Spaceborne Canopy Height Products Should Be Complemented With Airborne Laser Scanning Data: Toward a European Canopy Height Model

Abstract

Measuring and mapping vegetation structure is essential for understanding the functioning of terrestrial ecosystems and for informing environmental policies. Recent years have seen a growing demand for high‐resolution data on vegetation structure, driving their prediction at fine resolutions (1–30 m) at state, continental, and global spatial extents by combining satellite data with machine learning. As these initiatives expand, it is crucial to actively discuss the quality and usability of these products. Here, we briefly summarize current efforts to map vegetation structure and show that continental‐to‐global canopy height models (CHMs) exhibit significant errors in canopy heights compared to national airborne laser scanning (ALS) data. We recommend that regions with abundant ALS data, such as Europe, prioritize using ALS‐based canopy height metrics rather than relying on less accurate predictions from satellite products. Despite variations in ALS data characteristics, such as temporal inconsistencies and differences in acquisition characteristics and classification accuracy, the generation of spatially contiguous canopy height products in raster format at fine spatial resolution is necessary and feasible. This requires coordinating efforts for data and survey harmonization, developing standardized processing pipelines and continent‐wide ALS products, and ensuring free access for research and environmental policy. We show that ALS data now cover most of Europe, with newer surveys achieving higher point densities, improving their suitability for vegetation mapping. Beyond numerous applications in forestry, ecology, and conservation, such data sets are crucial for calibrating future Earth Observation missions, making them essential for producing reliable and accurate global, fine‐resolution vegetation structure data.

Introduction

Ecosystem structure—the spatial arrangement of biotic and abiotic elements that make up an ecosystem—is an Essential Biodiversity Variable (EBV) considered critical for monitoring the cover, distribution, and vertical profile of ecosystems (Pereira et al., 2013; Valbuena et al., 2020; Skidmore et al., 2021). Vegetation structure—the horizontal and vertical distribution of vegetation biomass—is one of the key components of ecosystem structure, especially in terrestrial ecosystems. It plays a crucial role in modulating multiple ecosystem processes. In particular, it regulates energy flow, water cycling, carbon sequestration, and primary productivity (Murphy et al., 2022; LaRue, Knott, et al., 2023; Li et al., 2024). Furthermore, vegetation structure creates unique habitats that support species coexistence across different vegetation layers (Davies & Asner, 2014; Kemppinen et al., 2024; Moudrá et al., 2025; Moudrý et al., 2021; Wildermuth et al., 2023). The prevailing theory is that structurally complex vegetation stands are most effective in optimizing the incoming light and water resources, leading to better carbon assimilation (Atkins et al., 2018; Seidel & Ammer, 2023), and that they provide a large number of ecological niches, thereby enhancing biodiversity (Coverdale & Davies, 2023; LaRue, Fahey, et al., 2023; Stein et al., 2014; Tews et al., 2004; Torresani et al., 2020). Consequently, data on vegetation structure is essential for a global biodiversity observing system (Gonzalez et al., 2023), supports the United Nations' System of Environmental-Economic Accounting (United Nations, 2021, 2022), contributes to the EU Forest Strategy for 2030 (European Commission, 2021), and plays a key role in tracking progress toward global biodiversity targets and Sustainable Development Goals (SDGs) (Skidmore et al., 2021).

Remote sensing technologies, such as light detection and ranging (LiDAR), have played a key role in addressing knowledge gaps, providing a way to accurately map vegetation structure across a variety of habitats and landscapes, including grasslands (Zlinszky et al., 2014), shrublands (Klouček et al., 2022), wetlands (Koma et al., 2021), forests (Toivonen et al., 2023), agricultural areas (Grondard et al., 2025), urban areas (Caynes et al., 2016), and Natura 2000 sites (Shi et al., 2025), from local to global scales (Herold et al., 2019; Janiec et al., 2025; Jutras-Perreault et al., 2023; Liu et al., 2023; Rosen et al., 2024; Stereńczak et al., 2018; Vaglio Laurin et al., 2025; Valbuena et al., 2020; White et al., 2025). Particularly, LiDAR sensors onboard airplanes (i.e., airborne laser scanning; ALS) have considerable potential to advance national monitoring programs (e.g., Assmann et al., 2022; Kissling et al., 2023). Moreover, robust approaches to convert ALS data into structural metrics are available (Fischer et al., 2019, 2024; Hawryło et al., 2024; Kissling et al., 2024). However, while ALS data provides high spatial resolution, its spatial extent and temporal availability are limited (e.g., Moudrý et al., 2023; Okyay et al., 2019). Access costs for end users of ALS have decreased in recent years, especially where national programmes or mapping agencies release data openly, but large-scale continuous coverage exists only in a few regions, mostly in Europe, North America, and Australia, with several other countries, such as New Zealand and Japan. A few countries (e.g., Denmark, Estonia, Netherlands, or Spain) have even mapped their entire territory more than once.

Recent advances in spaceborne LiDAR missions, such as the Global Ecosystem Dynamics Investigation (GEDI) and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), can help to address the limited spatial and temporal extent of ALS data (Dubayah et al., 2020; Markus et al., 2017). These missions provide free data that have enabled the creation of global models of vegetation structure (e.g., Burns et al., 2024; Mulverhill et al., 2022), supporting innovative and impactful research. For instance, vegetation structure products derived from spaceborne LiDAR data have been used to monitor forest and woodland structure and regrowth (Jucker et al., 2023; Milenković et al., 2022; Stritih et al., 2023), track carbon losses from disturbances (Holcomb et al., 2024), evaluate the effectiveness of protected areas from the perspective of carbon stocks and vegetation structure (Brodie et al., 2023; Ceccherini et al., 2023; Lang et al., 2023; Liang et al., 2023), and assess species diversity and species-environment relationships (Marselis et al., 2022; Smith et al., 2022; Vogeler et al., 2023; Xu et al., 2024). However, the spatial coverage of spaceborne LiDAR measurements is sparse and discrete, and their derived products, such as global canopy height models (CHMs), have a low spatial resolution (Burns et al., 2024) or suffer from accuracy issues (Mandl et al., 2023; Moudrý, Gábor, et al., 2024), which constrain their applicability (Hakkenberg et al., 2023).

Here, we aim to highlight the potential of ALS data to complement spaceborne LiDAR products. In particular, we (a) provide a brief overview of spaceborne LiDAR missions that measure vegetation structure, (b) examine the accuracy of CHMs predicted from these missions, (c) highlight recent developments in potential mapping of the Earth's surface integrating airborne and satellite data, and (d) propose a broader use of ALS data, emphasizing the need to develop continental (e.g., Europe-wide) CHMs. We identify the challenges involved and offer general recommendations for future progress.

Measuring Vegetation Structure With Spaceborne LiDAR

Details and examples of the usability and advantages of LiDAR remote sensing for mapping vegetation structure can be found in Lefsky et al. (2002), Bergen et al. (2009), and Moudrý et al. (2023). Simply put, LiDAR is ideal for measuring vegetation structure because it can penetrate through the gaps in the vegetation, capturing its vertical structure as well as the shape of the terrain underneath. LiDAR sensors can be installed on various platforms, including tripods, backpacks, cars, drones, helicopters, planes, and satellites. Notably, spaceborne LiDAR is valuable for large-scale mapping due to its consistent and extensive global coverage.

Satellite sensors are expected to become the primary data source for mapping vegetation structure in response to global monitoring requirements, with LiDAR playing an important role (Skidmore et al., 2021). The first global data set characterizing canopy structure was obtained from the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), operational from 2003 to 2009 (Abshire et al., 2005). That mission was primarily intended for measuring polar ice caps, but it also enabled the development of data sets for ground elevation and canopy height (Schutz et al., 2005; Simard et al., 2011). In 2018, NASA launched two spaceborne LiDAR missions, ICESat-2 (Magruder et al., 2021; Markus et al., 2017) and GEDI (Dubayah et al., 2020), aimed at providing near-global data on terrain and canopy height, among other objectives. Yet, technical challenges related to spatial and temporal coverage as well as data accuracy persist (Fernandez-Diaz et al., 2022; Hancock et al., 2012, 2021; Liu et al., 2021; Pang et al., 2022; Velikova et al., 2024).

Although current spaceborne LiDAR missions have substantially higher sampling densities than their predecessors, the derived products still have limited spatial and temporal coverage. A major limitation of spaceborne LiDAR sensors is that they collect discrete data samples along narrow orbital tracks (transects) rather than producing continuous, wall-to-wall coverage. This sampling design means that spaceborne LiDAR observations represent only a small fraction of the Earth's surface, leaving large gaps between ground tracks and individual footprints. As a result, these sensors cannot directly capture spatially contiguous patterns of vegetation structure. Notably, for some applications, such as in forestry, transect-based (profiling) LiDAR data are sufficient to characterize forest conditions over large regions in a timely and cost-effective manner, though their use has been increasingly overshadowed by scanning LiDAR instruments (see review by Wulder et al., 2012). In contrast, applications such as ecosystem (habitat) mapping and ecosystem extent accounting, assessing habitat fragmentation, or species-environment relationship analyses require spatially continuous data sets to capture fine-scale patterns across the landscape. For example, within agricultural landscapes, structural complexity is often provided by isolated trees and linear features such as hedgerows and tree belts, which serve as important habitats contributing to biodiversity and carbon storage (Grondard et al., 2025; Liu et al., 2023). Similarly, urban vegetation is recognized for its multiple ecological and societal benefits, such as mitigating the urban heat island effect, improving air quality, and sequestering carbon, making detailed monitoring of tree attributes essential (Dong, Tang, et al., 2025; Tang et al., 2024). However, spaceborne LiDAR data remain insufficient for producing high-resolution, continuous vegetation structure data for such purposes.

GEDI was expected to sample only 4% of the Earth's land surface over a 2-year mission, enabling the generation of near-global gridded vegetation structure metrics from aggregated GEDI waveforms, including canopy height and structural diversity at three spatial resolutions: 1, 6, and 12 km (Burns et al., 2024). Similarly, the ICESat-2 mission is designed to generate a global canopy height product at a 1-km resolution (Guenther et al., 2024). However, in Europe, the sampling intensity of ICESat-2 is even lower than that of GEDI due to its orbital configuration (Markus et al., 2017). Furthermore, GEDI and ICESat-2 missions are not designed to acquire laser pulses over the same location twice (i.e., they cannot provide direct observations of vegetation change (but see Clark et al., 2025; Holcomb et al., 2024; Liu et al., 2025)). In addition, various factors, including atmospheric conditions, solar background photons, laser pulse energy, and topography, affect data accuracy and require filtering, resulting in significant reductions in available data (e.g., Hayashi et al., 2013; Moudrý, Prošek, et al., 2024; Moudrý et al., 2022; Pardini et al., 2019). Indeed, the operational use of GEDI and ICESat-2 data remains limited, as canopy height accuracy falls short of the thresholds typically required for environmental and forestry applications (approx. 2 m; Bergen et al., 2009; Fassnacht et al., 2025), and sampling density and coverage are strongly shaped by the trade-offs between observation accuracy and required spatial resolution of derived products (Pracná et al., 2025). Moreover, GEDI observations are limited to latitudes between approximately −52° and 52°, excluding higher-latitude regions (Dubayah et al., 2020). This means that large parts of Europe are not covered by GEDI observations, which, in turn, restricts the usability of the data for continent-wide studies. A near global-scale 1 km resolution CHM is a significant achievement but has limited utility for applications such as ecosystem mapping or species-environment relationship assessments, which typically require finer spatial resolution (Anderle et al., 2023; Davison et al., 2023; Smith et al., 2022; Vogeler et al., 2023). Development of fine-resolution continuous data sets so far relies on interpolation or data fusion with other remote sensing products (e.g., optical or radar imagery), which is associated with known limitations of the resulting models (Section 3). Although future advances are expected to significantly enhance accuracy and resolution (Section 4), alternative solutions must also be explored to meet immediate monitoring needs (Section 5).

Spatially Contiguous, High-Resolution CHMs, and Their Limitations

The lack of global high-resolution data on vertical vegetation structure has stimulated the use of spaceborne LiDAR data in combination with other satellite products to make spatially contiguous predictions of vegetation structure (see review by Coops et al., 2021), such as canopy height, total canopy cover, above-ground biomass density, and foliage height diversity at fine resolutions, such as 10 m or 30 m (e.g., Diaz-Kloch & Murray, 2024; Kacic et al., 2021, 2023; Schwartz et al., 2023). A common approach to producing high-resolution, wall-to-wall data on vegetation structure lies in training predictive models that combine direct but discrete height measurements (e.g., from spaceborne LiDAR ICESat, GEDI, or ICESat-2) with spatially contiguous data (e.g., from spaceborne optical and radar data). The model establishes a relationship between the discrete and the continuous data that enables the estimation of canopy height at locations not directly measured by LiDAR (Bergen et al., 2009; Lefsky, 2010). Predicted CHMs are among the most common high-resolution vegetation structure products available at continental (Liu et al., 2023; Turubanova et al., 2023) and global scales (Lang et al., 2023; Pauls et al., 2024; Potapov et al., 2021; Weber et al., 2025), making them suitable for illustrating the pros and cons of such data. Recently, a web application for predicting canopy height, which combines GEDI with other remote sensing data, has been developed, making this approach easily accessible (Alvites et al., 2025).

Several continental or global predicted CHMs have been produced. The first such data set was developed by Lefsky (2010), who combined canopy heights derived from GLAS with MODIS data to produce a patch-based global CHM. Similarly, Simard et al. (2011) used the relationships between the GLAS-derived canopy heights and multiple environmental variables (e.g., tree cover, climate, altitude) to predict a global model of canopy heights at a 1 km spatial resolution. Recently, Potapov et al. (2021) and Lang et al. (2023) used optical satellite data (Landsat, Sentinel-2) trained on GEDI measurements to create global CHMs at 30 and 10 m spatial resolutions, respectively. Instead of deriving training canopy heights from satellite LiDARs, models can also be calibrated using ALS data, which, when combined with very high-resolution (VHR) satellite imagery, enables predictions at substantially finer spatial resolutions (e.g., Liu et al., 2023; Wagner et al., 2025). For example, Liu et al. (2023) used canopy height from ALS data and PlanetScope imagery to predict canopy heights in Europe at a resolution of 3 m. Likewise, Meta, in cooperation with the World Resources Institute, combined high-resolution data from optical satellites, ALS, and GEDI to develop a global CHM at a 1 m resolution (Tolan et al., 2024).

The main benefit of predicted CHMs based on spaceborne LiDAR lies in their easy availability, especially as there are no alternatives at scales beyond the regional level. These CHMs are usually readily available as open data in a raster format, allowing researchers to use them as input data in common GIS software to inform their analyses. This contrasts with the high data volume, time-consuming, and often challenging process of working with more accurate ALS point clouds, which can be difficult for many researchers to store and handle (Kissling et al., 2022; Kissling & Shi, 2023; Moudrý et al., 2023; Wang et al., 2024). However, the easy accessibility of such predicted CHMs is both a blessing and a curse as users may be unaware of data limitations, and the reliability of predicted global data sets is questionable (Duncanson et al., 2019; Meyer & Pebesma, 2022). Modeling canopy height is a complex process that involves errors and biases from multiple sources, ranging from ground detection with spaceborne LiDAR to the saturation of optical data in closed-canopy forests (e.g., Réjou-Méchain et al., 2019). Indeed, independent validation studies showed that the accuracy of these satellite-derived global CHMs is low (e.g., Bolton et al., 2013; Pascual et al., 2022), and their use in biodiversity modeling leads to erroneous results (Moudrý, Gábor, et al., 2024).

Validation of Predicted Global CHMs' Accuracies

To demonstrate the limitations of predicted CHMs, we followed a recent study by Moudrý, Gábor, et al. (2024), who compared three global predicted CHMs. In addition to their evaluation, we added a continental canopy height map for Europe produced by Liu et al. (2023) and a ten-year-old ALS scan, which allowed us to assess whether the accuracy of the predicted CHMs is higher than that of the outdated ALS data. We used a 2022 ALS scan from the Giant Mountains National Park (Czechia) as a reference and compared it to four recent satellite-derived predicted CHMs (Table 1) as well as to another ALS scan acquired 10 years earlier (i.e., 2012; all ALS data were processed with standard methods, cf. Moudrý, Gábor, et al., 2024).

Table 1 Predicted Global (Continental) Canopy Height Models (CHMs) Evaluated in This Study

Global CHM	Author	Resolution	RMSE	Valid for year
Global forest canopy height	Potapov et al. (2021)	30 m	9.1 m	2019
High-resolution canopy height model of the Earth	Lang et al. (2023)	10 m	2.8–9.6 m	2020
Canopy height map for Europe	Liu et al. (2023)	3 m	4.3–6.4 m	2019
Global map of tree canopy height	Meta and World Resource Institute; Tolan et al. (2024)	1 m	4.4 m	2018–2020

The study area was selected because it provides both a representative example of European landscape heterogeneity and access to high-quality ALS data collected at multiple time points. The Giant Mountains National Park can be regarded as broadly representative of many European landscapes, as it encompasses a mosaic of natural and human-modified ecosystems. These cover a diverse, rugged landscape shaped by glacial and postglacial processes and a broad altitudinal gradient (approximately 400–1,600 m a.s.l.). Canopy height of forest stands is typically up to 35 m, with some mature patches reaching up to 45 m. Vegetation transitions from lowland managed forests and extensive spruce (Picea abies) monocultures to montane and subalpine zones with thickets of dwarf pine (Pinus mugo), alpine meadows, and sparsely vegetated rocky areas. The park also includes peat bogs and remnants of native deciduous and mixed forests dominated by beech (Fagus sylvatica). This heterogeneous mosaic of open areas with low vegetation, interspersed with forested patches of varying canopy height (Figures 1 and 2) mirrors ecological and structural variability found across much of Central and Western Europe, with the exception of extensive flat coastal regions, such as in the Netherlands or northern Germany. Therefore, although regionally specific, the Giant Mountains landscape represents a realistic and data-rich test environment for evaluating CHMs.

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The evaluation demonstrated that current global and continental CHMs exhibit limited accuracy in our study area, with RMSEs ranging from 6.4 to 11.7 m (Figures 1 and 2). This is consistent with several recent independent validation studies. For example, Chen et al. (2025) and Ng et al. (2025) highlighted limitations and notable accuracy differences in global CHMs over China and New Zealand, respectively. Similarly, Dong, Xu, et al. (2025) identified consistent limitations across products, including widespread canopy misclassification, and systematic biases in canopy height predictions in urban environments. Importantly, all studies indicate that low canopies are typically overestimated and high canopies are underestimated, resulting in a limited ability of predicted CHMs to capture canopy height variability (Chen et al., 2025; Dong, Xu, et al., 2025; Moudrý, Gábor, et al., 2024; Ng et al., 2025). Together, these findings illustrate that despite recent progress, substantial uncertainty remains in predicted CHM products (Besic et al., 2025; Lencinas, 2025).

Strikingly, we found that the 2012 ALS data had a much lower error in predicting 2022 canopy height than any of the global or regional CHMs. Figure 1 presents a cross-section comparison of vegetation heights extracted from four predicted spaceborne data-based CHMs to reference heights extracted from ALS CHMs. Both large over- and underestimation of vegetation height can be observed in spaceborne CHMs (Figure 1; see Moudrý, Gábor, et al. (2024) for evaluation in other temperate forests). The continental canopy height map for Europe (Liu et al., 2023) exhibited a lower root mean square error than the three global products (Figure 2), consistent with the results of a recent comparison of the same CHMs conducted by Fogel et al. (2025). However, the change in vegetation height over 10 years is lower than the canopy height error in the four models for the selected area (Figure 2). In this example, such an error hinders effective change detection in canopy height. This limitation can be further influenced by the amount of disturbance, which is relatively low in our study area (Figure 2). In cases of large-scale deforestation detectable by optical data, the signal should still be visible in predicted CHMs. However, to estimate changes in vegetation height accurately, we must first know the vegetation height before such disturbances occur.

What to Report and Consider When Generating and Using CHMs

Global CHM data sets are indispensable for answering large-scale ecological questions, and with the increasing availability of suitable data, the number of such products will continue to grow (Lencinas, 2025). It is, therefore, imperative to improve the reporting of accuracy and uncertainty in predicted CHMs. In addition, new products should be routinely compared with existing ones to identify systematic discrepancies between products and to enable users to select the most appropriate data set for their purposes (e.g., Markonis et al., 2024). A detailed discussion of validation approaches is beyond the scope of this study. However, the Committee on Earth Observation Satellites (CEOS) protocol for biomass products provides a useful reference for best practices in estimating and reporting uncertainties, and similar principles can be applied to CHM products (Duncanson et al., 2021).

To select the best vegetation structure product, the overall evaluation metrics (e.g., Höhle & Höhle, 2009), such as mean error (ME) or root mean square error (RMSE), provided by existing products, are fundamental, despite providing a limited insight into the local map quality. Even if the user selects the most accurate map (i.e., that with the lowest overall RMSE), there may be considerable biases in the subregions. Rather than relying on a few individual metrics, products should provide a spatially explicit uncertainty layer; however, this is rarely implemented. The study by Lang et al. (2023) is a notable exception, explicitly evaluating both model and data uncertainty. Model uncertainty reflects uncertainty in the learned relationships and the model's ability to generalize beyond the conditions represented in the calibration data. Indeed, unevenly distributed calibration data can force the model to make predictions under conditions not represented during training, leading to invalid or unreliable predictor–response relationships. This issue is typical of CHMs developed using ALS data, as available samples typically do not capture the full range of ecosystems (e.g., Liu et al., 2023; Wagner et al., 2025). One way to improve the reporting of such uncertainties is to assess the area to which a prediction can be reliably applied. Methods such as the Area of Applicability (Meyer & Pebesma, 2021) inform users about regions where the predictor space resembles the training data, thereby reducing the risk of extrapolation-driven errors. In addition, uncertainty can arise from discrepancies in the data, such as between the true canopy height and the height measured by the satellite (or airborne) LiDAR sensors. These discrepancies are mainly caused by co-registration errors and by measurement inaccuracies of the sensor. These issues are particularly relevant for satellite LiDARs (e.g., Lahssini et al., 2024; Moudrý, Prošek, et al., 2024; Urbazaev et al., 2022). Another factor contributing to data uncertainty may be the discrepancy between the size of the LiDAR footprint and the spatial resolution of predictors (e.g., CHMs are often produced at a 10 m resolution, whereas the GEDI footprint is 25 m in diameter).

Importantly, uncertainty estimates themselves must be validated; recent findings show that the uncertainty layer of the global CHM by Lang et al. (2023) is inaccurate (see supplementary material in Moudrý, Gábor, et al., 2024). This can be easily added, for example, by comparing the estimated uncertainty with the error observed in the CHM relative to more accurate validation data (e.g., ALS). Furthermore, we suggest that in addition to validation with ALS data, the predicted CHM products should include representative profiles (as in Figure 1) in addition to standard 2D visualizations. Most authors of the global data sets only showed product visualizations in 2D space (e.g., Lang et al., 2023; Potapov et al., 2021; Schwartz et al., 2023), which can make even poorly performing models appear plausible (Figure 2). Vertical cross-sections reveal structural inconsistencies and over- or underestimation patterns that are otherwise hidden in 2D, thereby helping users better understand model limitations and associated uncertainty. Finally, ALS data are increasingly available worldwide (e.g., Fischer et al., 2025); users can validate global CHMs in representative regions, helping to select the most suitable product when multiple CHMs are available for a given region.

Continuous Mapping of the Earth's Surface by Integrating Airborne and Satellite Data

In contrast to costly and typically infrequent ALS campaigns (though see the next section), satellite data provide frequent, contiguous coverage, enabling dynamic estimation of vegetation structure. A potential solution to providing global fine-resolution data on the vertical structure of vegetation at a reasonable cost could lie in the creation of a fleet of LiDAR satellites that would continuously map the Earth (Hancock et al., 2021; Lowe et al., 2024). Such a constellation would enable consistent and repeatable monitoring of vegetation structure dynamics at a global scale. Hancock et al. (2021) estimated that producing such continuous data at a 30 m resolution every 5 years would require a constellation of 12 satellites acting concurrently. More recently, Lowe et al. (2024) investigated which platform-optics-constellation design offers the most promising and cost-effective solution, and suggested that micro-satellites, with a mass in the order of 150 kg, may present the most attractive performance-to-cost ratio. They estimated that a constellation of eight such satellites would be sufficient to produce CHMs at a 20-m resolution annually. The development of such a satellite constellation was announced relatively recently by the geospatial technology startup NUVIEW, which aims to deploy 20 commercial satellites equipped with LiDAR to map the Earth's entire land surface annually. In addition, NASA currently has an advanced proposal for the next-generation spaceborne laser altimetry mission, known as Earth Dynamics Geodetic Explorer (EDGE), which aims to significantly improve spatial accuracy and coverage, vertical precision, and change detection.

At present, however, such satellite systems do not exist, and an operational mission capable of delivering higher-resolution global LiDAR data is unlikely before 2030. Therefore, the assessment of vegetation structure (e.g., canopy height) and its changes at fine resolution will continue to rely on data fusion approaches in the foreseeable future (Valbuena et al., 2020). However, this remains a challenging task (see reviews by Coops et al., 2021; Balestra et al., 2024). To improve the accuracy and reliability of such fused products (see the previous section on the limitations of existing ones), continued development and refinement of modeling approaches are essential. This requires exploring and testing new algorithms, as recently demonstrated by Fogel et al. (2025), who evaluated the applicability of computer vision models for this purpose and suggested that transformer-based architectures exhibit superior performance. Moreover, the increasing availability of suitable auxiliary data sets from recent satellite missions, such as NASA-ISRO Synthetic Aperture Radar (NISAR) and the European Space Agency (ESA) Biomass mission (note, however, that Biomass mission data are not available over Europe and North America), is also expected to improve estimates of vegetation characteristics (e.g., Silva et al., 2021; Valbuena et al., 2020). In the relatively near future, these efforts may also benefit from planned missions, such as Landsat Next, which is expected to offer more spectral bands and improved spatial resolution compared to previous Landsat missions (Roy et al., 2026). Finally, continued refinement of GEDI and ICESat-2 data processing is also valuable, as they do not yet provide perfect ground truth. Some of the error in predicted CHMs likely stems from the limited accuracy with which both GEDI and ICESat-2 can measure terrain and canopy height (Lahssini et al., 2024; Moudrý, Prošek, et al., 2024; Pracná et al., 2025; Urbazaev et al., 2022). Enhancing the accuracy of data produced by these missions requires high-quality benchmark data sets, and ALS data represent an indispensable source for this purpose (e.g., Duncanson et al., 2019; Tang et al., 2023). Indeed, even if we manage to build a constellation of LiDAR satellites capable of dense spatio-temporal mapping of vegetation structure in the near future, it will need precise and consistent benchmark data sets over large geographical areas for its calibration and validation.

ALS data are also crucial for training and validating data fusion models (Balestra et al., 2024; Fogel et al., 2025), yet the lack of open-access high-resolution data sets hinders the reproducibility and evaluation of models (Fogel et al., 2025). When combined with very high-resolution satellite imagery, ALS data further enable canopy height prediction at spatial resolutions of up to 1 m (e.g., Tolan et al., 2024; Wagner et al., 2025), which can be particularly useful for extending coverage to areas where ALS data are currently unavailable (see the next section). Furthermore, the current status of vegetation structure derived from ALS data is essential for assessing changes, either in combination with direct observations from satellite LiDAR (Guerra-Hernández & Pascual, 2021; Parra & Simard, 2023) or through predicted CHMs (Fogel et al., 2025; Pauls et al., 2025). For all these reasons, available state- and country-wide ALS data represent the cornerstone for advancing global vegetation structure mapping—serving as a benchmark for future spaceborne missions, a driver of innovation in modeling and data fusion, and a source for immediate monitoring needs. They should, therefore, be used to produce uniform, seamless vegetation structure products.

Toward European Canopy Height Model Derived From Airborne Laser Scanning Data

Unlike spaceborne laser altimeters, which offer broader coverage but discrete and sparse measurements (Dubayah et al., 2020), ALS offers dense continuous coverage and is commonly used for regional or state-wide mapping. However, processing of ALS point clouds and their integration into a single product is challenging for large-scale analyses covering multiple countries (Fischer et al., 2024). As a result, large-scale studies are impeded by the absence of consistent, accurate, and accessible vegetation structure data, and generally rely on global predicted products from satellites (see Section 3) due to the difficulties in managing and processing ALS data at a continental scale.

The United States sets a good example with the 3D Elevation Program (3DEP), managed by the U.S. Geological Survey (USGS), which aims to collect ALS data for the conterminous United States, following specific collection requirements to ensure consistent LiDAR coverage across the entire territory (USGS, 2024). So far, however, it only aims at providing a digital terrain model (DTM; Stoker, 2020; Stoker & Miller, 2022), not seamless CHMs (or other vegetation structure products) in a raster format. Digital surface models (DSMs) could, however, be created through OpenTopography (), where most U.S. LiDAR data are also hosted. Europe is lagging behind, as no common data collection requirements or methodology that regulates mapping activities exists. This responsibility falls to the individual states and countries. As a result, ALS coverage in Europe is managed at the national (e.g., in Denmark, France, Netherlands, Poland, Spain) or sub-national (e.g., in Austria, Belgium, Germany, Italy) level and data are scattered among providers, leading to different characteristics across regions (D'Amico et al., 2021; Kakoulaki et al., 2021).

Of the 44 countries in Europe, ALS data are collected by governmental institutions in at least 33 countries (Figures 3 and 4; see Supporting Information S1 for more details on ALS campaigns in individual countries). However, national ALS acquisitions are typically designed to capture terrain information rather than vegetation structure, which means that the data are often collected using acquisition parameters that are suboptimal for characterizing vegetation structure (e.g., low point densities or acquisitions under the leaf-off period). Notable exceptions are France's LiDAR HD program, carried out by the Institut National de l’Information Géographique et Forestière (IGN, ), with dedicated summer acquisitions for forest resource assessments, and a recent effort by the Federal Agency for Cartography and Geodesy of Germany, which aims to map the entire Germany under leaf-on conditions and with point cloud density of 40 points per square meter between 2024 and 2025 as a part of the Digital Twin Germany initiative (GIM International, 2025; Hopfstock et al., 2022).

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At the moment, only a few European countries provide ALS-derived metrics of vegetation structure in a raster format (Denmark, Netherlands, and Switzerland; see Assmann et al., 2022; Kissling et al., 2023; Külling et al., 2024; Shi et al., 2025). These products typically include dozens of vegetation structure metrics describing, for example, vegetation cover and density (often separated into vertical strata), as well as various measures of vertical structural variability, such as skewness, kurtosis, or the coefficient of variation of vegetation height. Nevertheless, the choice of vegetation metrics, the methods used to calculate them, and their resolution can vary significantly among products (Cosgrove et al., 2024; Kissling & Shi, 2023; Moudrý et al., 2023; Wang et al., 2024). Therefore, it is important to coordinate these efforts from the outset to enable their harmonization and the development of a transnational, Europe-wide product. Such a harmonized product would ensure consistent interpretation and utilization of data across various studies and applications, and improve the reliability and reproducibility of results, enabling comparable assessments of vegetation characteristics across broad spatial extents.

Achieving this, however, will require addressing several challenges that currently limit the production of consistent and reliable products: (a) inconsistencies in acquisition characteristics and classification accuracy across data sets and countries, (b) temporal inconsistency (e.g., scans with differences in acquisitions in the order of several years or scans conducted in leaf-on vs. leaf-off periods) and differences in coordinate reference systems, and (c) availability and reliability of (meta)data. Below, we illustrate these challenges using the example of creating a near-continental CHM from existing ALS data, which we consider the first step toward this broader goal.

Inconsistencies in Acquisition Characteristics and Classification Accuracy

Combining LiDAR data from different campaigns presents challenges, as the three-dimensional structure of the point cloud, and consequently vegetation structural metrics, can vary with acquisition characteristics (Fischer et al., 2024; Goodwin et al., 2007; Roussel et al., 2017; Wulder et al., 2012; Zhang et al., 2024). These inconsistencies can, however, be mitigated by systematically evaluating how LiDAR-derived vegetation metrics respond to changes in factors such as spatial resolution, pulse density, and scan angle, and by selecting an appropriate resolution for the final product (e.g., Kissling et al., 2024; Shi et al., 2025; van Lier et al., 2022).

The typical point densities of LiDAR point clouds available in Europe are around 5–10 points per square meter (see Figure 3 in Kissling et al., 2024); still, they vary considerably across the continent (Figures 3 and 4). For low point densities, it is advisable to calculate vegetation metrics at coarser resolutions (e.g., 10 or 20 m) to minimize potential errors in estimating the vegetation structure (D'Amico et al., 2021; Kamoske et al., 2019; Kissling et al., 2024; Ruiz et al., 2014; Wilkes et al., 2015). On the other hand, vegetation metrics, such as upper percentiles of height, are generally less sensitive to point cloud properties (Fischer et al., 2024; Kissling et al., 2024; LaRue et al., 2022; Roussel et al., 2017), and deriving a CHM at a 10-m resolution should provide a reasonable balance between spatial resolution and vertical accuracy. The point cloud classification across countries, with differences in methods such as automated classification, visual inspection, and AI algorithms, constitutes another factor. While classes such as ground, low/medium/high vegetation, building, water, and unclassified are most commonly available in open-access ALS data across Europe, power lines, bridges, and viaducts are less often included (see Figure 3d in Kissling et al., 2024), potentially introducing bias in vegetation metrics (Shi & Kissling, 2023). However, this may not be a major issue if the focus is primarily on forests or nature reserves, where even less accurate classifications can still provide better results than predicted CHMs.

The scan angle of the laser pulses is another factor that has to be considered. The use of wider scan angles expands the swath width, allowing larger areas to be surveyed and potentially helping to optimize costs. However, as scan angles increase, the laser pulse travels a longer path through the canopy, increasing the likelihood of striking upper-canopy and reducing the probability of reaching the ground (e.g., Disney et al., 2010; Goodwin et al., 2007; Montaghi, 2013). With wider scan angles, the canopy effectively appears denser under otherwise identical acquisition conditions, which can, in turn, introduce bias into vegetation-structure metrics (Roussel et al., 2018). Although small deviations from nadir (up to approximately 15°–20°) generally have little effect on maximum canopy height or upper percentiles metrics, larger off-nadir angles can introduce biases, particularly for metrics such as lower height percentiles or gap fraction (Dayal et al., 2020; Liu et al., 2018; Montaghi, 2013; van Lier et al., 2022). Moreover, the effect of scan angle is influenced by the vegetation structure itself (e.g., by species composition and crown shapes; Liu et al., 2018; van Lier et al., 2022). In general, the impact of scan angle is modest and largely mitigated by overlapping flight lines and increased point density (Dayal et al., 2022; Lovell et al., 2005; van Lier et al., 2022). For instance, national ALS in Sweden requires the use of maximum scan angles of ±20°, whereas Slovenia allows angles up to ±30°. This highlights the need to account for scan angle to minimize systematic biases (Roussel et al., 2018), particularly when combining data sets from different ALS campaigns, such as country-wide surveys in Europe, or when using multi-temporal ALS data (Liu et al., 2018; Montaghi, 2013; Riofrío et al., 2022).

Additional acquisition characteristics, while beyond the scope of this study, may also influence the structure of ALS point clouds and derived vegetation metrics. For example, the wavelength of the laser pulse can affect canopy penetration and backscatter intensity (Lefsky et al., 2002). Similarly, the total power of the transmitted pulse (i.e., pulse width) and footprint size can further modulate how the laser interacts with vegetation, potentially altering the representation of vegetation structure in the point cloud (e.g., Hovi & Korpela, 2014). While these parameters may introduce differences, their effects are typically secondary compared to above mentioned scan angle and point density. Likewise, differences between sensor technologies, such as linear-mode versus single-photon LiDAR (White et al., 2021), can introduce additional variability. However, to our knowledge, single-photon LiDAR has so far seen very limited use in large-scale ALS acquisitions across Europe, with the likely exception of ongoing surveys in Germany between 2024 and 2025 for the Digital Twin initiative.

Temporal Inconsistency and Differences in Coordinate Reference Systems

The temporal inconsistency of ALS data acquisition across countries is a concern, as ALS surveys remain costly and infrequent. Moreover, ALS data collection often predominantly aims to provide accurate topographic modeling, so many countries carry out scans under leaf-off conditions (such as Slovenia, England, Czechia, and the Netherlands). In some countries, however, scanning is explicitly timed to occur close to the peak vegetation greenness (e.g., Estonia), while in other countries it depends on the region (e.g., Spain). Some countries may even merge point clouds across different scanning periods (France). Therefore, the density of vegetation returns may vary considerably depending on when the data were collected. If unaccounted for, the resulting differences could introduce substantial bias and limit the usability and accuracy of harmonized vegetation structure maps. In addition, as new advancements in scanning technology emerge (e.g., higher pulse repetition frequency, wider scan angles, use of multiple wavelengths, and different sensor types), older data sets can become less compatible with current data (i.e., due to considerable differences in acquisition characteristics), making it challenging to ensure compatibility. If we consider European countries where data have already been collected or are in the process of being collected, the time span between the first and last scans amounts to about 16 years (2010–2025; Figure 3). While this is not optimal, a 10-year difference can introduce (as illustrated above) less error than the predicted maps (Figure 2), if the area under study did not experience major disturbance events. It also indicates that with a coordinated effort, it should be possible to achieve a better temporal range similar to the US 3DEP (9 years) for the entire continent of Europe within this decade. In addition, higher observation frequencies in hotspots (e.g., areas experiencing considerable disturbances) could enhance the applicability of ALS data for timely management decisions (e.g., Fassnacht et al., 2024; Kissling et al., 2024).

The standardization of coordinate reference systems (both horizontal and vertical) is another important aspect in creating a near-continental CHM across Europe. Europe is historically fragmented in terms of coordinate reference systems, with individual countries having developed their own horizontal and vertical datums, which adds complexity to creating a unified CHM and can introduce misalignments and inconsistencies when merging point clouds. Ensuring that all data sets are converted to a common horizontal and vertical reference system is, therefore, a prerequisite for producing seamless and accurate CHMs at a continental scale. Ideally, all ALS data across Europe should be available in the ETRS89 reference system with ellipsoidal heights referenced to the GRS80 ellipsoid (i.e., the system in which all ALS data across Europe are originally acquired, since the positions of the aircraft collecting the data are determined using differential Global Navigation Satellite Systems methods relying on reference station networks realized in this system). Nevertheless, for distribution to end users, ALS point clouds are typically converted into local horizontal projected coordinate systems and either orthometric heights (e.g., in Spain) or normal heights (e.g., in Czechia), and access to the original ETRS89 data is, in most cases, complicated.

Standardizing vertical reference systems to a single unified system requires access to all local geoid or quasi-geoid heights (i.e., undulations) to enable conversion of the data to ellipsoidal heights. However, this can be challenging, as geoid or quasi-geoid data are not freely available in many countries. Fortunately, in the specific case of vegetation metrics such as canopy height, which express relative height above the ground rather than absolute height above sea level, the issue of unifying vertical reference systems can effectively be ignored. Although not ideal, a similar solution is also often used when combining multitemporal ALS acquisitions (Riofrío et al., 2022). This, of course, does not apply when creating a continental DTM, and although focusing here solely on vegetation structure allows us to largely circumvent this issue, we acknowledge that local geoid (and quasigeoid) information will eventually be necessary for fully unified elevation products. In the first step, point clouds should be transformed into the same horizontal coordinate reference system (e.g., ETRS89) and height-normalized. Subsequently, the CHM can be obtained using any existing method (Fisher et al., 2024). Alternatively, these steps can be reversed, and the transformation applied to raster data after the CHM (or other vegetation metric) generation. This approach may be necessary in the case of countries where point cloud data are not freely available, but raster-based DSM and DTM products are accessible.

(Meta)Data Availability and Reliability

Although the data have different characteristics as mentioned above, it is possible to derive vegetation structure metrics in raster format at a relatively fine resolution (Assmann et al., 2022; Kissling et al., 2023; Shi et al., 2025). This requires detailed documentation of the metadata in order to develop standardized processing pipelines that can account for differences in scanning properties (Fischer et al., 2024). However, metadata, if available, are documented with various degrees of depth and reliability, which significantly limits their accessibility and utility for potential users. For example, we made every effort to review the characteristics of ALS data available in Europe (see Supporting Information S1), but we had to limit our focus to point density and the year of data acquisition. This was due to the difficulty of narrowing down the acquisition time to the exact month, the lack of announcements regarding future acquisitions, and the absence of information on the classification categories and methods used to classify them. Therefore, in line with the FAIR guiding principles (Wilkinson et al., 2016), it is important that ALS surveys provide standardized, machine-readable metadata of survey variables and sensor characteristics, as well as documentation of preprocessing steps and provenance of (sub)national ALS point cloud data sets (Kissling et al., 2024). For ecological applications, it is also important to provide the flight line timestamps in a spatially explicit way because the actual date/month during which an area is scanned can vary within a national ALS data set (Shi et al., 2025).

In addition, in many cases, accessing the data itself remains challenging. Point clouds are still not freely available in several European states (e.g., Austria, Bosnia and Hercegovina, Romania, Malta), may not be easily accessible through web interfaces and require formal requests for release (e.g., Croatia), or the portals may be difficult to log into (e.g., Montenegro). Furthermore, while ALS data have been collected over multiple time periods in some parts of Europe (Figure 3), and countries such as Estonia, the Netherlands, and Spain openly provide all existing data, in other areas, only the data from the most recent period are easily accessible (e.g., Saxony, Switzerland). Hence, establishment of a centralized repository, and creation of a metadata catalog with human- and machine-readable metadata would be a major step forward (Kissling et al., 2024). Access to funding will be a crucial factor in this effort. A European funding initiative similar to 3DEP, supported by the EU, would be a good approach to generate vegetation structure metrics from existing data, to establish a centralized repository, and to collect data in European countries where ALS data is not yet available or where only limited coverage exists, such as the Balkans, Hungary, and Moldova.

Conclusions and Outlook

The availability of remote sensing data greatly facilitates forestry and ecological research. On the other hand, the growing number of data sets of varying quality introduces challenges regarding which data sets to choose. Users typically do not have the chance (and/or expertise) to critically evaluate the available data. It is, therefore, essential to ensure that data producers clearly communicate the limitations of their data sets. Predicted CHM products should provide reliable uncertainty estimates and visualizations of vegetation profiles (Figure 1) for representative areas and ecosystems.

For vegetation structure, accurate, consistent, and repeatable continental products derived from ALS data are key and should be prioritized over predicted spaceborne products. We strongly recommend that ALS-rich regions prioritize the production of ALS-based canopy height maps over relying solely on modeled global data. In Europe, the first step lies in creating a near-continental CHM using existing data (Figure 4), which, even given the differences in the data collected, is possible at a reasonably fine spatial (e.g., 10–20 m) and temporal (e.g., 15 years) resolution. While canopy height represents a key structural attribute, it should be viewed as only the first step toward a broader objective. The next step lies in expanding this coordinated effort to encompass additional ALS-based metrics (e.g., skewness, kurtosis, and the coefficient of variation of vegetation height, as well as vegetation density and cover; Moudrý et al., 2023; Kissling & Shi, 2023), thus enabling a more comprehensive assessment of ecosystem structure.

To ensure the effective use of ALS across Europe in the future, better transnational coordination is needed. It is necessary to establish a common data collection protocol to harmonize mapping activities (e.g., time of acquisition, pulse density, update period), a centralized repository for data sharing, as well as a metadata catalog. A European-wide coordination of data collection would lead to improved forest management, ecosystem monitoring, and climate change modeling on a continental scale. Beyond forestry and ecology, such data would also provide a valuable foundation for numerous other disciplines—including geomorphology, hydrology, and urban studies—where detailed and consistent 3D information on terrain, vegetation, and built structures is essential. Finally, they would provide a benchmark for calibrating spaceborne laser altimetry products.

Acknowledgments

Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This work was funded by the Horizon Europe project EarthBridge (grant agreement no. 101079310). ES was supported by the Internal Grant Agency of the Faculty of Environmental Sciences, Czech University of Life Sciences Prague (2023B0046). TJ and FJF were supported through a Research Project Grant from the Leverhulme Trust (RPG-2020-341). FJF acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant Agreement 101001905, FORWARD). PK was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation—459717468). ŽK was financially supported by the Slovenian Research and Innovation Agency core funding Earth observation and geoinformatics (P2-0406). RR, AC and SR were supported by the DFG under Germany's Excellence Strategy—EXC 2070–390732324. Researcher JAMV conducts his research under the Marie Skłodowska-Curie Actions—COFUND project, which is co-funded by the European Union (MERIT—Grant Agreement No. 101081195). MG was financially supported within the Grant APVV-22-0024 of the Slovak Research and Development Agency. GVL acknowledges the PNRR, Missione 4, Componente 2, Avviso 3264/2021, IR0000032—ITINERIS—Italian Integrated Environmental Research Infrastructures System CUP: B53C22002150006. WDK acknowledges the MAMBO project funded by the European Commission (grant agreement number 101060639). We are grateful to Ada Perello for providing information on laser scanning campaigns in several European countries.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Data Availability Statement

The evaluated global canopy height models, that is, the global forest canopy height (Potapov et al., 2021), the high-resolution canopy height model of the Earth (Lang et al., 2023), and the global map of tree canopy height (Tolan et al., 2024) are provided under Creative Commons Attribution 4.0 International License. The canopy height model of Europe at 3 m resolution was kindly provided by Liu et al. (2023). The canopy height models of Giant Mountains National park (2012 and 2022), derived from airborne laser scanning and used for evaluation of global canopy height models, are available from Zenodo: (Moudrý, 2024).

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