dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019; Naesset et al. , 2020). For instance,  Saatchi et al. (2011)  developed a global biomass map at 1km resolution based on the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud, and Land Elevation Satellite (ICESat) and trained with field measurements and airborne LiDAR. Since 2019, the Global Ecosystem Dynamics Investigation (GEDI) mission  (Dubayah et al. , 2020)  has been collecting high-resolution measurements of vertical forest structures through LiDAR data. Although the sampling is too sparse to derive continuous maps, this new dataset brings a tremendous amount of information on global forest structures. For instance, the GEDI Level 4 Biomass (L4B) product provides 1-km aggregated estimations of above-ground biomass density (AGBD) that come from allometric equations based on waveform metrics calibrated on the biomass measured across forest plots  (Dubayah et al.
, 2022; Duncanson et al. , 2022). However, to properly monitor forests at a local scale, especially in Europe, where forests are divided into small stands of a few hectares, a typical 10 to 50 m spatial resolution is needed. In recent years, studies started to address this issue by spatially extrapolating GEDI height measurements with ancillary continuous satellite data such as Sentinel-1 (S1), Sentinel-2 (S2), or Landsat data, thus creating 10 to 30 m resolution height maps  (Lang et al. , 2022; Morin et al. , 2022; Potapov et al. , 2021; Schwartz et al. , 2022). Additionally, the use of deep learning, and particularly convolutional neural networks (CNNs), has brought new tools to process remote sensing data with improved accuracy and the ability to automatically learn complex multi-scale features like texture from large training datasets  (Ball et al.
(2022). Then, we developed allometric equations based on NFI data to produce FORMS-B and FORMS-V. Comprehensive validation of FORMS-H is carried out with thousands of plots from the French National Forest Inventory (NFI) data and Airborne Laser Scanning (ALS). As we used NFI for calibration, we further validated FORMS-B estimates using two independent sets of non-NFI forest plot data. Finally, we conducted a comparative analysis with other height and AGBD maps available over France to highlight the increased performances of our products. These results contribute to a better understanding of France's forest structure and carbon stocks at an unprecedented spatial resolution, with potential applications in forest management, climate change adaptation, and mitigation efforts.
Data This study relies on 15 datasets to generate and evaluate the high-resolution tree height, AGBD and WVD maps of France. Three spaceborne datasets from GEDI, S1, and S2 were employed to train the deep learning model and generate the 10 m resolution FORMS-H product. To derive 30 m resolution FORMS-B/V products, we applied allometric equations based on in situ measurements from the French NFI data, along with a broadleaf/coniferous mask obtained from the Copernicus Dominant Leaf Type (DLT) map. FORMS-H was validated against several datasets, including the French NFI height data and ALS data from the French LiDAR HD campaign. Furthermore, FORMS-B/V were evaluated against two forest inventory datasets  (GLORIE and Renecofor)  and aggregated data at the French ecoregion scale ("SylvoEcoRegion," SER, https://inventaire-forestier.
ign. fr/spip. php?article773). Finally, we compared FORMS-H and FORMS-B with existing height  (Liu et al. , 2023; Potapov et al. , 2021; Lang et al. , 2022)  and AGBD  (Santoro and Cartus, 2023; Liu et al. , 2023)  products available for France. Table  1  provides comprehensive details about the datasets used in this study and how they were used to train and assess the accuracy of our FORMS products.
From height to wood volume and biomass To produce an AGBD map at 30 m resolution (in Mg. ha -1 ), we derived power-law allometric equations from the French NFI plot data (See Table  1 ). We chose a 30 m resolution to correspond to the size of the NFI plots and to obtain a sufficient number of trees within one pixel so that AGBD has real significance. Every year, the French Geographical Institute (IGN) measures~ 6000 new plots for the French NFI  (IGN, 2018). For each 30 m circular NFI plot, dendrometric measurements are made in concentric plots of 6, 9, and 15 m radius according to the DBH, and for trees having a minimum of 7. 5 cm DBH. DBH and species are collected for all the trees, and height is measured for one tree per species and DBH class. The tree volumes are computed using allometric models involving DBH and height, and a WVD estimation (in m 3.
Products validation To evaluate the accuracy of our 10 m resolution FORMS-H product, we compared it to four independent datasets: the 2,479,668 GEDI Test footprints, the 5,475 French NFI plots measured in 2020, and two ALS datasets (one large area of 2500 km² and one set of 20 smaller areas of 1 km² each) from an ongoing French Lidar HD campaign that aims at covering the whole national territory within the next years (Table  1 ). GEDI Test footprints were taken from the 589 Test tiles (Fig. 1 ) and filtered with the Copernicus DLT map (Table  1 ) to remove non-forest data. We compared here the maximum FORMS-H value within the 30 m circular plot area to the dominant height variable provided in the NFI data. This height is computed from ~ 7 representative trees and stands for the mean height of the 100 highest trees within a surface area of 1 hectare.
The ALS point-cloud data come from the French LiDAR HD measurement campaign (Table  1 ). The first ALS site is a large distributed in different sites with more complex topography. We used ALS data point cloud classification (See Table  1 ) to create a 50 cm resolution canopy height model that we resampled at 10 m by taking the 95 th percentile of height and compared it to FORMS-H. Additionally, we conducted a comparative analysis with three height maps available globally or in Europe to assess the novelty of FORMS-H  (Lang et al. , 2022; Liu et al. , 2023; Potapov et al. , 2021). These maps will be referred to as "Lang", "Liu", and "Potapov" respectively in the following. We evaluated FORMS-B by comparison to two independent forest inventory datasets and to aggregated NFI statistics at a larger scale.
The NFI does not provide official plot-level biomass estimates, but rather only volume and height, and our method for establishing the height-AGBD relationships shown in Fig. 2  would lead to large errors and a degree of circularity if AGBD inferred for individual plots was used for validation. Further, it would not be an independent validation since the NFI AGBD data are used to define the relationship for transforming height maps to AGBD maps. For these reasons, we used for validation two smaller inventories ("Renecofor", "GLORIE", described hereafter) that are independent of the NFI. The Renecofor permanent plot network  (Ulrich, 1995)  gathers 102 forest plots distributed over France (Fig. 3 ) that grow under different climatic and soil conditions. Measurements of numerous parameters, including DBH, are performed yearly by the French National Forest Office (ONF) to monitor and understand changes in forest ecosystems.
These monitoring plots were installed in public forests, and their stands are managed by local foresters with the same thinning intensity as the surrounding stands. To derive a biomass estimation that we could use as reference data to evaluate our FORMS-B product, we used species-dependent DBH-based allometric equations and wood densities described in  Forrester et al. (2017). We applied generic equations based on the tree leaf type (coniferous/broadleaf) for the tree species not covered by this study. Thus, by dividing by the plot surface (0. 5 ha), we obtained an AGBD estimation for each forest plot of the Renecofor network. These values were compared here to the mean FORMS-B values within a 100 m diameter circle around the location of the plot. The GLORIE forest inventory  (Motte et al.
, 2016; Zribi et al. , 2019)  includes measurements of tree heights, DBH, and tree density in 104 forest stands of maritime pine located in the Landes Forest (Fig. 3 ). This area represents mainly private and intensively managed forests, representative of the Les Landes area. In the GLORIE data set, AGBD estimations were derived from allometric equations applied to plot measurements of DBH  (Shaiek et al. (2011)  for DBH => 10 cm and Baldini et al. (1989) otherwise). These values were compared to the mean FORMS-B values within a 50 m diameter circle around the location of the plot, which corresponds to the average plot dimensions. At a regional scale, we also evaluated our FORMS- 2014. These estimations, given in above-and below-ground carbon per hectare, were converted to AGBD with ratios described in  Loustau et al.
(2010) : the values were divided by 0. 475 MgC. Mg -1 to estimate the oven-dry biomass and by a 1. 3 root expansion factor to account for AGB only. As for FORMS-V, we further compared these estimates to FORMS-B to assess its performance at a regional scale. Additionally, we compared FORMS-B to two other biomass products available globally or for Europe  (Liu et al. , 2023; Santoro and Cartus, 2023) , which were converted when necessary to WVD maps with the factors detailed in Sect. 3. 2. These maps will be referred to as "Liu" and "ESACCI" respectively in the following. In this study, we used several error metrics, including the mean absolute error (MAE), the mean error (ME), the mean absolute percentage error (MAPE), and the coefficient of determination (R²). The MAE gives information about the overall error, the ME highlights the model's bias, and the MAPE computes the relative error percentage to compare the model's performances on different validation datasets.
The comparison analysis with Renecofor plots for the two other biomass maps leads to the same conclusion but with a higher MAE. The GLORIE forest plots are all located in the Landes Forest, an intensive maritime pine plantation in the South West of France. On this dataset, with AGBD ranging from 0 to 160 Mg ha -1 , FORMS-B has an MAE of 19. 7 Mg ha -1 , better than the two other products with an MAE of 26. 5 Mg ha -1 and 28. 3 Mg ha -1 for Liu and ESACCI respectively. We observe a tendency to overestimate lower AGBD values which can be explained here by the time difference between the date of inventory and the date of the maps presented here, given the high growth rates of young maritime pines in the study area  (Lemoine, 1991). Overall, Fig. 8  highlights the capability of FORMS-B to estimate biomass density, compared to other existing biomass maps, across various types of forests in France.
Still, errors are larger (MAPE = 24. 1 % for Renecofor, 39. 9 % for GLORIE) than the ones obtained for the height map validation (MAPE = 18. 4 % for the NFI plots, 16. 9 % for ALS 1 and 22. 5 % for ALS 2), which showcases the difficulty to derive an AGBD map only from a height-based product. Our height-biomass allometric equations for both coniferous and broadleaved forests could be refined with other parameters such as forest cover and detailed for specific species, which requires a high-resolution dominant species map to increase FORMS-B accuracy. 1. Other datasets were either protected by privacy rules or made available upon request to their owners. These dataset providers were added as co-authors of this paper as their work largely contributed to the creation and validation of our products.
DATA AVAILABILITY PMC strains are available upon request (https://mcam. mnhn. fr/en/cyanobacteria-andlive-microalgae-1281). Raw reads were deposited into the GENBANK Sequence Read Archive (SRA) database under the BioProject PRJNA693796 as well as A. gracile genome assemblies (JAQSYX000000000, JAQSYY000000000, JAQSYZ000000000, JAQSZA000000000).
Ring-width and anatomical trait measurements We carefully inspected each cross-section to detect wedging rings and measured RW along three radii from high-resolution (1200 dpi), digitized images using the CooRecorder 9. 0 software. We first developed a RW chronology following a three-step procedure which included the cross-dating and averaging of (1) three radii within each cross-section, (2) three cross-sections of the same individual, and (3) RW series from the 14 individuals  (Francon et al. , 2017; Francon et al. , 2020a; Francon et al. , 2020b; Francon et al. , 2021). Based on image quality, series length and correlation with the master chronology (r > 0. 6), we selected 9 individuals for QWA analyses  (Fonti et al. , 2013). The sample size is in line with recent studies investigating dwarf shrub anatomical traits and water potential (5 individuals;  Anadon-Rosell et al.
, 2018; Ganthaler and Mayr, 2021) , as well as tree species anatomical parameters, including Larix siberica Ldb. (5 individuals;  Fonti et al. , 2013) , Pinus sylvestris L. (8-9;  Pritzkow et al. , 2014; Seo et al. , 2014) , Picea abies (L. ) Karst. (8 individuals;  Castagneri et al. , 2015) , Larix decidua Mill. and Pinus cembra L. (6-7 individuals;  Carrer et al. , 2017). For each individual, we performed anatomical measurements along two radial subareas of the root collar cross-section (2-mm wide bands with lengths extending from bark to pith, selected with the Zeiss ZEN software), while carefully avoiding incomplete ring sequences, rotten parts, callus tissue, reaction wood, or mechanical damage  (Carrer et al. , 2017). Using the ROXAS 3. 0. 586 image analysis software (von  Arx and Carrer, 2014) , we measured eight vesselrelated anatomical traits within each ring: number of cells (CNo), mean vessel lumen area (MLA), the 25 th and 95 th percentiles of lumen area distributions (MLA25 and MLA95), hydraulically weighted mean vessel diameter (Dh), theoretical total (Kh) and xylem-specific (Ks) hydraulic conductivities as well as cell density (CD)  (Anadon-Rosell et al.
, 2018). The ROXAS software (von  Arx and Carrer, 2014 ) allowed thorough analysis of anatomical features by coupling automated image-analysis tools, with accurate manual editing at both ring (e. g. , digitizing ring boundaries not automatically recognized) and cell (e. g. , correction of anomalous structures) levels. After applying proper image calibration and sample-specific configurations, the anatomical traits were detected using an automated segmentation process, improved by manual editing (von  Arx et al. , 2016 , Garcia-Pedrero et al. , 2018).
Chronology development and variable selection Using the dplR package  (Bunn, 2008)  for R software (R Core Team, 2016), we detrended individual series of vessel anatomical traits using a cubic smoothing spline with a 50% frequency response at 30 years  (Cook and Peters, 1981; Cook, 1987)  to eliminate non-climatic trends (e. g. , age-related growth trends and/or effects of natural or human-induced disturbances) and to maximize interannual variations  (Francon et al. , 2017). The resulting growth indices were averaged in annually-resolved chronologies using a bi-weight robust mean designed to reduce the influence of outliers  (Cook and Peters, 1981). We assessed the robustness of each chronology by computing the mean inter-series correlation (rbar), the expressed population signal (EPS;  Wigley et al.
N°indiv First shifts in shrub growth responses to climate, we investigated the entire period covered by meteorological records (1960-2019) as well as two subperiods  (1960-1989 and 1990-2019)  of sufficient length (i. e. 30 years) to be considered as climate normal. The two subperiods were chosen according to the results obtained by computing moving correlation functions (MCFs) over a 30-year time window to test stability over time (see below). We performed monthly and seasonal bootstrapped correlation functions (BCFs) and 30-year moving correlation functions (MCFs) using the Treeclim package  (Zang and Biondi, 2015)  for R software. To this end, we detrended meteorological data prior to climate-growth relationship analyses using the standardization procedure employed for the series of anatomical traits.
The time window considered for BCFs and MCFs spans from August of the year preceding growth-ring formation (n-1) to current August (n). In addition, to retrieve more detailed information on anatomical and radial growth responses to climatic parameters  (Jevsěnak and Levanic, 2018; Thomte et al. , 2020) , we computed MCFs between RW and those vessel-related trait series that were identified from the PCA (i. e. CNO and MLA25, see below) and daily temperature and precipitation series from the Segl-Maria weather station averaged over time periods ranging from 10 to 150 days data using the dendroTools R package  (Jevsěnak and Levanic, 2018).
Acknowledgments This research was carried out within a Ph. D. in  Environmental Science at Insubria University, Italy. The sample preparation and analyses were performed at Dendrolab. ch, University of Geneva, Switzerland. The meteorological data series were provided by the  Federal Office of Meteorology and Climatology MeteoSwiss, 8058 Zürich-Flughafen. Rhododendron shrubs were sampled with the help of  Ilaria Bonfanti.
Introduction One of the major goals of molecular spectroscopy is the development of a radiation propagation model, which is capable to cover a wide spectral range for various gas thermodynamic conditions. The information gathered in spectroscopic databases and well-developed theoretical methods allow modeling the observed spectra. In the spectral interval of a single line, the experimental data can be reproduced with a relative deviation down to about 0. 1 % or even better. The wider the interval, the worse the agreement. In a broad range the spectra are usually simulated as a sum of individual line profiles representing resonant absorption and the so-called "continuum" or the non-resonant part. Such subdivision is rather arbitrary because all current line shape models are developed in the impact approximation, meaning that their validity range is limited to the vicinity of the line center  1, 2.
Collisional pressure shifts were omitted in the model because their influence on the result of the modeling can be safely supposed to be negligible. The integrated intensities of the lines are proportional to the number density of CO molecules: I j = S j (T ) • n CO = S j (T ) • P CO k B T , ( 10 ) where S j (T) are normalized line intensities, as supplied by spectroscopic databases. The number density was calculated using an ideal gas approximation. It was verified in Paper I 30 that accounting for nonideality of gas leads to number density correction, which does not exceed 0. 1 % for the experimental conditions of the resonator spectrometer and thus can be safely neglected. Intensities, central frequencies and self-broadening coefficients were taken from the HITRAN2020 database  43. Only lines with Fig.
6  Stick-diagram of the CO spectrum in the range under study in accordance with the HITRAN2020 database. The horizontal lines are for the resonator spectrometer sensitivity limit (4 × 10 -9 cm -1 ) for two most unfavorable cases (maximal absorption at the line center) of recordings in pure gas and in CO-Ar mixture. Identification is given for the R(1) lines only because the spectrum pattern is the same for both transitions. intensities larger than 10 -28 cm/molec were taken into account. The lines with ν 0 > 100 cm -1 were omitted. Ar-broadening coefficients were determined according to Luo et al. 52. The vibrational dependence of the broadening was neglected owing to the (nearly) triple bond of CO. Figure  6  presents the CO spectrum diagram in the range near the R(0) and R(1) rotational transitions.
It demonstrates that notable contribution to the absorption can be from lines of the most abundant isotopologues in the ground vibrational state and rotational lines of the first excited vibrational state of the main CO isotopologue. The J-dependence of the line mixing coefficients was determined by the iterative procedure starting with the experimental data on He-broadening of CO lines from Predoi-Cross et al. 28  At the first step, we supposed that y CO-CO R(J) = y CO-Ar R(J) = y CO-He R(J). At the second step, after analysis of our experimental data and determination of y coefficients for the R(0) and R(1) lines, the y R(J) coefficients were calculated for both the self-and Ar-broadening cases as: y R(J) = y R(0),exp for J = 0, y R(1),exp for J = 1, y R(J-1) • y CO-He R(J) y CO-He R(J-1) for J > 1; (11) Another possibility of calculating the J-dependence is based on the y CO-CO R(J) data provided by the HITRAN2020 database (see also  Hashemi et al.
53  ). For an unknown reason these coefficients are equal to zero for J ≤ 3 and values for J > 3 differ from the coefficients calculated using equation (11) on the basis of our experimental results (Fig. 7 ) Note that the use of self-mixing coefficients from HITRAN instead of our approximation for J > 3 does not have a notable effect on the values of the retrieved line shape parameters (frequency, width, intensity, mixing), but will slightly impact the additive term responsible for the continuum (which is detailed below). This effect is small and close to the sensitivity limit of the spectrometer. A more detailed analysis of how the uncertainties of line shape This work CO -CO, 296 K Fig. 7  CO-CO first-order line mixing coefficients versus quantum number J in rotational band. Red diamonds correspond to the dependence obtained using equation (  11 ) on the basis of our experimental results (first two points) and data from Predoi-Cross et al.
28. Blue diamonds are from the HITRAN2020 database. coefficients of other lines of the CO spectrum can affect the retrieved coefficients of the R(0) and R(1) lines is given in Section 3. Spectra recordings obtained at close, but slightly different conditions (e. g. , Ar pressure differs by several torrs, or temperature by several tenths of kelvins) were corrected to provide their correspondence to the same conditions. The correction was calculated using formulae (6-7) for the total spectrum modeling. After the correction, the spectra were averaged. Thus, we obtain a number of averaged spectra, each of which corresponds to specific conditions (P CO and P Ar ). The averaged spectra were fitted by the speed-dependent Van Vleck -Weisskopf model, which accounts for SD of collisional cross-section and line mixing effect (SDVVWLM).
The resonator response signal was too weak near the line maximum due to strong absorption. Thus, the line wings were recorded only. A typical example is given in Fig. 9. It shows that absorption cannot be measured in the range near the line center. The maximum measurable (in the current version of the spectrometer) absorption coefficient is about 5 × 10 -5 cm -1 , which is smaller than the absorption around the line maximum. Most parameters of the qSDVVWLM profile are fixed: (i) intensities are taken from HITRAN2020 database; (ii) central frequencies are from Lamb-dip measurements  22  ; (iii) the broadening coefficients are taken from the RAD spectrometer data (Table  4 ). The gas pressure, Y parameter and parameters of the pedestal C 0 and C 2 are free parameters of the model. The deviation of CO pressure determined from the fit from Baratron gauge readings is within 0.
4 %, which exceeds the declared uncertainty of the Fig. 8  Upper panel: experimental spectrum of a CO-Ar mixture in the R(0) line region (points) and its approximation (solid curve) using Eq. (  14 ); lower panels: residuals of the approximation for VVWLM, qS-DVVW and qSDVVWLM line profiles. gauge (0. 25 %). This deviation can be explained by the inaccuracy of the line integrated intensity in the HITRAN database, which will be discussed below (Section 3). We emphasize that even a small change of CO partial pressure of about 0. 1 %, leads to a significant change of the residual (Fig. 9 ). This demonstrates that our resonator spectrometer can be used for calibration of pressure gauges (if the intensity and the self-broadening parameter of a line are known with sufficiently low uncertainty). An example of the combined spectra of the R(0) and R(1) lines is shown in Fig.