In this study, two years of 10-day resampled S2 time series were used to identify the optimal set of features and period needed to distinguish poplar plantations at broad scale. Based on a single-feature configuration using SFFS, we investigated the ability of specific wavelength regions and spectral indices with their respective dates to classify poplars. We also assessed the interest of combining multiple bands or indices using a multi-feature SFFS configuration. A new poplar detection index based on a SWIR and red edge S2 bands was defined and used to map plantations at the French countrywide scale. To our knowledge, this is the first attempt to analyse the most discriminating spectral regions of hybrid poplar plantations with S2 time series and to provide a S2-based poplar index to map poplar plantations including a large number of cultivars in different management contexts and over large areas.
5.2. S2 Spectral Bands in the SWIR and Red Edge Domains Are Required to Identify Poplar
The results of variable selection with SFFS showed the relevance of the SWIR spectral region (B11 and B12) in distinguishing poplar plantations from other deciduous species. The results also highlighted the secondary importance of the red edge domain and particularly B5. Interestingly, these findings are in line with those reported by Viinikka et al. [
23], who used airborne hyperspectral data with 460 spectral bands covering the visible, NIR, and SWIR ranges to discriminate European aspen from three other species in southern Finland. These authors found that the most discriminative bands of aspen trees were located in the red edge (
) and SWIR (
and
) ranges. This does not perfectly fit the approximate wavelength range of B5 (
) and B11 (
) in Sentinel-2, but does refer to adjacent wavelengths.
Theoretically, the reflectance in the SWIR range is predominantly affected by leaf water content [
65]. Changes in water content can be observed with the SWIR bands located near the major water absorption features at approximately the 1200 nm, 1450 nm, 1950 nm, and 2500 nm wavelengths of the spectrum [
66], and most notably around 1450 nm and 1950 nm [
67]. In case of high water contents, these absorptions bands become saturated, which induces a sensitivity to difference in leaf water content in the regions of intermediate absorptions near 1650 nm and 2200 nm [
68], which coincides with B11 and B12 in S2. A high correlation between water status of
Populus spp. and spectral indices based on SWIR bands (especially between 1500 and 1750 nm) was already observed in [
69].
A number of remote sensing studies showed that SWIR bands’ reflectance decreased with an increase in leaf water content [
65,
70], suggesting that poplar leaves should have higher water content during the spring–summer period according to the results. This is consistent with the fact that high growth rate of poplar is associated with a high water demand. Irrigation, in addition to nutrient supply, is a common cultivation practice for poplar plantations [
71]. For poplar, Zhang et al. [
72] analysed seasonal (June to September) trends in the water consumption of trees in a temperate climate and showed that the increase in solar radiation was followed by an increase in evaporative demand. At the beginning of the summer season, the increase in transpiration was offset by an increase in the absorption of water available in the soil due to the typical capacity of poplar to exploit groundwater. Later in the season, soil water content decreased, and absorption reached a low plateau, leading to different adaptation mechanisms, such as stomatal closure to control losses through transpiration. This behaviour is consistent with the temporal profiles of the two SWIR bands, where in early spring (March), SWIR reflectance decreases rapidly as a result of increased water consumption to reach a low plateau, which continues throughout the dry season (July–August) (see
Figure 4). Available information from past studies makes it hard to properly compare the seasonal variation of water contents between hybrid cultivated poplars and other deciduous trees with the corresponding spectral curves (e.g., see [
73]). However, the LOPEX and ANGERS leaf optical properties databases provide water content values for different tree species [
74,
75]. For LOPEX, leaves were collected during early summer (June), and for some of them, also in early autumn (September). No information is provided for ANGERS. The poplar species available show rather high equivalent water thickness (EWT) values compared to other common tree species, especially Carolina poplar, which is a hybrid of P. x euramericana (
Populus nigra x
Populus deltoides), as we have in this study (
Table 5). These values are independent of any irrigation practice.
However, the reflectance of vegetation canopies does not only depend on the leaf optical properties. Other factors are involved, such as viewing geometry and background signal, in addition to the canopy leaf area index (LAI) and the leaf angle distribution (LAD). Thus, at the stand level, the contribution of water content in spectral reflectance is modulated by these other factors [
76,
77]. Since high correlation may exist between SWIR bands and LAI (e.g., at 1650, 2100, and 2260 nm according to [
78]), the variation in B11 and B12 may be related to the variation in LAI, in addition to water content. This is also true for spectral indices that exploit water absorption bands such as SIWSI, NBRI, and MSI [
77]. The last one, whose performance was quite high in the single-index configuration, is correlated to the canopy LAI of poplar plantations [
76]. More generally, in addition to the sensitivity towards water content, these indices respond to LAI and LAD in Sentinel-2 and possible other confounding structural drivers (e.g., stem density and crown diameter), as demonstrated by Morcillo-Pallarés et al. [
77] from simulations at the leaf and forest canopy levels. Therefore, a possible functional convergence among optical traits could be suspected to explain the specific reflectance pattern of poplars in the SWIR bands.
An alternative assumption is the difference in phenolic compounds. More than 160 different types of phytochemical compounds have been identified in poplar species, including various phenolics including flavonoids, glucosides, acids, alcohol, lignan, and others [
79]. Absorption features (depth, width, and area) centred near 1660 nm have been identified as robust indicators to quantify plant phenolic concentrations using reflectance spectra [
80]. Variability in phenolics concentrations and compounds between poplar and non-poplar species may contribute to making B11 important. Further research is needed to verify this.
Concerning the red edge, this spectral region (680–780 nm) proved to be highly sensitive to the chlorophyll content of the vegetation [
81,
82] and has been used to estimate structural features such as LAI [
83,
84], or nutritional status such as N concentration (e.g., [
85]). Recently, Kyaw et al. [
86] showed that leaf reflectance of hybrid poplars at 712 nm (i.e., included in the B5 range) was a significant wavelength for predicting nitrogen content per unit leaf area (N
), even if the observed correlation was weak (R
2 = 0.29 with a LASSO model). This relation was found using 105 leaf sample data of 62
Populus genotypes across seven taxa. Leaves were measured in July and early September in two young plantations located in upland regions of Mississippi, USA. In deciduous tree species, photosynthetic activity increases during the spring growing season (starting in March–April in temperate regions) along with the concentration of chlorophyll resulting from foliage growth [
87,
88]. Like with the SWIR bands, reflectance decreases at the start of the growing season, reflecting an increase in photosynthetic activity and in chlorophyll concentration [
81]. The temporal signature of B5 reflects this trend with a decrease in reflectance from the end of March to mid-September (see
Appendix F). However, only a marginal difference can be observed between plantations of poplar and those of other deciduous species, which may explain the minor importance of the red edge compared to that of SWIR for the discrimination of poplar plantations. The seasonal pattern of B5 suggests a possible higher chlorophyll content in poplars, especially in May and June, but this contradicts the low values available in the LOPEX and ANGERS leaf optical properties databases (
Table 5). However, as for EWT, this comparison is rather unreliable because of the influence of the species-specific canopy structure on the spectral behaviour [
89]. Further analysis is required to advance our understanding of the underlying biophysical process. It can be mentioned that S2 red edge band B5 was previously shown to be important for discriminating tree species in temperate forests, as well as SWIR bands B11 and B12 [
90,
91,
92].
Finally, the PI
index, combining both B11 and B12 and subtracting them from B5, makes it possible to accentuate the difference between the reflectance of poplar and of other tree species. The lower the reflectances in the SWIR, the higher the value of PI
(
Appendix F). This formulation was more competitive than the other variants of the Poplar index (PI
, PI
, PI
) even if the results of PI
(based on B11 and B12 only) were very close to the results obtained in the single-index configuration. This was also true using B11 or B12 alone or the SIWSI spectral index (a normalised difference combining B11 and B8a), for which the difference in performance was significantly smaller (
Appendix D). The PI index defined from S2 imagery could be adapted for other sensors, and in particular, Landsat 8-9 by selecting bands 6 (SWIR 1 at ∼1560–1660 nm) and 7 (SWIR 2 at ∼2100–2300 nm).
5.4. The National Map of Poplar Plantations Requires Field Validation
A first version of the national map was produced using the PI index. As the feature selection analysis revealed, the map could have been produced using only a few dates between May and August, in addition to October, using this new index. However, because of cloudy acquisitions that vary from one region to another, the possible influence of residual noisy pixels (undetected clouds), and the existence of some evergreen deciduous species (e.g., eucalyptus plantations), we used the full year time series of PI to generate the national map in 2018 (36 dates). After checking, we found that classification performances were not affected by the addition of all the dates (no Hughes phenomenon).
This map now needs to be validated in the field by forest partners to detect specific confusion with other species (missing from the reference set), the possible influence of the understorey vegetation in some regions where plantations were abandoned, and the detection limit related to the stage of development of the plantations. We assumed that no plantations less than three years old were mapped due to insufficient canopy cover and a possible effect of soil, but in practice, the minimum age for detection is more gradual because other factors have to be taken into consideration such as site conditions and the cultivar planted. Plantations can present different growth patterns with, for instance, a maximum growth rate in the first two years or a slow growth rate at the beginning and an increase in the growth rate later in the cycle [
93,
94]. We conducted a first analysis with a specific dataset of reference samples for which the cultivars and the age of the plantations were known. We observed an increase in the confidence values of the detected plantations with age, but this depended to a great extent on the cultivar concerned. Additional field-checked references are required to better define the detectability threshold of plantations. The absence of confusion with other short-rotation coppice (SRC) plantations, such as willow (
Salix), which have higher water requirements, should also be verified. A previous study revealed a clear distinction between poplar and willow, but willow was growing in a natural context and not in SRC plantations [
31].
The national map identified the main poplar plantation sites in France, but also a large number of municipalities with either a low density of poplar trees with a high level of confidence (in blue), or the opposite, a high density of poplar trees with a low level of confidence (in yellow). In the first case, in addition to the few plantations, this coincides with the presence of poplar in riparian areas. Some natural patches of poplar were detected along waterways. No precise evaluation was carried out to estimate the true ability to discriminate these poplars, but it opens up possibilities for such areas of high conservation value [
95]. Over-detection was also observed in some places with low confidence values despite a high density of poplars. Closer examination revealed that these cases often refer to confusions with coniferous stands (or agricultural fields). From the modelling point of view, these errors are not surprising, since only references of deciduous species were used for training. Rather, they highlight the imperfection of the forest/non-forest mask used to only retain areas with deciduous species. The HRL Dominant Leaf Type 2018 was considered as the best candidate currently available, but its quality directly influenced our poplar plantation layer. In the future, the reference dataset could be enriched with conifer samples derived from the French NFI spatial database to distinguish between coniferous and deciduous trees in the classification process. An alternative would be the adoption of a novelty detection approach to identify test data (unseen pixels) that differ significantly from the training set [
96]. This would make the method more independent of the existence of an accurate forest/non-forest mask.
We are confident about the accuracy of the French NFI spatial database and the way we used it as a reference dataset to limit classification bias. For the poplar class, the database was only used to identify a potential location of plantations, and each of them was checked by visual interpretation before integrating it into the reference dataset. Misidentification (evaluated by field campaigns) was considered negligible (<1%) and below the level that may affect the performance of the random forest classifier [
97]. Moreover, unlike sub-natural forests, poplar plantations are monospecific and even-aged, composed of clones, which makes them very homogeneous and does not require a precise GPS survey to position trees. On the contrary, stands of other deciduous trees have not been checked. Therefore, it is possible that in some cases, the land use has changed or the species have been replaced by others since the year of the production of the forest database. According to the NFI statistics, the average timber extraction of deciduous trees from 2011 to 2019 is estimated at approximately 0.1 millions (M) m
3/year in tile 31TCJ, 0.35 Mm
3/year in tile 30TYT, and 0.55 Mm
3/year in tile 31UEQ. The reference dataset can be affected by this noise. However, this imperfection should have a limited impact on poplar recognition because it mainly concerns the non-poplar class, with possible confusions between species within this class. Another source of imperfection is the possible existence of natural poplars in the mixed class of deciduous species. There is no specific pure class of natural poplars in the database, contrary to poplar plantations that are never assigned to the mixed class, whatever the stand area. In this case, the learning process could be affected, and some pixels of poplar could be predicted in the mixed class (and vice versa). Once again, this noise is probably too marginal to affect the model, and what appear to be confusions are not always in this case (see the matrix in
Appendix E). Ultimately, we think that the most important bias for the countrywide prediction is the fact that the non-poplar class is not fully representative of the diversity of all the deciduous species. Only the species existing in the three image tiles were integrated. This limit can be easily overcome by collecting additional non-poplar references from the NFI spatial database in other image tiles.