dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). Our study demonstrates that highest NH3 concentrations are recorded during the period when fires predominate (December-February), while the lowest are obtained when rainfall is high. Indeed, during the dry season, farmers take advantage of the absence of rainfall to clear land, weed and burn agricultural residues. This slash-and-burn agriculture contributes significantly to nitrogen (NOx and NH3) and carbon (CO and CO2) emissions  (Tiemoko et al. , 2021)  into the atmosphere during the dry season. Fires related to agriculture and hunting become more important in the dry season and represent respectively 64% and 6% of the economic activities of the villagers in certain areas such as Lamto  (Suzanne, 2016). In order to show the influence of the combustion source on the atmospheric NH3 concentrations and total column densities, we conducted linear correlation study between monthly ground-based NH3 concentrations and IASI NH3 total column densities on the one hand, and the GFED4 (1998-2018) emission data of NH3 from biomass burning  (Giglio et al.
The section 3. 2. 2 focuses on long term trends based on monthly data of NH3 ground-based concentrations and total column densities at the six stations by using the SK test coupled to SKS. In these sections, we present only the results of significant trends. In the paragraph preceding the conclusion of the paper, we present a general comment on the trends obtained for each ecosystem and explain the differences obtained between ground-based concentration and total column density measurement trends. Reported ground-based NH3 concentration and IASI NH3 trends are analyzed in the light of NH3 emissions from all combustion sources (described in section 2. 3), meteorological (air temperature and rainfall) and physical (LAI) parameters when available, which influence the atmospheric level of NH3.
Trends accounting for seasonality Long time series of atmospheric NH3 could usually be affected by seasonality, which is the cyclical changes in concentrations or densities over the course of the year. The SK test is significantly robust in revealing trends in seasonal time series. In this section, we perform trend computations using SK coupled to SKS of monthly mean ground-based NH3 concentrations and IASI NH3 total column densities of the entire dataset. Results for only significant monthly trends (p<0. 1) from all INDAAF sites are shown in Figure  8. In general, the statistical tests reveal significant decreasing trends for ground-based NH3 concentrations (except at Bomassa), but increasing trends for IASI NH3 total column densities (Figure  8 ). trend of IASI NH3 total column densities at Bomassa (+0.
17 molec cm -2 yr -1 or +1. 21 % yr -1 ) is three times lower than that of Zoétélé (+0. 56 molec cm -2 yr -1 or +3. 76 % yr -1 ). The SK applied to monthly data from January, 1998 to December, 2018 shows that relative humidity decreases by -0. 15% yr - 1 at Lamto. We calculate Pearson's correlation between ground-based NH3 concentrations and relative humidity and we find a coefficient of -0. 50 significant at 99%. This statistical test demonstrates that the decreasing monthly trend of ground-based NH3 concentrations cannot be explained by the monthly relative humidity trend. Trend studies of NH3 concentrations and densities obtained respectively with the INDAAF passive samplers and the IASI instrument have shown significant trends depending on each biome. Overall, we obtained decreasing trends for ground-based measurements (except at the Bomassa forest site), but increasing trends for IASI total column densities of NH3.
This result was found for all ecosystems. The long-term statistical trend results for NH3 emissions from GFED4 database are not significant, so could not explain the trends obtained for the ground-based and satellite data. A plausible explanation for the contrasting trends between surface concentrations and satellite columns could be the impact of biomass burning plumes. The latter are likely less well captured from INDAAF passive samplers, while they are very well measured by IASI  (Zheng et al. , 2021). It is likely that IR sounders have a higher sensitivity to fire plumes, which are located higher in the atmosphere (and so the ground-based measurements will show less sensitivity to them).
Acknowledgement This paper is part of the  INDAAF (International Network  to study Deposition  and Atmospheric chemistry in Africa ) long-term project supported by the  CNRS/INSU (Centre National de la Recherche Scientifique / Institut National des Sciences de l'Univers) , by the ACTRIS-FR research infrastructure and by the  IRD  ( Institut de Recherche pour le Developpement). We are particularly grateful to all INDAAF local field technicians, for their work. This study has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 871944. This work was supported by the  CNES. IASI  has been developed and built under the responsibility of the  Centre National d'Études Spatiales (CNES, France). It is flown on board the Metop satellites as part of the  EUMETSAT Polar System.
The research in Belgium was funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex HIRS)  and the  Air Liquide Foundation  ( TAPIR  project). This work is also partly supported by the  FED-tWIN  project  ARENBERG  funded via the  Belgian Science Policy Office (BELSPO). L. Clarisse  is Research Associate supported by the  Belgian F. R. S. -FNRS.
the most widely used global fire emissions inventory-has been widely cited in the literature, and GFED4 data can be downloaded from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad3. sedoo. fr/). https://doi. org/10. 5194/acp-2022-793 Preprint. Discussion started: 16 January 2023 c Author(s) 2023. CC BY 4. 0 License.
Pearson's correlation tests to meteorological and NH3 emission data from GFED4 databases. The results show the decreasing trend in ground-based NH3 concentrations in the wet season at Zoétélé could be attributed to wet season-to-wet season https://doi. org/10. 5194/acp-2022-793 Preprint. Discussion started: 16 January 2023 c Author(s) 2023. CC BY 4. 0 License.
Abstract. The frequency and intensity of summer droughts and heat waves in Western Europe have been increasing, raising concerns about the emergence of fire hazard in less fire-prone areas. This exposure of old-growth forests hosting unadapted tree species may cause disproportionately large biomass losses compared to those observed in frequently burned Mediterranean ecosystems. Therefore, analyzing fire seasons from the perspective of exposed burned areas alone is insufficient; we must also consider impacts on biomass loss. In this study, we focus on the exceptional 2022 summer fire season in France and use very high-resolution (10 m) satellite data to calculate the burned area, tree height at the national level, and subsequent ecological impact based on biomass loss during fires. Our high-resolution semi-automated detection estimated 42 520 ha of burned area, compared to the 66 393 ha estimated by the European automated remote sensing detection system (EFFIS), including 48 330 ha actually occurring in forests.
74 Mt in the Atlantic pine forest, and 0. 57 Mt in temperate forests, amounting to a total loss of 2. 553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests, as reported by the national inventory. A comparison of biomass loss between our estimates and global biomass/burned areas data indicates that higher resolution improves the identification of small fire patches, reduces the commission errors with a more accurate delineation of the perimeter of each fire, and increases the biomass affected. This study paves the way for the development of low-latency, high-accuracy assessment of biomass losses and fire patch contours to deliver a more informative impact-based characterization of each fire year. Published by Copernicus Publications on behalf of the European Geosciences Union.
, 2013; Leenhouts, 1998). Statistical distribution  (Prichard et al. , 2019)  or spatial interpolations  (Keith et al. , 2014)  of biomass using plot data from forest inventories could bring an improved description of the spatial variation of biomass losses but still lack precise locations to be crossed with the actual burned area location. Integrative models of fire emissions combining burned area datasets, land cover, seasonal ecosystem functioning, and a simulation of the biomass carbon pools affected by fires have been applied globally at 0. 25 • resolution in the GFED model  (Randerson et al. , 2017). A finer resolution of 500 m was recently achieved with this method and constitutes key information for global studies  (van Wees et al. , 2022). However, this resolution is still too coarse to capture small fires in Europe.
, 2021). Hence, we propose in this study to combine a highresolution exhaustive dataset of fire contours from remote sensing and a new map of tree height at a high resolution (10 m) converted to biomass using local forest inventory plot data to assess the biomass loss in French forests during the fire seasons of 2020 to 2022. We discuss the uncertainties of our approach and assess the benefits of high-resolution burned area and high-resolution biomass maps compared to existing approaches obtained at coarser resolution. By adopting a multifaceted approach that includes a detailed description of the fire season's distinctiveness, we will revisit the conclusions drawn from the 2022 fire season in France.
Fire polygons -BAMTs method Due to the lack of a reliable and available fire contour dataset over the country, we developed our own fire polygon dataset with BAMTs (Burned Area Mapping Tools), a semi-automated method of fire contouring at high resolution  (Bastarrika et al. , 2014; Roteta et al. , 2021). We processed atmospherically corrected and orthorectified images from the L2A product of ESA's Sentinel-2 mission between 2020 and 2022. The BAMTs method involves the calculation of three spectral indices: Normalized Differential Vegetation Index (NDVI)  (Rouse et al. , 1974) , Normalized Burn Ratio (NBR)  (Key and Benson, 1999) , and NBR2  (García and Caselles, 1991). Each fire was first spatially and temporally located using NASA's Fire Information for Resource Management System (FIRMS) or national registration from the French Base de Données des Incendies de Forêt en France BDIFF official fire registration (BDIFF, 2023) to target a BAMTs processing zone.
A visual inspection follows this semi-supervised procedure to confirm that the detected perimeters were indeed burned areas. This key step is hardly provided by automated methods and helps to reach the international standard recommended by the CEOS (Committee for Earth Observation Satellites) Working Group on Calibration and Validation of remote sensing datasets  (Franquesa et al. , 2020). By focusing on forest fires larger than 30 ha, a total of 113 fire polygons were obtained over the 2020-2022 period of analysis (Fig. 2  shows three examples of a BAMTs fire polygons). We selected only fire polygons that dominantly occurred in forests and shrublands, in turn re-moving fires occurring on pastures and grasslands, and thus matching the French forest fire database BDIFF.
The Global Ecosystem Dynamics Investigation (GEDI) mission is a spaceborne infrared lidar on the International Space Station (ISS). It provides energy return waveforms (L1B product) and derived metrics such as canopy relative height (RH) (L2A product) that describe the vertical forest structure within 25 m diameter circular footprints. In this case, we used RH95 height metrics as the reference height in order to train our model. We downloaded all GEDI footprints available for France since the beginning of the GEDI mission and filtered them using the quality flag provided by NASA. U-Net is a fully convolutional network (FCN) widely used in deep learning for image segmentation tasks. Here, we adapted this model for pixel-wise height regression, with GEDI data as the reference height and S1/S2 images as predictors.
We cropped the tree height map at 10 m resolution from  Schwartz et al. (2022)  within the BAMTs fire polygons, keeping only the 10 m pixels with height values higher than 3 m. -Step 2: NFI plots around the fire patch. Data collected since 2005 on more than 100 000 plots were used. The morphological characterization of more than 1. 7 million trees allowed the establishment of allometric relationships for each species. For each fire patch, the NFI plots located at a maximum distance of 5 km outside the fire perimeter were selected. This buffer made it possible to rely on a sufficient number of plots ranging from a minimum of 5 plots to 278 (median = 41). -Step 3: DBH (diameter at breast height) ∼ height allometry. We established an allometric relationship between the diameter at breast height (DBH NFI ) and the tree height (H NFI ) from NFI measurements for each tree of the dominant species in each plot (Eq.
1). The parameter p is species dependent and affects the relationship between height and diameter for the dominant species considered. We computed p for each fire patch based on all surrounding NFI plots. DBH NFI = α p H 2 NFI π (1) -Step 4: DBH map. We applied Eq. (  2 ) to the 10 m×10 m resolution tree height values from Schwartz et al. (  2022 ) (H x ) to compute the DBH of the highest tree for each 10 m pixel (DBH x ). DBH x = α p H 2 x π (2) -Step 5: height ∼ biomass allometry. To estimate the aboveground biomass of the highest tree in each pixel (AGB x ) from its DBH, we used allometric relationships from the R package allodb  (Gonzalez-Akre et al. , 2022)  for each species. This tool compares the allometric relations of different studies and builds a new relationship according to the taxonomic and geographical information provided.
log(D x ) = log(AGB tx ) -k p -3 2 (4) In Eq. (  4 ), the parameter k p has a specific value for each fire patch and was estimated by fitting the equation to all the trees in NFI plots surrounding each fire patch (see Appendix A for more details). Finally, we obtained the aboveground biomass of a forest pixel (AGB fx ) by multiplying the density D x of the pixel by the biomass of the highest tree of this pixel (AGB tx ) as shown in Eq. (  5 ). We assume here that our high-resolution 10 m dataset highly limits tree height variability within the pixel so that the difference between the average tree height and maximum tree height is below the uncertainty of the maximum tree height (2. 75 m) extracted from  Schwartz et al. (2022). AGB fx = AGB tx • D x , (5) which led to Eq. (  6 ) expressing the forest biomass of a 10 m × 10 m pixel as a function of the pixel height (H x ) and other fire-patch related parameters (α p and k p )  (Chan et al.
-Step 8: biomass of short sclerophyllous vegetation. The Mediterranean vegetation composed largely of sclerophyllous shrubs (mostly maquis and garrigues) was identified by the CORINE land cover dataset  (SDES et al. , 2019)  as the sclerophyllous vegetation class. Within this CORINE class, all burned 10 m pixels with vegetation higher than 3 m were treated like forests to calculate AGB-L. The pixels with vegetation shorter than 3 m were considered to be non-forests. In those pixels, our GEDI-based tree height map is not reliable for estimating vegetation height and biomass because the height of short vegetation cannot be separated from bare soil. We thus assigned a fixed value of 10 t ha.
Comparison with other burned area and biomass datasets The calculation of AGB-L described in Sect. 2. 4 was applied on the 113 fire patches from BAMTs. The same calculation has been realized for the whole region outside the burned areas (see Sect. 2. 1) to get a rough estimation of living biomass compared to burned one. We also computed AGB-L using two global burned area products and three global height or biomass products to compare with our high-resolution results (Table  1 ). The 113 fire polygons from BAMTs were compared, when available, with the national fire polygons estimates from the EFFIS (San-Miguel-Ayanz et al. , 2012) and MCD64A1 burned area  (Key and Benson, 2005; Giglio et al. , 2018). Because the BAMTs fire dataset focused on forests and shrublands, a large amount of fires and total burned area occurring on pastures and grasslands were removed from the EFFIS and MCD64A1 data.
While BAMTs relies exclusively on image analysis from the Sentinel-2 sensors, the EFFIS product relies on both hotspots obtained by the MODIS sensor and Sentinel-2 images crossover. MCD64A1 (500 m resolution) relies solely on the MODIS sensor. The AGB-L from each patch from our study was compared with AGB-L calculated from global tree height maps at 30 m resolution delivered globally by  Potapov et al. (2021)  and processed according to the same method as presented in Sect. 2. 4 for the Schwartz tree height map. We also used AGB-L directly sampled within each patch from the ESA CCI Biomass dataset at 100 m resolution for the year 2018  (Santoro and Cartus, 2021). It should be noted that this AGB product considers all the biomass compartments, including foliage and understory vegetation, whereas our AGB product considers only woody tree biomass.
Finally, AGB-L obtained in this study was compared to the (burned) wood carbon pool of the global 500 m resolution fire emissions dataset provided by  Van Wees et al. (2022) , based on the GFED framework and with biomass cycling simulated by the CASA model  (van Wees et al. , 2022). All these alternative products have a coarser resolution than our burned area and AGB-L maps (Table  1 ). Overall, we compared our results with an ensemble of three burned areas maps times four AGB maps, thus 12 different estimates of AGB-L for each of the three forest regions.
Comparison of burned area and biomass loss datasets The refined BAMTs method (see Sect. 2. 2) shows differences in the detection rate and total area of fire polygons compared to the EFFIS and MCD64A1 products (Table  2 ). In contrast to BAMTs, EFFIS detected only 89 % (101 fires) of the 113 fires due to a lower detection capacity of small fires (< 100 ha), as only 51 of 63 fires were recorded (81 %). However, all fires > 100 ha were detected. Despite its limited capability to detect small fires, EFFIS reported a much higher total burned area of 62 002 ha over the period of 2020-2022 compared to 56 053 ha in the BAMTs method. This 10 % overestimation by EFFIS was primarily due to the overestimation of large fires (> 500 ha), with the total burned area of the 16 largest fires of 2020-2022 being 4649 ha (+10 %) higher.
On the other hand, the MCD64A1 product presents a higher burned area in the same sample of 113 fires de- tected by BAMTs, despite its lower detection rate of only 39 % (44 fires), which is much lower than EFFIS. Although MCD64A1 was capable of detecting almost all large fires (> 500 ha), it was unable to achieve the same accuracy on intermediate fires (100-500 ha), with a detection rate of 68 % (23 fires), and even less on small fires (< 100 ha), with a detection capability of only 10 % (6 fires). Over the matching fire events, we observe that MCD64A1 tends to overestimate the burned area for fires larger than 500 ha by 16 % (Fig. 9 ). Regarding our estimate of AGB-L (Sect. 2. 4), we found higher values than the one obtained from global products (Fig. 10 ). When our biomass estimation method is applied to the tree height data from  Potapov et al.
(2021) , we estimate that the AGB-L is 8 % lower. This difference reflects the propensity of our 10 m resolution tree height map to capture higher trees and their spatial variability within forest patches, including forest edges. Secondly, although the ESA CCI Biomass data take into account the fine elements of the trees (branches, leaves), it provides a much lower estimate of AGB-L (-32 %). This is particularly related to the underestimation of the biomass of Mediterranean ecosystems. Finally, the Van Wees et al. (  2022 ) AGB-L seems relatively close to our estimates (-9 %) despite a much lower resolution. The major difference also appears in the Mediterranean region, for similar reasons as for ESA CCI Biomass. In conclusion, the lower-resolution products tend to estimate a higher burned area but a lower biomass being affected.
Uncertainties in burned area and biomass estimates Our study represents a significant step towards the development of a national monitoring system for burned area polygons at high resolution for fires larger than 30 ha. This type of dataset is not yet available either for burned areas estimations delivered by forest services and previously used in scientific studies  (Ruffault and Mouillot, 2017)  or for regional/landscape applications relying on coarse-resolution global datasets  (Barbero et al. , 2019)  or hand-drawn fire contours  (Mouillot et al. , 2003; Ganteaume and Barbero, 2019). To ensure the reliability of our dataset, we visually checked and adjusted each fire contour following international standards  (Franquesa et al. , 2020). We add a reminder here that pixel reflectances are mostly driven by the upper canopy so that NBR and other indices, used in BAMTs, have been shown to hardly capture understory fires  (Roy et al.
, 2006; Morton et al. , 2013). This "weakness" is shared by all global burned area datasets, as they use the dNBR (difference normalized burn ratio) index in a similar approach. Then, the mismatches we observed in burned areas across BA datasets is mostly a consequence of pixel resolution and algorithm construction. Recent advances using lidar may be of use in the future to better consider understory fires  (East et al. , 2023). We also set the seed of observation from registered fire events by French forest services (BDIFF), potentially non-exhaustive, thus completed by thermal anomalies (this latter source being the only source for 2022, before fire events are published by official statistics) following  Majdalani et al. (2022). Our estimate of a forest burned area of 42 520 ha was lower than the official 66 363 ha provided by EFFIS for 2022, as we focused our study on forest and shrubland fires.
This is a major limitation when comparing the registered national burned area's statistics with remote sensing data not filtered by land cover, as pointed out by  Turco et al. (2019). EFFIS provides a total burned area assembling all sources of fires without filtering out fire types, information that is largely directly used by the media. To ensure consistency with BDIFF, the French fire observation system, we focused on forest or shrubland fires, voluntarily omitting pasture and agricultural fires where uncertainty is still high  (Hall et al. , 2021). We warn here about the use of raw data provided by remote sensing services without mentioning a proper description of vegetation types affected. When filtering out pastoral fires, covering roughly 15 000 ha and occurring between February and April when prescribed burning is allowed in the Pyrenees and central France, EFFIS detected 48 328 ha, much closer to our estimates, with a 10.
6 % overestimation, a result actually consistent with  Llorens et al. (2021). Mostly, EFFIS tends to produce a smooth external envelope of the burned polygons and ignores unburned patches within the fire patch. Still, EFFIS remains a reliable dataset when this limitation is known. We observed a much greater uncertainty on burned areas with global remote sensing datasets at coarse spatial resolution (500 m). Mostly, small fires < 100 ha were not observed by MCD64A1, fires between 100 and 500 ha were underestimated, and fires larger than 500 ha were overestimated by 10 % with the same reason as for EFFIS, as they provide an external envelope omitting unburned internal patches. This caveat and the fire size threshold for reliability are consistent with  Nogueira et al. (2016) , or  Katagis and Gitas (2022)  and  Galizia et al.