Duccio Rocchini
email: duccio.rocchini@unibo.it

Maria J Santos

Susan L Ustin

Jean-Baptiste Féret

Gregory P Asner

Carl Beierkuhnlein

Michele Dalponte

Hannes Feilhauer

Giles M Foody

Gary N Geller

Thomas W Gillespie

Kate S He

David Kleijn

Pedro J Leitão

Marco Malavasi

Vítězslav Moudrý

Jana Müllerová

Harini Nagendra

Signe Normand

Carlo Ricotta

Michael E Schaepman

Sebastian Schmidtlein

Andrew K Skidmore

Petra Šímová

Michele Torresani

Philip A Townsend

Woody Turner

Petteri Vihervaara

Martin Wegmann

Jonathan Lenoir

The spectral species concept in living color

Background

Rapid environmental changes are occurring across the globe at small to large spatial extents due to the combined effects of climate change, land-use changes, and biological invasions [START_REF] Kreft | Global patterns and determinants of vascular plant diversity[END_REF]. Therefore, there is an urgent pressing demand for operational biodiversity monitoring systems that will improve our understanding of the repercussions of these drivers of changes on ecosystem functioning and lead to better ecosystem management [START_REF] Skidmore | Priority list of biodiversity metrics to observe from space[END_REF]. New approaches are required to obtain timely biodiversity data that are consistently and routinely measured across the Earth surface. Some of these needs are fulfilled by remote sensing information [START_REF] Schweiger | Plant beta-diversity across biomes captured by imaging spectroscopy[END_REF]. Over the past decades, remote sensing has opened possibilities for Earth observation from air and space, allowing us to monitor ecological change, primarily expressed by changes in vegetation cover, distribution, and functioning, which can be subsequently linked to drivers of change in space and time, from local to global scale [START_REF] Asner | Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation[END_REF][START_REF] Skidmore | Agree on biodiversity metrics to track from space[END_REF]. Recent technological advances in remote sensing data acquisition and processing now open new perspectives for monitoring changes in biodiversity at unprecedented details over large geographic areas, and ultimately over the entire Earth [START_REF] Luque | Improving biodiversity monitoring using satellite remote sensing to provide solutions towards the 2020 conservation targets[END_REF][START_REF] Randin | Modeling biodiversity in the Anthropocene using remote sensing in species distribution models[END_REF]. Furthermore, missions like the Surface Biology and Geology (SBG) by NASA (https://sbg. jpl.nasa.gov/, [START_REF] Cawse-Nicholson | NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms[END_REF] have been implemented to support the development of algorithms for exploiting spaceborne remotely sensed data and providing a relatively fast but accurate estimate of ecological properties in vast areas over time.

Spaceborne and airborne passive optical sensors relying on imaging spectroscopy (i.e., spectral remote sensing including multispectral and hyperspectral imaging) are a good example of the recent remote-sensing revolution in ecology [START_REF] Kwok | Ecology's remote-sensing revolution[END_REF]. By measuring information from most of the electromagnetic spectrum operable for Earth observation, imaging spectroscopy has demonstrated significant capabilities to detect and monitor the spatial distribution of plant communities, species, and traits (Asner & Martin, 2008;[START_REF] Schaaf | Mapping plant functional types at multiple spatial resolutions using imaging spectrometry data[END_REF][START_REF] Schweiger | How to predict plant functional types using imaging spectroscopy: Linking vegetation community traits, plant functional types and spectral response[END_REF][START_REF] Skowronek | Mapping an invasive bryophyte species using hyperspectral remote sensing data[END_REF]. The pixel reflectance in an optical image results from the integration of multiple interactions between light and matter, including vegetation and the surrounding environment (soil, atmosphere). Intrinsic properties of vegetation influencing this remotely sensed information correspond to biophysical and biochemical properties (i.e., traits) of leaves and the canopy that can be related to levels of ecological organizations such as ecosystems, communities, species, and potentially to the intraspecific trait of plant genotypes [START_REF] Blonder | Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)[END_REF][START_REF] Madritch | Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales[END_REF][START_REF] Pollock | Protecting biodiversity (in all its complexity): New models and methods[END_REF]. Spectroscopy has long been used to capture the characteristic absorption features of biochemical compounds of plants, which biologically corresponds to the phenotypic expression of some of the genes that describe individuals belonging to a given species (e.g., [START_REF] Jacquemoud | Leaf optical traits[END_REF]. These biochemical traits and their dynamics are linked to functional traits, opening access to the monitoring of ecosystem functions, processes, and services. Besides, imaging spectroscopy has already demonstrated capabilities for species discrimination in various types of ecosystems [START_REF] Fassnacht | Review of studies on tree species classification from remotely sensed data[END_REF]Féret & Asner, 2014;[START_REF] Skowronek | Mapping an invasive bryophyte species using hyperspectral remote sensing data[END_REF]. At the sub-organism level, biochemical properties estimated from observations at leaf and canopy scales [START_REF] Baret | Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties[END_REF][START_REF] Kokaly | Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies[END_REF][START_REF] Ollinger | Sources of variability in canopy reflectance and the convergent properties of plants[END_REF][START_REF] Serbin | Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature[END_REF] are described in commonly used radiative transfer models [START_REF] Féret | PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments[END_REF][START_REF] Jacquemoud | Estimating leaf biochemistry using the PROS-PECT leaf optical properties model[END_REF][START_REF] Torresani | Which optical traits enable an estimation of tree species diversity based on the Spectral Variation Hypothesis[END_REF]. The fine spectral resolution and spectral sampling interval on imaging spectrometers provide information to quantify key biochemical properties of vegetation such as leaf pigment (e.g., chlorophylls, carotenoids, anthocyanins), water, cellulose, lignin, nitrogen, phosphorous, and protein contents based on their specific light absorption characteristics [START_REF] Ewald | Analyzing remotely sensed structural and chemical canopy traits of a forest invaded by Prunus serotina over multiple spatial scales[END_REF].

The link between biochemical properties, functional attributes (morphological, physiological, and phenological traits), and taxonomic information is often implicitly assumed when performing spectroscopic analysis for biodiversity monitoring. Recently, the spectral species concept-an algorithm that clusters pixels from spectral images with a similar spectral signal (referred to as 'spectral species')-has brought attention (Féret & Asner, 2014; ROCCHINI ET AL.

10.1029/2022JG007026

3 of 13 signature in a remotely-sensed spectral image does not necessarily match with the actual distribution pattern of a given species but may rather reflect the spatial distribution pattern of a group of species sharing similar biochemical properties [START_REF] Woodcock | The factor of scale in remote sensing[END_REF].

The capacity to identify species is often explained by similarities in spectral signatures between individuals of the same species, and dissimilarities in spectral signatures between individuals of different species. There is no taxonomic marker in spectroscopy, but individuals of the same species are characterized by a limited set of biophysical and biochemical properties, allowing differentiation from individuals from other species. Despite significant scientific advances, development of automated retrievals of plant biochemistry, traits, and species identification from satellites across the globe and over time remains aspirational and more work is needed to accomplish this goal, especially for devising global monitoring of biodiversity change. Here, we aim at introducing the ecological functioning principles of the spectral species concept and refining its definition by a better linkage with field observations of plant species distribution data (i.e., presence-absence data) available from vegetation surveys. Finally, we will deeply face complexities, challenges, and opportunities associated with the use of this concept for remotely-sensed biodiversity monitoring, including species richness and evenness (alpha-diversity) as well as composition turnover (beta-diversity).

A Constellation of Optical Sensors to Explore the Spectral Domain of the Earth Surface

The current generation of Earth observing spaceborne and airborne sensors acquires highly-resolved images over a wide range of wavelengths in the solar (optical) and microwave domains of the electromagnetic spectrum [START_REF] Ustin | Current and near-term advances in Earth observation for ecological applications[END_REF]. The information measured by optical sensors operating in the visible (380-750 nm) and near to shortwave infrared (750-2,500 nm) regions largely corresponds to the region of solar reflectance from the Earth's surface, and are acquired for each individual pixel in an image. Image acquisition can occur at fine temporal resolutions of days to weeks and in a few cases even at multiple revisits during the day, but data availability and quality are strongly dependent on atmospheric conditions. Optical remote sensing systems include those that measure a few discrete spectral bands (multispectral imaging), such as: Some spaceborne sensors provide enhanced spectral capabilities and measure surface reflectance over hundreds of narrow contiguous spectral bands covering the solar radiation spectrum. These hyperspectral satellites are also called imaging spectrometers. The term hyperspectral emphasizes instruments measuring a large number of spectral bands while imaging spectrometer refers to the type of instrument used, that is, a spectrometer that measures bands across a spectral wavelength interval (spectrum) and produces a 2D array of pixels and spectra. Hyperspectral satellites have demonstrated strong potential for characterizing the chemical and physical structure of the Earth surface, with applications in mineralogy, soil sciences, and vegetation sciences [START_REF] Plaza | Recent advances in techniques for hyperspectral image processing[END_REF]. Such images reveal details that improve estimates of key vegetation properties and better discriminate between vegetation types, species, or even between genotypes of a given species when the spatial and spectral resolutions are sufficient to match individuals [START_REF] Blonder | Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)[END_REF][START_REF] Madritch | Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales[END_REF]. Several satellites hosting imaging spectrometers were recently launched, and a multitude of satellite missions are in preparation (see the review paper by [START_REF] Ustin | Current and near-term advances in Earth observation for ecological applications[END_REF]. These include DESIS, EMIT and HISUI on the International Space Station (ISS), the free flying platforms PRISMA and EnMAP (expected launch in 2022), along with NASA's and ESA's global monitoring imaging spectrometer missions SBG and CHIME (expected launches in the late 2020s).

Remote Sensing Tools for Monitoring Biodiversity

Species identification from the measurement of the absorptive and reflective characteristics of plants is based on the hypothesis that individuals from the same species share similar biochemical properties, leading to similar spectral characteristics measured at the pixel scale. However, individuals from the same species may also share similar biochemical properties with individuals from another species which may limit our ability to assign a given spectral signature to a given species. Besides, one needs to also consider phenological patterns, since remote sensing data is generally only acquired during specific periods of time (e.g., seasonal, in case of airborne data), or under specific conditions (e.g., health status), or with specific sensor information (e.g., spatial, spectral, and radiometric resolutions across all wavelengths), all of which may complicate species identification or discrimination. Thus, there are several challenges to identifying a taxonomically identified species with a unique spectral signature. Previous research has shown that minor shifts in plant development induced by the environment and its interaction with different plant genotypes (i.e., phenotypic variability) may result in co-location between vegetative, flowering, fruiting, or senescent stages, each of which expresses different biophysical and biochemical properties. Hence, multiple spectral signatures corresponding to multiple biochemical traits may exist for the same species. Although adding complexity, these problems have solutions. For example, fast growing annual species and facultative annuals, like the invasive Water Hyacinth (Eichhornia crassipes), are often found in different growth stages and detection requires several spectral signatures to account for the different growth stages that are later combined for mapping the focal species of concern [START_REF] Khanna | An integrated approach to a biophysiologically based classification of floating aquatic macrophytes[END_REF]. Further, spectral discrimination among species is difficult, for example, when several species in an ecosystem may share a suite of traits (i.e., strong overlap in the trait space) due to climate constraints and environmental filtering. This happens when trait combinations make individual species phenotypically similar (e.g., grass species in grassland habitats, as shown in diversity studies by [START_REF] Gholizadeh | Detecting prairie biodiversity with airborne remote sensing[END_REF][START_REF] Gholizadeh | Multi-temporal assessment of grassland α-and β-diversity using hyperspectral imaging[END_REF], or when different combinations of traits result in similar spectral signatures [START_REF] Kokaly | Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies[END_REF][START_REF] Ollinger | Sources of variability in canopy reflectance and the convergent properties of plants[END_REF]. Additional empirical evidence is thus necessary to contextualize and resolve these questions [START_REF] Andrew | The role of environmental context in mapping invasive plants with hyperspectral image data[END_REF], such as the data provided by field data collections, experimental studies, and modeling, to achieve full ecological understanding of information from the satellite data to monitor biodiversity.

From Optical Types to the Spectral Species Concept

Just as traditional biodiversity theory focuses on differences between individuals to assign individuals to different species in order to assess species richness, diversity measured with imaging spectrometer data from spaceborne and airborne optical sensors is based on pixels, each with its own spectral information (i.e., reflectance) (Rocchini et al., 2021a). The term spectral signature is more specific than spectral information and usually applied to a specific type of surface (soil, vegetation, water), to a specific material (i.e., traits like chlorophyll concentration), or to a specific level of biological organization (genotype, population, species, stand, community, ecosystem). Spectral diversity corresponds to the spatial variation of spectral information: it is tightly related to the notion of (multivariate) variation among species traits [START_REF] Rocchini | Measuring β-diversity by remote sensing: A challenge for biodiversity monitoring[END_REF] which is the basis of functional diversity in classical ecological theory. In other terms, spectral diversity reflects-at least to a large extent-diversity in community functioning based on the assortment of functional traits in the community, irrespective of the species/ individuals that possess these traits [START_REF] Matson | Functional richness, functional evenness and functional divergence: The primary components of functional diversity[END_REF][START_REF] Petchey | Functional diversity (FD), species richness and community composition[END_REF]. Therefore, spectral diversity is conceptually closer to the notion of functional diversity than to the concept of species or taxonomic diversity. For this reason, it is important to keep in mind that the spectral signature of a given pixel unit in a spectral image cannot directly be assigned to a given individual belonging to a specific taxonomic entity (i.e., the spectral species concept). First of all, the pixel size of the image may not match with an actual individual in the field but may contain several individuals belonging to the same species (i.e., a population) or several individuals belonging to different species (i.e., a community) such that the same species could be involved in different spectral signatures or 'optical types' (Figure 1). Additionally, the same suite of biochemical traits (e.g., foliar nutrient contents) may show extremely high spectral variability depending on the health status of the focal plant species such that different optical types could be assigned to the same species depending on its status: healthy versus stressed (Figure 1, panels f,g,h). Hence, prior to defining the spectral species concept, one needs to identify the different optical types occurring within a given spectral image and then assess how each optical type overlaps with the concept of taxonomic species so that one can assign an optical type to a specific species (i.e., the spectral species concept) or a group of species. To confirm that the spatial distribution pattern of pixels sharing a similar spectral signature (i.e., an optical type) can be directly assigned to the spatial distribution pattern of individuals from a single species (i.e., the spectral species), one needs ground-truth data on species distribution (i.e., presence-absence data from vegetation surveys) from within the spectral image.

Depending on pixel size and size of individuals, pixels can cover multiple individuals or parts of an individual, or stands of one species or mixed stands (Figure 1). In forests, where the spectral species concept was born, a match between spectral and biological species was furthered by the fact that pixels have a higher chance to belong to one species alone (Figure 1, panels a to e, with small pixel size). This does not apply to all situations. Grassland ROCCHINI ET AL. 10.1029/2022JG007026 5 of 13 communities, for example, pose a greater challenge because pixels regularly contain several species and optical types can at best be linked to entire communities (Figure 1, panels f to h with any pixel size). These, however, are even less clearly delimited objects than species.

Spectral Species Translating Spatial Distributions of Optical Types Into Diversity Metrics

Various analytical approaches have been developed to take advantage of the spectral information in the optical domain and investigate different dimensions of biodiversity (see the review paper by [START_REF] Rocchini | Measuring β-diversity by remote sensing: A challenge for biodiversity monitoring[END_REF]. These approaches have demonstrated that it is possible to map and understand functional and taxonomic diversity through space and time, achieved through methodological approaches that differ in their focus, either statistical or process-based. They include data transformation [START_REF] Rocchini | Measuring Rao's Q diversity index from remote sensing: An open source solution[END_REF], feature selection and dimensionality reduction [START_REF] Feilhauer | Combining isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape[END_REF][START_REF] Feilhauer | Optical trait indicators for remote sensing of plant species composition: Predictive power and seasonal variability[END_REF], and machine learning techniques [START_REF] Kitzes | Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches[END_REF].

The number of plant optical types proposed by [START_REF] Ustin | Remote sensing of plant functional types[END_REF] is consistent with the Spectral Variation Hypothesis (SVH) [START_REF] Palmer | Quantitative tools for perfecting species lists[END_REF][START_REF] Rocchini | Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges[END_REF] which states that ecosystem heterogeneity is associated with high spectral variability. In other words, increased environmental heterogeneity provides more niches for species to co-occur in geographical space with an expected increase in local species richness [START_REF] Palmer | Quantitative tools for perfecting species lists[END_REF]. For optical types, we therefore expect greater diversity where greater spatial environmental heterogeneity occurs.

Spectral species-that is, the number of spectrally distinct classes that approximate species-are based on the hypothesis that proper processing of images allows discrimination among species, groups of species, or functional groups. Spectral species aim to discretize remotely-sensed information into groups of pixels through unsupervised clustering. The notion of spectral species was successfully applied for mapping tropical biodiversity using airborne Figure 1. Some potential scenarios that can happen in optical remote sensing of vegetation canopies. The graph shows sources of variation in the relationship between species and optical types. Plants of different species might belong to different optical types, but many other situations can also be found. Optical types can be related to information of interest (e.g., species or plant traits) or to irrelevant pattern (e.g., shadows, depending on the research question). Scenario (a) represents a stand with individuals of only one single species, with a similar reflectance. In scenario (b) individuals of two species have a similar reflectance; hence they would be grouped in the same spectral species. This is further complicated once mixing individuals belonging to the same taxon but to different optical types (c) or individuals of multiple species belonging to different optical types that do not follow the species boundaries (d). What many would hope for is that plants of different species belong to different optical types, which may happen (e). Finally, the same plant individual can consist of different optical types showing different spectral properties in for example, young versus old leaves, shadow and light, or differences in health conditions. This intra-individual mixing property will be related to all of the previous cases (f)-(h). Note that a stand or individual can pass through several of these scenarios in time (intra and interannual variability).

ROCCHINI ET AL. 10.1029/2022JG007026 6 of 13 high spatial resolution imaging spectroscopy (Féret & Asner, 2014), under the assumption that the majority of pixels did not contain plant mixtures and that individual pixels could be assigned to a species, given that spectral variance among pixels meet statistical criteria and that the average tree crown size approximates the pixel size. [START_REF] Underwood | Mapping nonnative plants using hyperspectral imagery[END_REF][START_REF] Underwood | A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California[END_REF] and [START_REF] Thorp | Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA)[END_REF] evaluated information content for species mapping from different spatial and spectral resolutions, concluding that high spectral resolution contributed substantially more information for species mapping than higher spatial resolution. Hence, spectral species and optical types are conceptually equivalent in their purpose to discriminate among optical entities acquired from an image, but the appropriateness of the terminology may vary with the type of ecosystem and spatial resolution of the sensor.

A spectral species is then a set of pixels having similar spectral properties that can be used as a proxy for ecologically relevant taxonomic or functional groups, and eventually inventoried to calculate biodiversity metrics like alpha-diversity (e.g., Shannon's H, Simpson's D, Rényi's H) and beta-diversity (e.g., Bray-Curtis dissimilarity or Rao's quadratic entropy) across a landscape (Box 1). Clustering approaches similar to the spectral species framework have been successfully used to map plant species diversity over very different habitats and geographical regions: from African savannas [START_REF] Baldeck | Landsape-scale variation in plant community composition of an African savanna from airborne species mapping[END_REF] to grasslands in the Platte River ecosystem near Wood River, Nebraska, USA [START_REF] Gholizadeh | Multi-temporal assessment of grassland α-and β-diversity using hyperspectral imaging[END_REF], and from the Peruvian Andes-Amazon tropical forests (Féret & Asner, 2014) to old-growth, secondary, and artificial forests of the Shennongjia National Forest Natural Reserve in China [START_REF] Zhao | Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China[END_REF].

Spectral species combined with functional trait estimation contribute to biodiversity understanding by characterizing the morphological (canopy architecture, gap fraction, etc.) and functional traits, that define the functional role of a species in an ecosystem, for example, carbon-capture strategies by resource acquisition or resource conservation strategies (like drought responses), that then provide a basis to connect spectral species with the taxonomic species that exhibit these trait assemblages. Therefore, spectral species provide a basis to connect with biological species and eventually explore spatial distribution of trait assemblages, their evolution in time, and their linkages to environmental or human factors.

Box 1. The Spectral Species Algorithm at Work

The spectral species concept is grounded in an algorithm which is now readily available under a free and open source R package in GitHub (https://github.com/jbferet/biodivMapR) named biodivMapR [START_REF] Féret | biodivMapR: An R package for α-and β-diversity mapping using remotely sensed images[END_REF], which is able to produce α-and β-diversity maps starting from the detection of spectral species based on the optical properties of vegetation in the field.

Starting from a multi-or hyper-spectral image, a spectral transformation like a Principal Component Analysis (PCA) is performed to reduce the dimensionality for further calculations (Figure 2). Based on those principal components explaining most of the variance, k-means clustering is applied to a random set of pixels in order to detect group of pixel with the same spectral reflectance, possibly related to single groups of individuals/canopies in the field that are sharing similar traits and thus that are likely to be phylogenetically related (spectral species concept). The detection of spectral species is applied backward to the whole map defining a membership probability of each pixel to a certain spectral species, as based on its spectral euclidean distance from the centroid of the previously defined clusters. A single final spectral species map can be attained by using the maximum probability for each pixel to attain to a certain spectral species. The spectral species map is further divided into elementary spatial units with a higher pixel dimension (hereafter simply units), in which calculation of α-and β-diversity can be performed, leading to crucial information on both local diversity and turnover. In order to attain α-diversity, Shannon's H' is calculated for each unit, while for β-diversity calculation, Bray-Curtis dissimilarity is considered among all the possible pairs of units. The α-diversity map can be shown by directly taking into account Shannon's H' for each unit and reporting it in a final resampled map. Rather, the β-diversity map needs a further step to pass from a spectral distance matrix (see [START_REF] Rocchini | Distance decay in spectral space in analysing ecosystem β-diversity[END_REF] to a 2D spatial representation, namely the application of Non metric Multidimensional Scaling (NMDS), by deciding the final number of reduced dimensions (in this case three in order to compose an RGB image). The final result will be a unitless β-diversity map in which different colors represent differences between communities in space.

Complexities and Challenges

Many of the uncertainties of the spectral species concept apply generally to remotely sensed classified images, which we will not review in detail because they are extensively addressed elsewhere (e.g., [START_REF] Kun | A review of classification method of remote sensing imagery[END_REF][START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF][START_REF] Rocchini | Are landscapes as crisp as we may think?[END_REF]. The spectral species method of identifying spectral species with distinctive traits facilitates identification of rare species that occupy few pixels in an image. Rare and endemic plants by definition do not grow in large colonies of many individuals and generally have restricted distributions. But they are of importance to biodiversity mapping because there are far more rare species than frequent species and they are often concentrated in areas of high biodiversity, especially in hotspots [START_REF] Griggs | The ecology of rare plants[END_REF][START_REF] Medail | Hot-spots analysis for conservation of plant biodiversity in the Mediterranean Basin[END_REF][START_REF] Ricotta | Patterns of native and exotic species richness in the urban flora of Brussels: Rejecting the 'rich get richer' model[END_REF]. In terms of biodiversity they may represent species with unique morphological features and genetic richness [START_REF] Joppa | Biodiversity hotspots house most undiscovered plant species[END_REF][START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. Other aspects of uncertainty around the operationalization of the spectral species concept are similar to many problems encountered in vegetation classification systems, such as whether rare species are retained in the classified data, whether the classes are realistic in terms of field measured data, and how well the method deals with mixed pixels (multispecies pixels, species with multiple phenotypes). to maximize signal corresponding to biological patterns on forested areas and discard noisy components. Spectral species are defined for each pixel by applying an unsupervised k-means clustering on the spectral space defined by selected components (c). In this phase, a field survey recognition based on in situ data is crucial to define the number of singular spectral signatures (spectral species) expected. The spectral species map is divided into elementary spatial units and the spectral species inventory is performed for each spatial unit, by further calculating Shannon's H and Bray-Curtis metrics to derive (d) alpha-(ranging here from minima to maxima from black to blue, green and red) and (e) beta-diversity (in which colors represent differences among spectral species) maps, respectively.

Spatiotemporal Mismatches

Some of the complications that can make the spectral species concept difficult to handle are shown in the aforementioned Figure 1, where differences in reflection do not necessarily adhere to species boundaries. In fact, several factors determine whether the spectral species concept predicts the current number of species in the area under study. First, more fundamentally it depends on how the taxonomic species are determined, for example, whether related taxa are characterized by 'splitting' or 'lumping' criteria, leading to more or fewer species. There are also cases of cryptic diversity, that is, species that are not morphologically or phenotypically distinct, but having a distinct genetic make-up. Thus, the actual number of detected species approximates the true number. Second, even if species are clearly assigned, the spectral species concept requires the measurement of optically active traits, which may not be the traits that differentiate species, or-because of trade-offs (based on the species functional strategy) or selective pressures (resulting from competition, human use, and other types of ecological interactions)-they may not be expressed at the time of measurement. Third, community composition and abundance interact with what is detectable-as the sensor primarily measures top of canopy dominant species, which may identify important ecosystem functions but miss much of the diversity.

The spectral species concept, as with most remotely sensed measures, is dependent on the temporal dimension to which it is applied. For instance, measuring the phenological differences among co-occurring plant species throughout the season would improve species detection. Including seasonal changes in the analysis may improve detection of understory species when the deciduous overstory is dormant. Multitemporal datasets can form multi-seasonal spectral signatures to account for seasonal changes in a species (e.g., [START_REF] Somers | Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis[END_REF] and multitemporal spectral libraries are under development [START_REF] Dudley | A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients[END_REF]. The spectral species concept has shown consistency in identifying taxonomic species and traits from other taxonomic species and their traits. Detecting species in mixed pixels depends on the sensor traits listed above and the magnitude of the differences among the spectral signatures of the species in the pixel. Species with different functional strategies that are distinguishable in optically active traits will be easier to identify than species with more similar trait assemblies, but which are not optically active. Differences in traits due to leaf types (evergreen or deciduous, needle leaf, or broadleaf) create considerable differences in leaf reflectance and timing the date of data acquisition to maximize phenological differences generally always improves the ability to detect sub pixel species. The spectral species concept has yet to be widely tested over the global range of ecosystems and environmental conditions so there is a need to determine its performance and limitations at these scales.

Ecological Issues Behind Spectral Species

The spectral species concept was initially developed to allow computation of diversity indices usually computed from species inventories, but with pixels used instead of species. It did not intend to directly estimate 'absolute' species richness from the spectral clustering, but rather hypothesized that relative species richness and higher-level diversity indices integrating richness and abundance or dissimilarity over space, could be estimated and compared within a limited spatial extent corresponding to an airborne imaging spectroscopy acquisition. Even in a very unlikely situation of perfect spectral discrimination among species in a remotely sensed acquisition, the predicted number of species may be smaller than the actual species count due to problems related to the potential similarity of traits among multiple species, especially when the growth forms and phenology are similar. The predicted number of species may also be higher than the actual species count if individuals from the same species show phenological shifts. The naming of spectral classes requires matching the taxonomic species information with the spectral information. If the taxonomic naming was based on extreme clumping of groups into fewer taxa or the splitting of taxa into increased number of taxa, the predicted spectral species may differ from the number of biological taxa identified at a site. Perhaps knowledge of the predicted number of spectral species might result in rethinking the criteria for recognition and taxonomic revisions, or similarly knowledge of the species may require rethinking of how spectroscopy data were analyzed.

While ecological communities have a variety of either dominant, co-dominant, and rare species, the latter contribute a lot to the highest diversity measurable on Earth. This is a problem for remote sensing approaches based on clustering, as these algorithms often delete clusters with just a few data points (minimum class size or minimum/ maximum number of classes may be selected in the setup), thus there is a tendency to lose rare and endemic species likely fundamental for biological diversity and ecosystem functions. A 'continuous surface' analytical method that directly addresses this problem is the widely used 'Multiple Endmember Mixture Model' (MESMA, [START_REF] Roberts | Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models[END_REF], which models each pixel from a range of 'endmembers' (also referred to as 'pure spectra') identified statistically from the convex hull of the data hypervolume. The model can retain classes with small numbers of endmembers depending on user criteria but may have the difficulty in assigning class labels when the variation is continuous (e.g., across a natural continuum or from one habitat type to another, [START_REF] Schmidtlein | Mapping the floristic continuum: Ordination space position estimated from imaging spectroscopy[END_REF].

Environmental heterogeneity may also affect the accuracy of the spectral species methodology [START_REF] Schmidtlein | The spectral variability hypothesis does not hold across landscapes[END_REF]. Disturbances may increase or decrease biodiversity, with a net zero effect on heterogeneity. There are several examples (e.g., forest gap openings, fire spread, and urban development) that can increase heterogeneity without an increase overall biological diversity. Spectral species should detect these changes as differences in the assemblage and range of traits being measured, although there is uncertainty about the least detectable magnitude of change. Therefore, we expect remotely sensed spectral species to provide a first-order exploratory tool to, at least detect areas that are suspected of hosting a high number of species or, alternatively, identify areas that should host larger numbers of species (based on independent criteria) but don't. From this point of view, in situ data would greatly improve the classification, validate its accuracy, and provide a basis to investigate and identify sites that do not match a priori expectations [START_REF] Foody | The sensitivity of mapping methods to reference data quality: Training supervised image classifications with imperfect reference data[END_REF].

The R package (biodivMapR) dedicated to applications of the spectral species algorithm [START_REF] Féret | biodivMapR: An R package for α-and β-diversity mapping using remotely sensed images[END_REF] on raster data requires the number of spectral species be defined by the user. The method is not intended to estimate the absolute number of species from remote sensing, as this problem is highly scale and context dependent as explained earlier. The definition of the optimal number of clusters in a dataset is a specific problem that the spectral species framework was also not intended to solve. Adding a method to determine the number of clusters corresponding to species richness over a full dataset would definitely contribute to the generalization of the spectral species framework over various ecosystems. The number of species included in a remote sensing acquisition is not generally known a priori for most of the Earth's ecosystems. However, there are statistical approaches to estimate the number with enough accuracy that realistic values could be produced [START_REF] Chang | Estimation of number of spectrally distinct signal sources in hyperspectral imagery[END_REF][START_REF] Gholizadeh | Multi-temporal assessment of grassland α-and β-diversity using hyperspectral imaging[END_REF], as well as deeply rooted ecological principles based on species dispersal, biogeography, landscape ecology (Rocchini et al., 2021b).

There are questions on whether this method will continue to be successful at the spatial scales of satellites, where multiple species within pixels cause spectral mixing, and under conditions of sparse vegetation where mixed pixels can include plants, soils, plant litter, geological minerals, water, ice, and man-made materials. This will be resolved with the use and applications of the new generation of hyperspectral satellites. The various spaceborne imaging spectrometers (see the review paper by [START_REF] Ustin | Current and near-term advances in Earth observation for ecological applications[END_REF] that are available now (DESIS, EMIT, EnMAP, HISUI, PRISMA) or in the next few years (e.g., CHARM, SBG) all have 30 m pixels. These imaging spectrometers will provide ample opportunities to examine how well the spectral species concept scales up and across ecosystems.

In addition to passive optical sensors, reviewed in this manuscript, LiDAR and radar active sensors might complement observations with the capacity to penetrate the uppermost layer of vegetation and provide information on canopy structure, while being less sensitive to atmospheric perturbations than passive optical sensors, Asner et al., 2008;[START_REF] Bergen | Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions[END_REF][START_REF] Lenoir | Unveil the unseen: Using LiDAR to capture time-lag dynamics in the herbaceous layer of European temperate forests[END_REF][START_REF] Mulatu | Linking Terrestrial LiDAR Scanner and Conventional forest structure measurements with multi-Modal satellite data[END_REF][START_REF] Simonson | Use of an airborne lidar system to model plant species composition and diversity of Mediterranean oak forests[END_REF][START_REF] Zhao | Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China[END_REF]. For instance, the GEDI LiDAR mission on the ISS is measuring at high vertical and spatial resolutions the distribution and height of global woody vegetation to provide structural information, including understory data, for biodiversity estimates. Besides, the NISAR and Biomass radar missions (launches in 2023) will monitor global patterns of biomass, disturbances, and impacts on biodiversity. Together, advances in the spatial, spectral, and temporal dimensions of imagery offer immense data streams that can be harnessed to better understand the processes of biological functioning, and to systematically map and monitor ecosystem changes from local to regional to global scale.

Outlook

In this manuscript we focused on the need to model biodiversity from hyperspectral remote sensing and field data to calibrate such models. A huge amount of field (free) data is actually available (e.g., sPlotOpen, Sabatini et al., 2021, GBIF), but creativity for mapping species diversity from hyperspectral imagery should be reinforced. From this point of view, the spectral species concept is expected to make a major contribution in mapping ROCCHINI ET AL. 10.1029/2022JG007026 10 of 13 and analysis of hyperspectral satellite data to produce remote sensing based essential biodiversity variables (RS-EBVs, see [START_REF] Skidmore | Agree on biodiversity metrics to track from space[END_REF][START_REF] Skidmore | Priority list of biodiversity metrics to observe from space[END_REF]. Even if the predicted spectral species do not precisely identify the number and identity of taxonomic species in an image, it will provide a first-order exploratory tool to detect areas of low to high species diversity. Such approaches will require a better understanding of the range of conditions under which the spectral species concept can operate. In Section 2 we highlight some limitations for applications of the concept, but many of these limitations should be seen as new opportunities for research. These include, among others, identification of additional spectral characteristics that can be captured and what additional traits, and thus more species will be detected. We are aware that very small absorption features are present in plant spectra and are statistically detectable but we do not know the physical basis of the biochemical/biophysical material that is absorbing this energy, highlighting an important avenue of research for the future. There is an ever-growing number of new traits reported from imaging spectroscopy research, ranging from phenolic and isoprene compounds, non-structural carbohydrates, fiber content [START_REF] Ely | Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-Nitrogen status[END_REF][START_REF] Serbin | Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species[END_REF][START_REF] Singh | Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties[END_REF], essential nutrients including potassium, phosphorous, and calcium [START_REF] Asner | Quantifying forest canopy traits: Imaging spectroscopy versus field survey[END_REF][START_REF] Ely | Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-Nitrogen status[END_REF] to RuBP carboxylation (Vcmax) and regeneration (Jmax) [START_REF] Rogers | A roadmap for improving the representation of photosynthesis in Earth system models[END_REF][START_REF] Serbin | Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature[END_REF][START_REF] Serbin | Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species[END_REF][START_REF] Wu | Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests[END_REF]. Such information is retrieved from measurements enabled by today's imaging spectrometers, which typically have spectral resolutions of 10 nm spectral bands cross the solar spectrum for a total of around 200-250 bands, some have 3-5 nm in the visible-near-infrared wavelengths and 5-10 nm in the shortwave infrared region. New satellite sensors under development (Flora on the Flex platform and GeoCarb) are designed to detect chlorophyll fluorescence with 1-3 nm narrow bands in the wavelength regions where chlorophyll fluoresces near the oxygen bands.

The spectral species concept is based on the principle that the variability in a spectral data cube is sufficient to identify a suite of traits in a pixel. In these cases, pixels are composed of multiple species with an assortment of different traits. The spectral species concept has been broadened to that of "spectranomics", which is "an approach to conceptually and geographically link plant canopy species and their functional traits to their spectral-optical properties" [START_REF] Asner | Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing[END_REF]. Broadening "spectranomics" has benefit for biodiversity assessment across different organizational levels, another area in which the spectral species concept could contribute.

While differences in traits identify different species, they can also describe patterns at different levels of ecological organization such as habitats, communities, and ecosystems. In [START_REF] Ustin | Remote sensing of plant functional types[END_REF], the concept of "plant optical types" is based on their spectral attributes, without any direct reference to species or traits. By relating these optical types to specific traits (e.g., leaf and canopy resources allocated to productivity), then optical types acquire the definition of plant functional types. Just as actual plant species change, the spectral species concept must be dynamic and change. As the range of applications increase, the range of spectral patterns will increase (e.g., including phenological events like mass flowering or senescent states) [START_REF] Poyry | Predictive power of remote sensing versus temperature-derived variables in modelling phenology of herbivorous insects[END_REF].

Understanding the interactions between biodiversity and ecological/environmental drivers is difficult [START_REF] Kreft | Global patterns and determinants of vascular plant diversity[END_REF]. From this point of view, collecting exploratory remote sensing data on environmental heterogeneity across large geographical extents is relatively simple and combining that information with changing patterns of functional traits could improve species identification, mapping, and monitoring of potential diversity hotspots [START_REF] Asner | Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation[END_REF][START_REF] Skidmore | Agree on biodiversity metrics to track from space[END_REF].

As biodiversity research expands to global ecosystems, there are many questions unresolved. Both ecological sciences and Earth observation technologies are still in maturing phases of development. In this paper, we clarified the links between the spectral species concept, optical types and optical traits, and their analogy with ecological dimensions including species, functional types and functional traits, as well as the mechanistic link between biophysical properties of vegetation and what is usually expressed as spectral signatures, corresponding to species. The relevance of discrete approaches, and their complementarity with continuous approaches was also highlighted.

As stressed in this paper, important advances have been made in understanding how the spectral signature relates to biodiversity and where there is untapped potential to further disentangle these connections. While one should not overestimate the capacity of remote sensing to directly estimate biodiversity from space [START_REF] Skidmore | Priority list of biodiversity metrics to observe from space[END_REF], remote sensing captures patterns of reflected or emitted electromagnetic radiation that are driven by biophysical and biochemical properties of vegetation (i.e., patterns dependent on the optical properties of plants that are the phenotypic expressions of their fitness strategies). There are a wide range of scientific disciplines that include all ecological subdisciplines from the population to the global biosphere, biology, soils, hydrology, evolution and phylogenetics, optics, mathematics, statistics and informatics, and engineering that are necessary to understand, interpret, refine, and improve remote sensing research. To actually develop and verify methods to identify and monitor global patterns of biodiversity, it will truly take "more than a village" but instead an engaged and committed international contingent of scientists, social scientists, citizen scientists, engineers, policy makers, land owners, farmers, and more, to shorten the long road ahead.

Authors' Contributions

DR, MJS, SLU, JBF, and JL led the writing of this manuscript. DR, JBF, GPA, MD, MJS, and SS analyzed the data providing the output for the figures of this manuscript. SS conceived and crafted Figure 1. All authors contributed critically to the development of the concept of this manuscript and to the writing of the draft, and they gave final approval for publication.

  (a) the Thematic Mapper on USGS Landsat; (b) MERIS (Medium Resolution Imaging Spectrometer) on the ESA Envisat; (c) MODIS (Moderate Resolution Imaging Spectrometer) on the NASA Terra and Aqua platforms; and more recently (d) the Sentinel-2 satellites of the ESA Copernicus program. These instruments provide open access data describing the Earth surface at frequent intervals and are complemented by commercial satellites that have high spatial resolution like IKONOS, SPOT, Quickbird, WorldView, and more recently the Planet constellation.

Figure 2 .

 2 Figure 2. Box 1 Figure -The spectral species algorithm phases. The original image was acquired with the CAO AToMS imaging spectrometer during an airborne campaign over the CICRA experimental site (Amazonian Peru) (https://www.amazonconservation.org/about/mission-vision/cicra-station/). The first image (a) corresponds to the RGB representation of an imaging spectroscopy subset. A standardized PCA is applied on (a) and a reduced set of components is selected (b)to maximize signal corresponding to biological patterns on forested areas and discard noisy components. Spectral species are defined for each pixel by applying an unsupervised k-means clustering on the spectral space defined by selected components (c). In this phase, a field survey recognition based on in situ data is crucial to define the number of singular spectral signatures (spectral species) expected. The spectral species map is divided into elementary spatial units and the spectral species inventory is performed for each spatial unit, by further calculating Shannon's H and Bray-Curtis metrics to derive (d) alpha-(ranging here from minima to maxima from black to blue, green and red) and (e) beta-diversity (in which colors represent differences among spectral species) maps, respectively.

Acknowledgments

We are grateful to the handling editor and to two anonymous reviewers who

Data Availability Statement

Data used in this paper are part of the Forest Plots network and can be accessed upon request at forestplots.net. The data used in this paper are part of the CICRA experimental site (Amazonian Peru) and can be accessed upon request at: https://www.amazonconservation.org/about/mission-vision/cicra-station/.

provided useful insights on a previous version of this manuscript. This research was carried out, in part, at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). DR and DK were partially supported by the H2020 Project SHOWCASE (Grant agreement No 862480). DR was also partially supported by the H2020 COST Action CA17134 'Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO)'. JBF was funded by the Agence Nationale de la Recherche (France) (BioCop project-ANR-17-CE32-0001).


Noëmie Delaplanque
email: noemie.delaplanque@univ-eiffel.fr

Sylvain Chataigner

Laurent Gaillet

Marc Quiertant

Karim Benzarti

Arnaud Rolland

Xavier Chapeleau

A Saravia Flores

Durability in Alkaline Environment of a Fiber Optic Sensor Bonded at the Surface of Reinforcing Bars for Distributed Strain Measurements in Concrete Structures

Keywords: Distributed optical fiber sensor, strain measurements, FRP rebars, concrete, bond behavior, alkaline environment, durability. EWSHM2022, 196, v6 (final): 'Durability in alkaline environment of a fiber optic sensor

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Distributed Optical Fiber Sensor (DOFS) embedded in a host material or bonded at the surface of a host structure are suitable for monitoring strain distributions. Paired to an optoelectronic interrogator based on Rayleigh scattering, this sensing technology enables obtaining distributed stain profiles with high spatial resolution. It is a very promising method for monitoring strain transfer processes at the interface between concrete and (steel or composite) reinforcing bars (rebars). So far, several authors have already investigated the mechanical behavior of reinforced concrete (RC) structures using optical fibers (OF) bonded at the surface of steel rebars [START_REF] Davis | Distributed fiber Bragg grating strain sensing in reinforce concrete structural components[END_REF][START_REF] Guo | Experimental study on strain and deformation monitoring of reinforced concrete structures using PPP-BOTDA[END_REF][START_REF] Quiertant | Deformation monitoring of reinforcement bars with a distributed fiber optic sensor for the SHM of reinforced concrete structure[END_REF]. This technique was also successfully applied to analyze strain distribution in Fiber-Reinforced Polymer (FRP) rebars embedded in concrete and subjected to pull-out tests [START_REF] Rolland | Experimental investigations on the bond behavior between concrete and FRP reinforcing bars[END_REF][START_REF] Rolland | Analytical and numerical modeling of the bond behavior between FRP reinforcing bars and concrete[END_REF]. Besides, this instrumentation opens interesting perspective for assessing the long-term durability of the bond between Glass FRP (GFRP) rebars and concrete, since GFRPs are prone to chemical degradations in alkaline environments. However, the durability of the DOFS instrumentation itself should be investigated in a preliminary stage, in order to evaluate the reliability of strain measurements in the long term.

If the durability of DOFS bonded at the surface of concrete or embedded within concrete has been investigated in a recent work [START_REF] Alj | Environmental Durability of an Optical Fiber Cable Intended for Distributed Strain Measurements in Concrete Structures[END_REF], to the author's knowledge, the ageing behavior of DOFS bonded to the rebars of RC structures is still an open issue. In the present study, an experimental program was proposed to investigate such behavior. Steel rebars were equipped with bonded DOFS, and then immersed in an alkaline solution at various temperatures. Tensile tests were performed on these instrumented rebars both in the unaged reference state and after exposure (specimens were loaded in the elastic domain of the steel material, and DOFS strain profiles were collected at selected load level). As it is expected that the mechanical behavior of steel doesn't change over ageing, any evolution of the strain profiles of exposed specimens would be discussed in terms of alteration/degradation of the DOFS instrumentation itself. This paper details the experimental program, reports the first results of this research and draws some preliminary conclusions.

2

Materials and experimental methods

Materials

The tests were conducted on two steel rebars of diameter 10 and 12mm, respectively. Each rebar was equipped with an OF sensor. Based on previous works of Quiertant et al. [START_REF] Quiertant | Deformation monitoring of reinforcement bars with a distributed fiber optic sensor for the SHM of reinforced concrete structure[END_REF] and Chapeleau and Bassil [START_REF] Chapeleau | A General Solution to Determine Strain Profile in the Core of Distributed Fiber Optic Sensors under Any Arbitrary Strain Fields[END_REF], a single-mode commercially available OF with a polyimide primary coating was used. For the installation of this OF, a U-groove with a depth of 1 mm, and a length of 220 mm for the first rebar and 300 mm for the second, was previously drilled along each rebar. Then, the OF was placed inside the Ugroove and sealed with a two-component epoxy adhesive. The X60 epoxy was used for bonding the OF to the 12mm diameter steel rebar, while the X120 epoxy adhesive was used for the other rebar. Both adhesives are bi-component epoxy systems commercialized by HBM Company (Darmstadt, Germany).

In the proposed configuration, the adhesive layer acts as a coating that protects the OF from the surrounding environment, especially against chemical attacks (alkali attack in a concrete environment for instance) or against mechanical damage. However, any ageing induced evolution of this adhesive material may affect the bond characteristics of the OF/epoxy interface and/or the steel/epoxy interface, and alter the properties of the bulk epoxy material as well. Each of these phenomena, and of course the damage of the OF itself, would have detrimental effects on the strain response of the DOFS instrumentation. The durability test proposed in the following intends to verify whether the sensor remains reliable after ageing, or whether one/several of the previous degradation mechanisms may affect the accuracy of strain measurements.

Mechanical tests and DOFS measurements

To verify if the DOFS instrumentation is resistant to alkaline environments at temperatures up to 60 °C (these ageing conditions are commonly used for durability studies on FRP rebars [START_REF] Alj | Environmental Durability of an Optical Fiber Cable Intended for Distributed Strain Measurements in Concrete Structures[END_REF][START_REF] Rolland | Accelerated Aging Behavior in Alkaline Environments of GFRP Reinforcing Bars and Their Bond with Concrete[END_REF]), half-length of each instrumented sample was submitted to these harsh conditions. Before being partially immersed in the ageing solution, each specimen was first submitted to an initial tensile test using an Instron universal testing machine of capacity 250 kN. This preliminary test enables to record reference strain profiles at different load levels. The different loading steps correspond to stress levels of 50, 100 and 150 MPa in steel rebars, and represent theoretical strains of 0.25 ‰, 0.5 ‰ and 0.75 ‰ (assuming a Young's modulus of 200 GPa of steel rebars) (see Table 1). Each load level was hold for 30 seconds before moving to the next plateau (see Fig. 2). The strain profiles were measured at a frequency of 23.8Hz during the entire test using a high spatial resolution optical interrogator (ODISI-B, Luna Technologies). These measurements were performed before and after ageing and compared in an attempt to detect possible changes in the response of the DOFS instrumentation.

As it can be assumed that the mechanical properties of the steel rebar are not affected by the adopted ageing condition, any alteration of the strain profiles would be directly attributed to an ageing-induced deterioration of the DOFS instrumentation (i.e. the adhesive interfaces, the bulk adhesive or the optical fiber itself). A load-unload path was conducted to verify the reversibility of the measured strain profiles.

Ageing of the specimens

The exposure condition consisted in immersing the DOFS the instrumentation into an alkaline solution at a given temperature (either 20 °C or 60 °C). In this line, each rebar (equipped with the bonded DOFS) was conditioned in an ageing cell, as shown in Fig. 3. A specific cap made it possible to install the cell without damaging the optical fiber. The two parts clamping mechanism was designed to fit the rebar and the OF, hence limiting the risk of damage during installation (See Fig. 4). A polyurethane sealant was used to seal the ageing cell (see Fig. 5).

The cell was filled with an alkaline solution composed of sodium hydroxide (NaOH) and potassium hydroxide (KOH), at concentrations of 0.1 mol/L (4 g/L) and 0.5 mol/L (28.05 g/L), respectively. This solution is commonly used in durability and corrosion studies [START_REF] Alj | Environmental Durability of an Optical Fiber Cable Intended for Distributed Strain Measurements in Concrete Structures[END_REF][START_REF] Rolland | Accelerated Aging Behavior in Alkaline Environments of GFRP Reinforcing Bars and Their Bond with Concrete[END_REF], as it is suitable for simulating the pore solution of concrete at early age.

Only the half bonded length of the OF is immersed in the alkaline solution, as shown in Fig. 3. When performing tensile tests afterwards on the exposed rebars, this configuration allows comparing the strain responses of the immersed and non-immersed sections of the OF, and hence to evidence possible alteration of the OF instrumentation induced by the alkaline environment (that may be related either to a degradation of the adhesive bond, an evolution of the bulk properties of the adhesive, or the alkali attack of the OF itself). It is to note that the overall setup was stored in the laboratory environment for the ageing condition at 20 °C, while it was placed in an oven for the condition at 60 °C.

Results

Figure 6 shows the strain profiles recorded at different load levels during the tensile test on the unaged instrumented steel rebar of diameter 12 mm. The x axis corresponds to the relative position along the rebar, and the bonded section of the optical fiber is located at abscissas between 0.175 and 0.37 m. Strain profiles collected at stress levels of 50 MPa, 100 MPa and 150 MPa are displayed together in the graph. They provide average strain values of about 300 µm/m, 500 µm/m and 800 µm/m, respectively, which can be considered as reference data for the unaged DOFS instrumentation. Furthermore, it is interesting to note that profiles collected at increasing increments (levels 1 to 3) are consistent with those obtained at decreasing increments (levels 3 to 5), showing the repeatability of strain measurements. Figure 7 gathers the strain profiles collected during tensile tests at a stress level of 150 MPa, both for the unaged rebar (reference state) and for the same rebar after onemonth exposure in the aging cell at 20 °C. An additional measurement was carried out on the aged rebar after changing its direction upside down in the grips of the tensile machine, in order to verify the symmetry of the test setup (the corresponding profile is denoted "measurement after ageing 2" in the graph).

Figures 6 and7 represent acquisitions made on the same frame. However, the two graphs display different ranges of relative positions because the acquisitions in the reference and aged states were made at different times and requested disconnecting/reconnecting the acquisition line to the ODISI interrogator. Therefore, the length of OF connecting the rebar to the interrogator was different from one test to another.

Polyurethane seal (lower cap) Fig. 6. Reference strain profiles recorded during the tensile test on the unaged instrumented steel rebar of diameter 12 mm, at the different load levels 1 to 5 (see Table 1 and Fig. 2) Fig. 7. Comparison of the strain profiles recorded during tensile tests on the 12 mm steel rebar at a stress level of 150 MPa, in the reference state and after one-month exposure in the ageing cell at 20 °C ("ageing 2" refers to the aged specimen that was turn upside down in the press)

From Fig. 7, it is found that i) the strain profiles of the aged rebar are superimposed to the reference profile and ii) that the average strain level is roughly constant along the immersed and non-immersed sections of the aged rebar. This result suggests that exposure for one month in the alkaline solution at 20°C did not alter the DOFS measurements, and consequently did not induce any significant degradation of the bonded DOFS instrumentation. However, it is important to note that during aging, some leakage of the alkaline solution occurred in the ageing cell and the level of the solution was lowered by one third. Therefore, it was necessary to perform an additional test to confirm the result.

To confirm the previous trend, a second steel rebar of diameter 10 mm was also equipped with the same bonded DOFS instrumentation. In this case, a more severe ageing condition was chosen, as the section of the rebar located inside the ageing cell was immersed in the alkaline solution at 60 °C. This corresponds to the most severe accelerated condition used in the framework of durability studies on FRP composite rebars (an objective is to instrument such FRP rebars with bonded DOFS in future work). Figure 8 displays the reference strain profiles recorded during the tensile test on the unaged specimen, at the same stress levels as for the previous experiment, namely 50 MPa, 100 MPa and 150 MPa (and again for increasing / decreasing load increments as reported in Table 1). At these stress levels, the average strain values are about 200 µm/m, 400 µm/m and 700 µm/m respectively. One can note that the strain profiles are not flat, contrary to what was observed on the first rebar. This suggests a preexisting curvature on the present 10 mm steel rebar. This specific shape of the strain profiles will be taken into account when analyzing the experimental data after ageing. Figure 9 gathers the strain profiles collected during tensile tests at a stress level of 150 MPa, both for the unaged 10 mm rebar (reference state) and for the same rebar after one month exposure in the aging cell at 60 °C. Here again, the graph shows that the strain profiles are similar in the reference and aged states, and that the profile of the aged rebar is not modified along the section immersed in the solution. These results confirm the previous observations made on the first rebar, and do not show any significant degradation of the DOFS instrumentation after one month exposure at 60°C in the alkaline solution. However, in future work, ageing tests should be conducted over longer periods in order to confirm the reliability of the DOFS instrumentation in the long term. In addition, a complementary durability study should be carried out specifically on the epoxy adhesive used to bond the OF. Besides, a next experimental campaign will be carried out on FRP composite rebars. In this case, the alkaline solution is able to diffuse into the rebar (unlike for steel rebars), which may affect the properties of the FRP itself [START_REF] Rolland | Accelerated Aging Behavior in Alkaline Environments of GFRP Reinforcing Bars and Their Bond with Concrete[END_REF], but also the response of the DOFS instrumentation.

Conclusion

In this research, it was demonstrated that a DOFS instrumentation bonded at the surface of steel rebars is not affected by a one-month immersion period in an alkaline solution at temperatures up to 60 °C. This is a first important step for demonstrating the reliability and the durability of this instrumentation. However, similar experiments should be conducted over longer periods to evaluate the long-term ageing behavior.

In future work, additional tests will be performed on instrumented FRP composite rebars to determine if bonded DOFS are suitable for durability studies on FRP reinforced concrete specimens in the laboratory or for the long-term monitoring of real FRP RC structures in the field.

Fig. 1 :Fig. 1 .

 11 Fig. 1 : Engraved samples: 12mm diameter steel rebar Fig. 1. Picture of the steel rebar of  12 mm, showing the OF sealed in the engraved groove

Fig. 3 .

 3 Fig. 3. Experimental set-up used for conditioning the instrumented steel rebars

Fig. 4 .

 4 Fig. 4. Two parts clamping mechanism of the lower cap of the aging cell

Fig. 5 .

 5 Fig. 5. Steel rebar of diameter 10 mm installed in the aging cell

Fig. 8 .

 8 Fig. 8. Reference strain profiles recorded during the tensile test on the unaged instrumented steel rebar of diameter 10 mm, at the different load levels 1 to 5 (see Table 1)

  Fig. 8. Reference strain profiles recorded during the tensile test on the unaged instrumented steel rebar of diameter 10 mm, at the different load levels 1 to 5 (see Table 1)

Fig. 9 .

 9 Fig. 9. Comparison of the strain profiles recorded during tensile tests on the 10 mm steel rebar at a stress level of 150 MPa, in the reference state and after one month exposure in the ageing cell at 60 °C

Table 1 .

 1 Load levels applied during the direct tensile tests. Steps 1 to 3 correspond to increasing load increments while steps 4 and 5 are decreasing increments.

	Loading step	Applied tensile force (kN) Steel rebar of diameter 12 mm Steel rebar of diameter 10 mm	Corresponding stress (MPa)
	1 (and 5)	5.65	3.93	50
	2 (and 4)	11.31	7.85	100
	3	16.96	11.78	150

Fig. 2. Applied strain history recorded by the DOFS during the tensile test on steel rebars