accuracy classification requires the use of a large variety of vegetation indices and the evaluation of the spectral properties of the training samples.

The use of UAV hyperspectral remote sensing (UAV-HS) in salt marshes combines the advantages of high spatial and spectral resolutions to capture the finer scale of spectral and spatial heterogeneity. UAV-HS has previously been used to classify desert steppe species [42], using spectral transformation to enhance species differences in vegetation indices with an overall accuracy of 87%. UAV-HS is able to detect salt stress in croplands and the accuracy performance of this technique improves in conjunction with other techniques [43]. Although several salt marsh vegetation species have undergone field hyperspectral investigation through field spectrometer measurements [28,44], this is likely the first work using a UAV hyperspectral sensor in a salt marsh environment.

Advances in RS technique applications require adequate study cases. Cadiz Bay offers an excellent system for assessing the capacity of UAV-HS in the discrimination of salt marsh vegetation species distribution. This tidal environment is home to the southernmost tidal salt marshes in Europe and is protected by numerous environmental protection figures at local and international levels. Cadiz Bay was designated a Natural Park in 1989 (Bahía de Cádiz Natural Park, PNBC, [45]) and RAMSAR site in 2002 (site no. 1265, [46]). The system is considered an important resting place on the migratory route of birds and is included in the Natura 2000 network (ES0000140, as SCA and SPA). Located between two seas and two tectonic plates, Cadiz Bay is a key place for biodiversity studies [47–50].

This work examines the potential of high spatial and spectral resolution UAV-HS data to accurately identify and differentiate the distribution of the salt marsh vegetation at the level of species. The specific goals are (1) to determine which is the appropriate UAV-HS dataset to map salt marsh vegetation; (2) to assess the separability of salt marsh interest classes; and (3) to estimate the elevation ranges of the detected species. These findings are expected to become a starting point for the early assessment of salt marsh degradation and help in the selection of areas for salt marsh rehabilitation or detection of the establishment and spread of alien species.
