dataesr/scientific-paragraphs-categorization huggingface dataset:
To answer this question and to explore the contribution of nonforest trees to tree cover and woody biomass across Europe, here, we generate continental-scale maps of forests, woodlands, and trees outside forests, including their height at 3-m resolution in 2019, which contains a level of details otherwise only previously accessible from the use of airborne LiDAR surveys. We retrieve these data from cost-efficient nanosatellites available at a daily basis and at a spatial resolution high enough to map large individual trees (>3-m height) using a deep learning approach where a convolutional neural network is learned end to end from airborne LiDAR canopy height reference data across Europe. We trained two deep learning models from PlanetScope optical imagery at 3-m resolution: (i) a segmentation model predicting binary canopy/nocanopy (>3-m height) and (ii) a regression model predicting canopy height.
Tree crowns and their shadows are visible in Planet-Scope images, representing key features when training deep learning methods to retrieve canopy heights. Furthermore, we use inventory data to convert tree cover and height into aboveground biomass maps including both forest and non-forest tree biomass, forming the basis for quantifying and comparing forest and nonforest tree resources across European countries. We further apply the model trained in Europe to the boreal and temperate zones of North America to test the scalability of the approach to different areas evaluated on independent datasets.
33 m and relative RMSE (rRMSE) = 21. 7%] and temperate zones (RMSE = 5. 06 m and rRMSE = 27. 8%), but the diverse tree structure of Mediterranean regions results in a higher uncertainty (RMSE = 6. 44 m and rRMSE = 32. 2%), observed as a more severe underestimation at higher canopies (>30-m height) (Fig. 2A ), which can be partially explained by the uneven distribution of the validation data, with a lower number of points for higher canopies (Fig. 2B ). Aggregating tree cover and height to 1 km-by-1 km grids, we found that the overall systematic bias (see Materials and Methods for equation) between our PlanetScope predictions and LiDAR data for areas outside forests, here defined as trees that we mapped in the "urban" and "cropland" classes using a global land cover and land use (GLCLU) dataset  (30) , was -1.
6% for canopy height and -2. 6% for canopy cover. For closed canopy forests, here defined as areas mapped as forest in the GLCLU dataset, the bias was -1. 2% for canopy height and +0. 4% for canopy cover (Fig. 2C ). When comparing two global canopy height products with airborne LiDAR-derived canopy cover and height, the systematic bias was high, particularly for trees outside forests: Here, the bias for canopy height and cover was +15. 4 and +231. 2%, respectively, for a fused Sentinel-2-and spaceborne LiDAR [Global Ecosystem Dynamics Investigation (GEDI)]-based map at 10-m resolution  (26) , while it was -31. 3 and -95. 8% for canopy height and cover, respectively, for a Landsat-and GEDI-based map at 30-m (Fig. 2D )  (24).
Quantification of tree cover outside forests across Europe To refine the forest versus non-forest area definition in relation to our dataset, we used the FAO definition  (21)  to separate forests from non-forest trees. Here, we aggregated our PlanetScope-based tree cover and tree height maps into 0. 5-ha grid cells. Following the FAO definition, woody vegetation in 0. 5-ha grids where the canopy cover of trees taller than 5-m height exceeded 10% and where the land use was not agricultural land or urban according to  (30)  was considered as forest. Note that the FAO definition includes areas of trees that potentially can grow above 5 m, which cannot be resolved for our map. Conversely, trees in 0. 5-ha grids where the cover of trees taller than 5 m was below 10% or where the land use was urban or agricultural land were considered as trees outside forests.
Quantifying aboveground biomass across Europe To quantify the woody resources into aboveground biomass, we used plot-based National Forest Inventory (NFI) data (fig. S13 ) and airborne LiDAR canopy height model (CHM) data from Denmark  (31)  to establish allometric relationships between aboveground biomass and canopy height, both averaged at the plot level (n = 11,296). Separate relationships were derived for broadleaf forest (n = 7232; bias = -8. 8%), coniferous forest (n = 3768; bias = -6. 8%), mixed forest (n = 7536; bias = -9. 9%), and plots with sparse tree cover (n = 409; bias = -5. 9%) (fig. S6 ) using a previously published forest-type map from 2018 (32) for the separation (see Materials and Methods). We aggregated aboveground biomass to the hectare level (100 m by 100 m) including both forest and non-forest trees (Fig.
4 ). The overall uncertainty is the combined uncertainty due to the canopy area and height prediction and to the height to biomass conversion and was quantified by comparing our final aboveground biomass product (after application of the allometric conversion on PlanetScope canopy height and cover data) with field measured biomass from the Danish (n = 3451) and Spanish (n = 1706) NFI data at the plot scale and the country scale (n = 30). At the plot scale, the correlation was moderate [correlation coefficient (r) = 0. 53 and bias = -23% for Denmark; and r = 0. 50 and bias = -25% for Spain] (fig. S7, C  and D ), which was expected because of a large uncertainty related to both satellite and field data, such as geolocation and image quality errors. These errors were found to be not systematic, as demonstrated by a comparison of statistics aggregated to the country scale, both from NFI and satellites.
Beyond Europe To test the scalability of our approach, we applied the tree cover segmentation and canopy height prediction to the boreal and temperate zones of North America and tested the performance with a fully independent LiDAR dataset (1000 km 2 at 1-m resolution) that was never seen during training and parameter optimization (Fig. 6 ). Errors were found to be comparable to the European test datasets if aggregated to 1 km-by-1 km samples [coefficient of determination (R 2 ) = 0. 71, bias = -15. 7%, and rRMSE = 20% for height regression; and R 2 = 0. 88, bias = +2%, and rRMSE = 28. 6% for tree cover segmentation]. At 3 m by 3 m, the overall RMSE was higher than in Europe (6. 5 m). Note that a conversion to biomass would requires region-specific calibrations with local field data, which is beyond the scope of this study.
Our maps could be operationally integrated in national carbon stock inventory schemes. First, the spatial resolution allows to identify individual tree crowns and makes it possible to align the satellite image with field plot data, which can be useful to reduce variances when upscaling plot data on, e. g. , carbon stocks to the national scale. Using different canopy height-to-biomass conversions for different forest and non-forest types account for the bias caused by the forest understory, which is undetected by classic aerial surveys. Aggregating our biomass maps to the national scales shows a good alignment with national NFI statistics (bias of 7. 7% overestimation for 30 countries) (5), although NFI plot-level data were only available for Denmark. Second, while the imagery is not free of charge, it is cheaper than aerial imagery, making it feasible to be acquired annually at the national scales.
Summary We used CHMs from airborne LiDAR data across Europe to train two deep learning models: a segmentation model predicting binary canopy/no-canopy (for trees taller than 3-m height) and a regression model predicting canopy height, both from PlanetScope optical imagery at 3-m resolution. The canopy map was used as a mask, and canopy height was only predicted within areas identified as tree cover. We included a GEDI-and Sentinel-2-based canopy height map from (  26 ) as an additional model input, in both the training and prediction processes, providing a priori knowledge on the height distribution of forest areas. We observed that this improved the underestimation of tall forest trees and that, in case of temporal or spatial mismatches (e. g. , clear-cuts and trees outside forests), the prediction followed the PlanetScope image overruling the information from the GEDI/Sentinel-2 map.
Here, it is important to mention that the map from (  26 ) severely overestimates trees in sparsely vegetated areas, which, however, did not affect our result because we applied a binary canopy map at 3 m as mask (fig. S4 ). We used Danish NFI data to establish allometric relationships between plot-based aboveground biomass and height (>1. 3 m) from aerial CHMs averaged over the field plot. The relationship between canopy height and biomass was then used to convert canopy height and cover predicted from PlanetScope imagery into biomass at 30-m resolution (same as NFI plot), which was lastly aggregated to the hectare level. We did not use the field measured height but the average vegetation height from the LiDAR data per plot because it comes closest to our predicted height. We also did not use PlanetScope predicted height because of alignment uncertainties with the field plots at 3-m resolution.
Airborne LiDAR canopy height maps We collected publicly available airborne LiDAR CHM data in Europe and the United States, with full country coverage for Denmark (0. 4 m-resolution merged from 2016 to 2021, in total 43,000 km 2 ), Estonia (1-m resolution from 2011 to 2017, in total 45,000 km 2 ), The Netherlands (0. 2-m resolution from 2018, in total 41,000 km 2 ), and Spain (2. 5-m resolution from 2008 to 2015, in total 500,000 km 2 ). Other samples were available for patches in Finland (1-m resolution from 2019, in total 50,000 km 2 ), Switzerland (0. 5-m resolution merged from 2017 to 2020, in total 20,000 km 2 ), and Wales (1 m-resolution from 2015, in total 10,000 km 2 ) (fig. S1B ). Some data were provided as CHM, others as raw cloud points, which were converted to Digital Terrain Model (DTM) and Digital Surface Model (DSM) used to calculate CHMs via LAStools  (43).
CHM data represent the top canopy height in the pixel area but, sometimes, include the height from buildings and short herbaceous and bush vegetation. Here, we used the building footprints from Microsoft  (44)  and Open Street Map  (45)  to mask buildings. We further visually compared Planet-Scope imagery and the corresponding CHM and decided for a 3-m height threshold to separate tree crowns from shrubs, bushes, and grasses. Woody plants below this threshold were not clearly visible in the PlanetScope images.
Training sample generation To adjust airborne CHM with PlanetScope images, we reprojected the PlanetScope imagery to match the aerial data Universal Transverse Mercator (UTM) projection and resampled the aerial CHM to the spatial resolution of PlanetScope data (3 m) by reserving maximum values (filling small gaps in forests in the aerial CHM, which are not visible in PlanetScope imagery). We applied a weighted sampling strategy to the aerial CHM data, according to collection year, mean height, tree cover percentage (area > 3 m), forest type [coniferous or broadleaf (32)], and dominating land use/cover classes, to control the training data distribution fitting into the model and to minimize possible mismatches between data sources caused by acquisition time differences (fig. S11 ). The final dataset contains 100,000 samples, each 328 × 328 pixels corresponding to 1 km-by-1 km ground spacing, where 10% was used for validation, 10% as test dataset, and 80% for training the model.
In the decoder, we replaced the simple two convolutional layers in the U-Net by residual blocks  (49) , which have been demonstrated to improve the model performance for segmenting small objects  (50). Furthermore, spatial and channel squeeze and excitation blocks were integrated in the decoder to build attention mechanisms  (51). The deep learning framework is built on an open-source segmentation library  (52). Mismatches between aerial CHM and PlanetScope data were sometimes observed in forested areas, when the images were not acquired from the same date. These inconsistencies cumulatively contribute to a substantial proportion of loss in the regression, resulting in an unstable training progress and large uncertainties in the tree height estimations. Therefore, we first segmented the tree canopies, which were then used to mask the non-forested areas for minimizing the regression loss (fig.
Tree crown segmentation We set a 3-m height threshold to the aerial CHM data and converted it into a reference binary (tree or background) map. The four-band input data from PlanetScope were augmented by random cropping, flipping, and brightness distortion and then normalized channelwise based on training dataset statistics before feeding into the model. We used the focal Tversky loss (53) as loss function for the segmentation model, and the alpha and beta parameters in the Tversky index (54) penalizing false positives and false negatives were set to 0. 4 and 0. 6, respectively. The focal parameter was set to 1. 2, which incentivizes the model to focus on harder examples (when the Tversky index < 0. 5), especially for the cases where scattered trees or tree lines do not exactly overlap between PlanetScope and aerial CHM data.
Tree height regression We used the same network architecture and training strategy as for the segmentation task, with an inverse sample-frequency weighting smooth-L1 loss function to mitigate the underestimation that is often observed for tall trees  (26). The regression loss was only calculated for areas that overlap between the segmentation tree mask and the aerial CHM (>3 m). While this largely eliminated uncertainties from mismatches between the two data sources, the model still underestimated taller forests, possibly resulting from unclear training data. For example, coniferous forests vary optically only little in PlanetScope image but can have different heights in the CHM, depending on their location. As a consequence, whenever the model encounters this situation, it conservatively predicts intermediate values.
S6 ). We acknowledge that the class representing the non-forest trees (nf ) is not ideal, because the Danish NFI does not systematically measure trees outside forests and other wooded lands. However, here, we assume that plots being located in areas of sparse tree cover and having a low biomass are most representative for nonforest areas. Because the NFI biomass data include all trees taller 1. 3 m and not only those visible from an aerial perspective and in the PlanetScope images (typically >3-m height and 10-m 2 crown size), the different relationships include understory or small trees, which indirectly correct for the bias of missing small trees in the PlanetScope-based tree height predictions. This fact makes it important to have a separated equation for trees outside forests, which typically have a lower amount of understory.
Evaluation We randomly selected 10% of the CHM dataset (10,000 1 km-by-1 km samples) as independent test dataset, which was not used for training and to optimize the model parameters during training, neither for the tree cover segmentation nor for the tree height regression tasks. We evaluated our results at pixel level (3 m by 3 m) using the intersection over union (IoU), which quantifies how well the predicted boundaries align with the ground truth boundaries, for the segmentation and the RMSE for tree height regression and aggregated to 1 km-by-1 km grids using the relative systematic error (bias %, Eq. 7); positive bias represents an overall overestimation, and negative bias represents an overall underestimation (56) bias ¼ 1 N X N i¼1 Y pred À Y true Y true � � � 100 ð7Þ and the rRMSE in percentage described in Eq.
8 rRMSE ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi 1 N X N i¼1 Y true À Y pred Y true � � 2 v u u t � 100 ð8Þ Tree cover segmentation Overall, our model achieved an IoU of 0. 692 on the test dataset (n = 10,000 plots of 1 km by 1 km). Further evaluation revealed a good alignment between our tree cover map and the tree cover map from aerial CHM data (using a 3-m height threshold) with overall R 2 values of 0. 92 but with variation across biomes (fig. S2E ). Trees can be segmented more precisely in both boreal (R 2 = 0. 97, bias = -1. 0%, and rRMSE = 13. 5%) and temperate zones (R 2 = 0. 99, bias = -0. 8%, and rRMSE = 18. 5%) than in the Mediterranean zone (R 2 = 0.
39, bias = -8. 2%, and rRMSE = 89. 8%), which is likely due to two reasons: First, a limited amount of training data was usable from Spain because of the large temporal gap between PlanetScope imagery and the available aerial CHM data, which is from 2012. Second, the method of using a constant height threshold to generate the tree mask is not fully functional in diverse vegetation structures consisting of trees and shrubs. For areas with trees outside forest, our predictions compared with aerial CHM data achieve an overall bias = -2. 4% and rRMSE = 36. 6% (fig. S3  and Fig. 2D ). We also calculated a confusion matrix comparing Danish NFI plots (n = 13,638) classified as either forest or no forest with our data following the FAO definition, showing a very high agreement with an overall accuracy of 0.
Tree height regression We evaluated our tree height model by a residual analysis between reference height from the test dataset and the corresponding predicted tree height at pixel level (3-m resolution) (Fig. 2A ). The model performance varied across biomes and canopy height ranges yet was closely related to the data distribution (Fig. 2B ). In general, the model did not show a considerable saturation for taller canopies, yielding an RMSE of 5. 4 m and an mean absolute error of 4. 2 m. We found similar metrics as for the segmentation across biomes at plot-level evaluation, with R 2 = 0. 82, bias = -2. 4%, and rRMSE = 17. 9% for the boreal zone; R 2 = 0. 9, bias = -1. 6%, and rRMSE = 14. 5% for the temperate zone; and R 2 = 0. 45, bias = -20. 1%, and rRMSE = 35. 4% for the Mediterranean zone (fig.
S2D ). All slopes below the diagonal line indicated a slight underestimation, consistent with the pixel-level evaluation (Fig. 2A ). As for trees outside forests, we obtained a bias of -1. 6% and rRMSE of 26. 4% (fig. S3A ). We further evaluated our top height estimation with field measured plot scale height from Danish NFI data (n = 3451) (fig. S7B ), showing an underestimation bias of 9%.
North America To test the generalization of our model, we then predicted tree cover and height for the boreal and temperate zone of North America and compared the results to aerial CHMs that were not used for either training or validation of the model parameters. Here, we acquired around 1000 km 2 of airborne LiDAR CHMs at 1-m resolution from (59) and generated 1036 samples with 1 km-by-1 km ground spacing following the same data processing procedure as described in the previous section. These evaluations show that our model can be deployed to a comparable landscape without a considerable loss of performance (R 2 = 0. 71, bias = -15. 7%, and rRMSE = 20% for height regression; and R 2 = 0. 88, bias = +2%, and rRMSE = 28. 6% for tree segmentation) (Fig. 6 ).
Evaluation of the allometry We used NFI data from Spain (fig. S13 ), mostly located in the region of Extremadura (768 plots) measured in 2016 and 2017, and the province of Leon in the northwestern part of Spain (1162 plots), measured in 2019. The Spanish NFI measurements represent an important remeasurement campaign on which existing NFI plot locations have been remeasured with upgraded global navigation satellite system equipment to improve the geolocation of plot centers in existing and new plots  (60). Airborne laser scanning data were collected in Leon from October 2019 to May 2020 and in Extremadura from October 2018 to July 2019, which largely match with the NFI measurements. In addition, we used 1284 plots that include a diverse mosaic of Mediterranean evergreen and broadleaf forests  (61).
Environmental and auxiliary data We used a GLCLU map from 2019 at 30 m (30) including forests, urban areas, cropland, and grassland to analyze trees outside forests and trees in different landscapes. We also used global canopy height products based on Sentinel-2 from 2020 at 10-m resolution  (26)  and another product based on Landsat from 2019 at 30-m resolution  (24)  for comparison. Both products use GEDI spaceborne canopy height data that were extrapolated with Sentinel-2 and Landsat, respectively. The GEDI/Sentinel-2-based canopy height map was also used to assist the height regression task. The Copernicus forest-type map based on Sentinel-2 data from 2018 at 10-m resolution and a PROBA-V-based forest-type map from 2019 at 100 m (32) were used to separate forests into coniferous and broadleaved forest across Europe.
Acknowledgments Funding: This research was supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 947757 TOFDRY ) and a  DFF Sapere Aude grant  (no. 9064-00049B ). S. Liu is funded by  China Scholarship Council (CSC ; grant no. 202104910123 ). R. F. , C. I. , and N. L. acknowledge support by the  Villum Fonden  through the project  Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics (DeReEco ; grant no. 34306 ). C. I. acknowledges support by the  Pioneer Centre for AI ,  DNRF  grant number P1. Y. Y. and M. B. acknowledge support by the  International Partnership Program of Chinese Academy of Sciences (CAS)  ( 092GJHZ2022029GC ) and  CAS Interdisciplinary Team  ( JCTD-2021-16 ).
The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote sensing measurements (NASA's GEDI LiDAR mission and ESA's Copernicus Sentinel 1 & 2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha -1 ) of France (FORMS-B).
Extensive validation was conducted. First, independent datasets from Airborne Laser Scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2. 94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59. 6 Mg ha -1 and 19. 6 Mg. ha -1 respectively, providing greater performance than other AGBD products sampled over France. These results highlight the importance of coupling remote sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable.