Current large-scale satellite-based forest disturbance monitoring systems operate at 10–30 m resolution, which is too coarse to detect changes at the scale of individual trees resulting in a systematic underestimation of forest disturbances. Here, we introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024), country-scale mapping of forest canopy height at 1.5 m resolution over France, together with FORMSpoT-Δ, annual disturbance polygons derived from height differences in the FORMSpoT time series.Canopy heights were derived from annual SPOT-6/7 composites using a hierarchical transformer model (PVTv2) trained on high-resolution airborne laser scanning (ALS) data. To enable robust change detection, we developed a post-processing pipeline combining co-registration and spatio-temporal total variation denoising.We find that (1) the French disturbance regime is dominated by small events. Sub-100 m2 disturbances alone represent 72% of all events, and disturbances below 0.1 ha account for 97% of events and 39% of the disturbed area. These events are largely missed by Sentinel-1/2 and Landsat-based products. (2) Validated against successive ALS revisits across 19 sites and 5087 NFI plot revisits, FORMSpoT-Δ provides reliable detection (F1 > 0.8) above 100 m2 while retaining sensitivity to finer events that coarser products do not capture. (3) At the national scale, FORMSpoT-Δ resolves contrasted disturbance regimes, from clear-cut-dominated dynamics in maritime pine plantations to diffuse, smaller disturbance events in mountain forests, and captures their temporal dynamics, including the salvage-logging signature of the 2017–2022 bark beetle crisis in northeastern France.

The Black Woodpecker (Dryocopus martius, L. 1758) is the largest primary cavity excavator in Europe. Its cavities represent an essential microhabitat for many other forest species and the knowledge on landscape factors linked with cavity excavation by the Black Woodpecker is needed to support the conservation of this species and associated species. Such relationships should thus be quantified at different scales ranging from the stand to the extended home range. We used cavity maps established by foresters and naturalists to build a large (2689 cavity bearing trees) database distributed over several sites in France. Based on this and on a set of background points, i.e. randomly selected points devoid of cavity in the vicinity, we analysed the effects of stand composition and landscape features (forest cover, forest connectivity and fragmentation) at three different scales around each cavity and background point corresponding to a forest management unit (10ha), the core (100ha) and extended (250ha) home range scales. We showed that indices describing forest continuity (cohesion, landscape shape index) and forest tree species composition (especially the presence of mixed forests) had significant positive effects but that the magnitude varied across the three scales. We notably observed the strongest effects at the core home range scale (100ha), indicating that Black Woodpecker requirements for cavity excavation are more pronounced at this scale. The Black Woodpecker tends to avoid pure conifer-dominated stands to excavate cavities, but benefits from mixed forests, that couple favourable foraging and cavity excavation sites. The bird also prefers continuous forest landscapes with high cohesion and low edge densities. We also showed that the positive effects of forest landscape were generally larger at higher elevation, indicating context-dependence. Forest planning rarely integrates the landscape patterns. A better understanding of the features linked with cavity excavation by the Black Woodpecker may hence help to better integrate their conservation in forest management planning. Our results also show the importance to maintain mixed broadleaf-conifer forests as well as continuous and well-connected forest landscapes to favour features that benefit primary and secondary cavity nesters.

Several regional initiatives and reporting efforts assess the state of forest biodiversity through broad-scale indicators based on data from national forest inventories. Although valuable, these indicators are essentially indirect and evaluate habitat quantity and quality rather than biodiversity per se. Therefore, their link to biodiversity may be weak, which decreases their usefulness for decision-making. For several decades, Forest Europe indicators assessed the state of European forests, in particular their biodiversity. However, no extensive study has been conducted to date to assess their performance - i.e. the capacity of the indicators to reflect variations in biodiversity - against multitaxonomic data. We hypothesized that no single biodiversity indicator from Forest Europe can represent overall forest biodiversity, but that several indicators would reflect habitat quality for at least some taxa in a comprehensive way. We tested the set of Forest Europe's indicators against the species richness of six taxonomic and functional groups across several hundreds of sampling units over Europe. We showed that, while some indicators perform relatively well across groups (e.g. deadwood volume), no single indicator represented all biodiversity at once, and that a combination of several indicators performed better. Forest Europe indicators were chosen for their availability and ease of understanding for most people. However, we showed that gaps in the monitoring framework persist, and that surveying certain taxa along with stand structure is necessary to support policymaking and tackle forest biodiversity loss at the large scale. Adding context (e.g. forest type) may also contribute to increase the performance of biodiversity indicators.

Most broad-scale forest biodiversity indicators are based on data from national forest inventories and are used to assess the state of biodiversity through several regional initiatives and reporting. Although valuable, these indicators are essentially indirect and evaluate habitat quantity and quality rather than biodiversity per se . Besides, most of these indicators are applicable at regional or national scales, while their use at a more local level is difficult. Therefore, their link to biodiversity may be weak, which decreases their usefulness for decision-making.For several decades, Forest Europe indicators assessed the state of European forests, in particular its biodiversity. However, no extensive study has been conducted to date to assess the performance of these indicators against multitaxonomic data. We hypothesized that – as implied by the reporting process – no single biodiversity indicator from Forest Europe can represent overall forest biodiversity, but that several – eventually combined – indicators would reflect habitat quality for at least some taxa in a comprehensive way. We tested the set of indicators proposed by Forest Europe against the species richness of six taxonomic and functional groups (tracheophytes, epixylic and epiphytic bryophytes, birds, saproxylic beetles, saproxylic non-lichenized fungi and epixylic and epiphytic lichenized fungi) across several hundreds of plots over Europe. We showed that, while some indicators perform relatively well across groups (e.g. deadwood volume), no single indicator represented all biodiversity at once, and that a combination of several indicators performed better. Surprisingly, some indicators showed weak links with the biodiversity of the six taxonomic and functional groups.Forest Europe indicators were chosen for their availability and ease of understanding for most people. However, our analyses showed that there are still gaps in the monitoring framework, and that surveying certain taxa along with stand structure is necessary to support policymaking and tackle forest biodiversity loss at the large scale.Impact statement Biodiversity indicators used to assess the state of Europe’s forests perform unequally; a combination of indicators is more informative### Competing Interest StatementThe authors have declared no competing interest.

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at https://doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).

Sensitivity of lidar metrics to scan angle can affect the robustness of area-based approach (ABA) models, and modelling the interplay of scan geometry and terrain properties can be complex. The study hypothesizes that neural networks can manage the interplay of lidar acquisition parameters, terrain properties, and vegetation characteristics to improve ABA models. The study area is in Massif des Bauges Natural Regional Park, eastern France, comprising 291 field plots in a mountainous environment with broadleaf, coniferous, and mixed forest types. Field plots were scanned with a high overlap from multiple flight lines and the corresponding point clouds were considered independently to expand the standard ABA dataset (291 observations) to create a dataset containing 1095 independent observations. Computation of lidar, terrain, and scan angle metrics for each point cloud associated each observation in the expanded dataset with the scan information in addition to the lidar and terrain information. A multilayer perceptron (MLP) was used to model basal area and total volume to compare the predictions resulting from standard and expanded ABA datasets. With expanded datasets containing lidar, terrain, and scan information, the R2 for the median predictions per plot were higher (R2 of 0.83 and 0.85 for BA and Vtot) than predictions with standard datasets (R2 of 0.66(BA) and 0.71(Vtot)) containing only lidar metrics. It also outperformed an MLP model for a dataset with lidar and terrain information [R2 of 0.77(BA and Vtot)]. The MLP performed better than Random forest regression, which could not sufficiently exploit additional terrain and scan information.

Old-growth forests provide a broad range of ecosystem services. However, due to poor knowledge of their spatiotemporal distribution, implementing conservation and restoration strategies is challenging. The goal of this study is to compare the predictive ability of socioecological factors and different sources of remotely sensed data that determine the spatiotemporal scales at which forest maturity attributes can be predicted. We evaluated various remotely sensed data that cover a broad range of spatial (from local to global) and temporal (from current to decades) extents, from Airborne Laser Scanning (ALS), aerial multispectral and stereo-imagery, Sentinel-1, Sentinel-2 and Landsat data. Using random forests, remotely sensed data were related to a forest maturity index available in 688 forest plots across four ranges of the French Alps. Each model also includes socioecological predictors related to topography, socioeconomy, pedology and climatology. We found that the different remotely sensed data provide information on the main forest structural characteristics as defined by ALS, except for Landsat, which has a too coarse resolution, and Sentinel-1, which responds differently to vegetation structure. The predictions were quite similar considering aerial remotely sensed data, on the one hand, and satellite remotely sensed data, on the other hand. Socioecological variables are the most important predictors compared to the remote sensing metrics. In conclusion, our results indicate that a wide range of remotely sensed data can be used to study old-growth forests beyond the use of ALS and despite different abilities to predict forest structure. Accounting for socioecological predictors is indispensable to avoid a significant loss of predictive accuracy. Remotely sensed data can allow for predictions to be made at different spatiotemporal resolutions and extents. This study paves the way to large-scale monitoring of forest maturity, as well as for retrospective analyses which will show to what extent predicted maturity change at different dates.

Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m2.ha-1 and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.

Abstract. Forests strongly modify the accumulation, metamorphism and melting of snow in mid and high-latitude regions. Recently, snow routines in hydrological and land surface models have been improved to incorporate more accurate representations of forest snow processes, but model inter-comparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–17 and 2017–18, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l, French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, instrumentation, and collection methods. The observations include distributed forest characteristics (tree inventory, LIDAR measurements of forest structure, sub-canopy hemispherical photographs), meteorology (automatic weather station and radiometers array), snow cover and depth (snow poles transect and laser scan), and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and provides continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l’Univers de Grenoble (OSUG) data center at http://dx.doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).

Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m2.ha-1 and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.

Forest ecosystems play a fundamental role in natural balances and climate mechanisms through their contribution to global carbon storage. Their sustainable management and conservation is crucial in the current context of global warming and biodiversity conservation. To tackle such challenges, earth observation data have been identified as a valuable source of information. While earth observation data constitute an unprecedented opportunity to monitor forest ecosystems, its effective exploitation still poses serious challenges since multimodal information needs to be combined to describe complex natural phenomena. To deal with this particular issue in the context of structure and biophysical variables estimation for forest characterization, we propose a new deep learning-based fusion strategy to combine together high density three-dimensional (3-D) point clouds acquired by airborne laser scanning with high-resolution optical imagery. In order to manage and fully exploit the available multimodal information, we implement a two-branch late fusion deep learning architecture taking advantage of the specificity of each modality. On the one hand, a 2-D CNN branch is devoted to the analysis of Sentinel-2 time series data, and on the other hand, a multilayer perceptron branch is dedicated to the processing of LiDAR-derived information. The performance of our framework is evaluated on two forest variables of interest: total volume and basal area at stand level. The obtained results underline that the availability of multimodal remote sensing data is not a direct synonym of performance improvements but, the way in which they are combined together is of paramount importance.

As studies have underlined the sensitivity of lidar metrics to scan angles, the objective of this study was twofold. Firstly, we further investigated the influence of lidar scan angle on the ABA predictions of stand attributes of riparian (29 field plots), broadleaf (42 field plots), coniferous (31 field plots) and mixed (45 field plots) forest types in France. Secondly, we evaluated the potential of voxelisation approaches to normalise scan angle effects in lidar metrics and mitigate scan angle effects in ABA models. To achieve these objectives, we first selected a model based on four lidar metrics with different sensitivities to lidar scan angle, i.e. mean and variance of canopy height values, gap-fraction, and coefficient of variation of plant area density (PAD) profile. For each plot, we considered the point cloud scanned from one flight line independently and characterised each resulting point cloud by the mean scan angle (MSA) and classified them into one of three classes: A (0 degrees <=MSA < 10 degrees), B (10 degrees<=MSA < 20 degrees) or C (20 degrees<=MSA < 30 degrees). An experimental setup involving nine scenarios was conceived to study the impact of the number of flight lines (sce-narios fl1, fl2 and fl3) and predominant scan angle (scenarios A, B or C) or combination of scan angle directions (scenariosA and B, orA and C, orB and C), on area-based approach (ABA) models. We built ABA models for the same forest plots for 5000 resampled datasets in each scenario to predict three forest attributes, i.e., stem and total volume (Vst and Vtot) and basal area (BA). Three goodness-of-fit criteria were computed for each model (coefficient of determination (R2), relative root mean square error (rRMSE) and mean percentage error (MPE). We compared the distributions of the goodness-of-fit criteria between scenarios to assess the behaviour of the predictive models when: 1) the number of flight lines (i.e., scan angles) increases (fl1, fl2 or fl3); 2) lidar datasets comprise specific scan angle (A, B or C) or combination of scan angles (AB, AC or BC); 3) voxelisation is used to compute Pfand CVPAD. The results show that models built with point clouds scanned from multiple flight lines were more robust, with a lower standard deviation of their goodness-of-fit criteria. On average, across all forest types, compared to fl1, the standard de-viations of R2 distributions were lower for fl2 and fl3 by 42 % and 77 %, respectively. We also observed that a dataset with a predominantly nadir configuration (i.e., scenario A) did not always result in better predictions (mean R2 higher by 0.08, 0.07, 0.04 for scenario B for broadleaf, coniferous and mixed, respectively). For a set of calibration plots, the resulting forest attribute models depend on the acquisition geometry over the plots, as observed in this study, which could result in unreliable wall-to-wall predictions. The risk is particularly high in acquisitions with low overlapping rates, with many areas covered by only one flight line. Using voxel-basedPfand CVPAD together with the mean and variance of heights helped to mitigate the impacts of changes in scan angles by a) increasing the means of the distributions, thereby improving the accuracy of predictions, or b) reducing the standard deviations, thereby increasing prediction precision, or c) both of the above.

Building a network of interconnected overmature forests is crucial to provide habitat and promote the dispersal of a multitude of species that depend on several forest structural maturity attributes such as very large living trees and deadwood. The use of Airborne Laser Scanning (ALS) to locate patches of overmature forests is still in progress. However, few studies have paid attention to the spatial distribution of overmature forests and its ecological consequences on landscape connectivity for species depending on forest structural overmaturity. In this study, we investigated the potential of ALS-derived maturity and connectivity indices to identify overmature forests that most contribute to connectivity and that could be integrated into a network of forest protected areas.

Forest ecosystems play a fundamental role in natural balances and climate mechanisms through their contribution to global carbon storage. They also represent important reservoirs of biodiversity. The sustainable management and conservation of forest resources is, therefore, crucial in the current context of global warming and biodiversity loss. To tackle such challenges, earth observation data has been identified as a valuable source of information capable to provide stakeholders with informative indicators to support the decision making process related to forest ecosystems management. In particular, LiDAR remote sensing has proven to be a powerful tool for the characterization of structural properties of forest ecosystems, which in turn are valuable information for their monitoring. To deal with this particular issue in the context of simultaneous forest variables estimation in a multi-output regression setting, we propose a deep learning based strategy to combine together information coming from metrics derived from high density 3D-point clouds acquired by airborne laser scanning (ALS) and topography descriptors extracted from the same source. The performance of our framework is evaluated on two stand-level forest variables of interest: Total Volume (referring to total wood volume) and Basal Area. It is compared to KNN and Random Forest (RF) algorithms, and also to a single-output version of our framework. As a result, the proposed multi-output framework performed better than KNN and RF and achieved a R-2 value of 0.72 and 0.73 for Total Volume and Basal Area, respectively. The obtained results are similar to those obtained when running two single-output deep learning frameworks and underline that the availability of additional topography descriptors to enrich the information provided by standard LiDAR-derived metrics brings interesting performance improvements in the estimation of both forest variables of interest.