The need for species and genus information is enhanced by the fact that many reed-dominated wetlands are in decline in Europe [7], which is probably mainly due to oxygen depletion of the root zone [8–10]. These areas show signs of stress at the level of the whole ecosystem, including stunted growth, restricted reproduction, encroachment of terrestrial species and the formation of clumps on the boundary to open water [11,12]. During this process, the patches previously occupied by reed can be colonized by other species. Monitoring vegetation health and species composition is necessary to understand reed decline and initiate restoration [13,14]. While wetland vegetation health can be understood in a change detection context (involving mapping by remote sensing time series), quantitative surveying of the distinctive symptoms of reed stress and die-back [15] allows reed health to be mapped by creating only a snapshot in time. This may provide a less deep understanding of the process but delivers up-to-date information for decision support. Beyond the scope of a single species, a healthy wetland ecosystem is usually a mosaic of patches of different species, where the presence of the natural zonation is a criterion of a functioning wetland habitat [16]. Wherever the mapping of different wetland plant genera is possible, the presence of zonation and mosaic structure can also be assessed and used as a proxy of wetland vegetation health [17,18].

The objective of this study was to develop a methodology for mapping wetland vegetation composition at genus level, and the recognition of the presence as well as health or stress of the dominant species, based only on ALS-derived features calibrated to ground truth data. This includes testing the accuracy of the proposed method on a survey of a major wetland system against independently collected reference data and comparing it to other surveying methods applied to similar targets. Mapping vegetation height and structural parameters is often the focus of ALS-based vegetation surveys, but was not an objective of the current study.

Vegetation monitoring typically relies on collecting field data on the presence, abundance and health of plant species [19]. However, in wetlands, difficult access limits the collection of classical botanical data, and airborne [20–24] or satellite imaging [25–27] is widely used for monitoring [28]. In addition to classical aerial photography, the potential of multispectral and hyperspectral methods for vegetation classification and mapping has also proved successful in many surveys [13,26,27,29–32]. Nevertheless, passive optical imaging of wetland vegetation has its limitations: the pixels of high spectral resolution images are typically larger that the ALS footprint sizes, and this causes aggregation of the spectral information encountered horizontally, which is difficult to resolve during classification [26]. The potential of spectrally based classification to identify different types of vegetation is always controlled by the de facto differences in their reflectance spectra, which can be limited in some cases [32].

Airborne Laser Scanning (ALS) samples the Earth’s surface by measuring the signal travel time of laser pulses between the terrain and the airborne platform. The travel time is directly proportional to the distance, which can therefore be computed [33]. A dense set of points in a three-dimensional coordinate system is created from these distances and the position and orientation of the sensor platform. This latter is typically constantly tracked by a synchronized global navigational satellite system (GNSS) and an inertial measurement unit (IMU). ALS is traditionally used for mapping terrain topography [33], exploiting its ability to penetrate vegetation but removing the echoes corresponding to the canopy as they are not informative for terrain modeling [34–36]. Since the ALS points corresponding to the echoes from the canopy provide a strong representation of the vertical and horizontal structure of the vegetation due to the high sampling density and accuracy, ALS holds potential for vegetation mapping. ALS mapping of vegetation structure is operational in forests [37–39], rapidly developing in shrublands [40–44] but applications to riparian vegetation types remain rare [45,46]

ALS has sometimes been used as a standalone tool to assess the hydrological roughness of floodplain vegetation for hydrological modelling but this has usually not been extended to a level of detail suitable for species or wetland condition monitoring [47,48]. Object-based analysis of derivatives of the point cloud has been successfully used to outline riparian vegetation and streambed extent [49]. Multiple wavelength or multi-temporal surveys have proved to contain sufficient information for vegetation classification beyond the level of growth forms: Spartina stands and the sediment accumulation they facilitate in a saltmarsh environment were successfully outlined using two consecutive ALS surveys [50]. Dual wavelength ALS has been applied by Collin [51] for mapping saltmarsh vegetation (17 categories) and very high accuracy (92%) was reached by multivariate classification of rasterized spectral and spatial ALS products as pseudo-bands.

Due to its potential to sample ground elevation even below a canopy [35], the first applications of ALS in wetland ecology were for creating a very detailed digital terrain model (DTM), which was then applied as a background variable map to explain the spatial patterns of different vegetation types classified from airborne true color or infrared orthophotos. Knight [52] used an ALS-derived DTM to calculate tidal inundation patterns in mangrove wetlands and identify potential mosquito habitats. Jenkins [53] also used ALS data for outlining upland swamps and identified vegetation categories within these boundaries on the basis of multispectral satellite data. Morris [54] combined ALS data with multispectral images to map intertidal habitats and evaluate the link between different vegetation categories and elevations above tide level. Gilvear [55] used an ALS derived digital surface model (DSM) as a background dataset to support visual interpretation of hyperspectral data and found that the introduction of vertical structural information in addition to spectral properties increased the accuracy of visual interpretation.

While in the previously listed studies, ALS was used as a background dataset or an introductory step of vegetation classification to delineate areas in focus, rasterized ALS data can also be fused with imaging spectrometer images on a pixel basis. This creates a raster dataset with several channels, some actual spectral bands from the imaging sensor, while others derived from ALS but accessible within the same dataset (pseudo-bands) [56]. Several such studies demonstrate a significant increase in wetland classification accuracy compared to using only spectral or ALS data [57,58]. In riparian areas of Australian savannahs, the fusion of QuickBird images with several ALS-derived data layers allowed the identification of riparian vegetation by object-based classification [59]. Hence, according to recent research papers, the combination of ALS and optical imagery gives the best results in individual species detection.

Single-band laser scanning data (e.g., 1,064 nm) alone are apparently considered to contain insufficient information to be reliably used for classifying wetland vegetation to health categories and genera. This is because the traditional approach to vegetation mapping from remotely sensed data involves classification on the basis of spectral differences [56]. However, several methodological studies show that the information content of ALS data can be enhanced after collection by various processes including intensity correction [60], radiometric calibration [61,62], and dropout modeling [63].

Most commercially available ALS systems record information on the amount of backscattered energy, i.e., the amplitude (often referred to as intensity) and, in case of full-waveform recording instruments, also the echo width. This parameter seems to hold significant information for species determination [64,65]. The amplitude of the returning laser pulse depends on the reflectance of the sampled surface, but is modulated by many other factors including (but not limited to) the energy of the laser pulses, the atmospheric transmittance, the angle between the beam and the local surface normal (i.e., the angle of incidence) and the distance between the ALS system and the target. Maps of echo amplitude can be used for visual interpretation of terrain features [65], but the quantitative application of echo amplitude is only possible if it can be calibrated to actually represent the optical properties of the surface [61]. Several solutions to this problem have been proposed, ranging from applying a smoothing filter [66] to modeling signal path and local surface normals [62] and combining this with surface reflectivity measured with an active instrument [61]. If the radiometric calibration of the data points is reliable enough, single-channel classification methods can be applied to the dataset.

The presence of open water is important for wetland vegetation mapping but it is difficult to map through single-band ALS. A calm water surface is an almost perfect specular reflector and thus only reflects a high amount of radiation back into the sensor when observed at approximately nadir (i.e., the laser beam and the ray of the reflected echo coincide) [63]. At other observation geometries the amount of radiation reflected towards the sensor is often too low to be detected by the receiving unit and this is enhanced by the fact that the reflectance of water in the near infrared wavelengths often used for ALS is generally low, so echoes from a water surface are often not recorded at all. Brzank et al. [67] propose a point-based fuzzy classification procedure that calculates the membership weights of the class “water” for each ALS point on the basis of the recorded amplitude, elevation and point density. Höfle et al. [63] demonstrate a method based on the combination of radiometrically corrected intensities and relative positions of ALS points that allows high accuracy identification and outlining of open water areas. This latter solution involves the reconstruction of the missing ALS points (dropouts) that were not recorded due to specular reflection on the basis of the GPS time tag of the points, the scanning rate and the scan pattern. The point cloud is segmented on the basis of the local surface roughness, the density of points with intensity values below a threshold, and intensity variation.

Lake Balaton is a large (594 km²) shallow (mean depth 3.3 m) lake in western Hungary, with more than half of the shoreline sustaining reed-dominated wetlands [68] (Figure 1). These are protected under Natura 2000 and the Ramsar Convention. Kis-Balaton is a reconstructed wetland of 70 km² slightly upstream of Lake Balaton, with an average water depth of 1.2 m. Changes in the spatial extent of these wetlands were identified on a long-term archive aerial image study [10,69] and the loss of wetland vegetation area since the 1970’s has been documented. However, hardly any spatially explicit data exist on the extent of different vegetation types within the wetlands or on the extent of reed stress that forecasts future losses of area. The last field-based botanical survey including non-reed wetland vegetation was carried out in 1987 on Lake Balaton [70] and 1982 on Kis-Balaton [71]; after this, wetland vegetation monitoring has focused on the suitability of reed for industrial use [68].

The categories of the classification were vegetation types selected before the flight based on field experience and knowledge from archive aerial photo interpretation [10,69]. The aim was to produce an ecologically relevant map with categories that can be recognized in the field, as well as potentially including all possible land cover types that were present in the littoral zone:

Wetland: For the purpose of this study, wetlands are defined in a vegetation ecology sense contrary to other definitions that might be used for geomorphology, hydrology, pedology, etc. Wetlands are areas where the water or groundwater surface is regularly near or above the sediment surface or the soil is regularly fully saturated with water and where the vegetation is mainly composed of emergent macrophytes [5].

Trees/shrubs (abbreviated as trees in the following): Although trees and shrubs can be present in wetlands, separating them into different classes is beyond the scope of this paper, so this category is simply defined as areas where the dominant plants have a branching woody stem. Typically, these are Populus, Salix or Alnus trees, but other species are also present. These plants are expected to be higher than wetland vegetation, and are usually found on slightly elevated patches of dry land on the shore, within or near wetlands. Trees typically produce multiple echoes of the ALS pulse, so both the top of the canopy and the terrain surface can be identified on a vertical profile of the point cloud (Figure 2).

Scripus/Schoenoplectus/Typha angustifolia (abbreviated in the following as Scirpus): Although these are also emergent shore macrophytes, their stand structure differs from wetland macrophytes, because they grow at a much lower stem density and although the tips of the leaves emerge from the water, most of their length is submerged. Such areas are typically found on the most exposed edges of wetlands because of their ability to tolerate wave energy.

Water/artificial: This class contains water surfaces and man-made structures. For the purpose of mapping wetland vegetation, water surfaces do not have to be separated from artificially cultivated or covered areas (grazed or mown grasslands, agricultural areas, asphalt and concrete surfaces), bare soil or otherwise unvegetated structures. Water typically has a very low reflectance in the near infrared spectral band where the instrument operates [63,72] similar to tarmac surfaces and railway embankments, while very high reflectance is produced by the dense closed canopies of cultivated fields, the flat surface of mowed grasslands, and gravel or concrete surfaces (Figure 2). Depending on the roughness of the water surface, the observed reflectances are very low (or the points can be completely missing), or extremely high wherever the local water surface is perpendicular to the incoming pulse (on wave slopes and/or at sensor nadir).

The wetland class as defined above is further divided into vegetation classes containing reed stress categories and genera of other species. Classification to species level was not attempted because identification of wetland macrophyte species is usually based on properties of flower structure and leaf veins, and is thus difficult and often uncertain even when done in the field. Different genera mostly represent different forms of growth and are thus relevant categories for habitat identification.

Typha: Typha plants are characterized by their narrow and long leaves, which all grow from the base of the stem, and can reach up to 250 cm of height. The leaves are relatively thick and rigid, so they are usually near-vertical for most of their length. This means that the penetration of the ALS pulse is high in these areas, often reaching the ground or water surface, where most of the pulse energy is lost to low reflectance of water and bare soil or specular reflection from water. This and the dark green color of the leaves means that Typha areas are usually characterized by low ALS reflectance (Figure 2). Typha is very tolerant to anoxic sediment but is sensitive to wave action; therefore it is mostly found in the central areas of wetlands, in water depths between 50 and 100 cm, surrounded on all sides by Carex or reed. It can also form monodominant stands on the open water boundary in sheltered areas or mixed stands with other species.

Carex: Carex plants also have leaves that all sprout from the bottom of the stem but these are less rigid and usually have a curved shape as they bend towards the ground. The canopy height can also reach 200 cm, but this is rare: 50–130 cm is typical. These leaves interlock to form a dense closed canopy which restricts ALS signal penetration and reflects most of the pulse energy (Figure 2). Carex stands are characteristic on the shore side of wetlands, in periodically dry areas with water shallower than 50 cm year round.

Reed: Phragmites australis is the dominant wetland macrophyte of the study area, and is the only Phragmites species known in the area [68,73]. The canopy of Phragmites consists of leaves growing in regular intervals along the stem, which can reach a height of up to 4 meters above the water level [74]. This means that signal penetration is initially high but the signal rarely reaches the water surface as it gets reflected from the subsequent layers of leaves (Figure 2). The echo amplitude is usually high as the canopy is dense but in some cases (especially on the SW shore of the lake) canopy density can be low enough to allow some penetration and thus loss of energy to specular reflection from water. Reed can grow in a wide range of habitats from dry roadside ditches to several meters of open water but it is typically found on organic sediment accumulating on sheltered stretches of shore. Reeds standing in deep water have high conservation value, because they provide an essential habitat for spawning fish and nesting birds [75].

Healthy reed: A reed stand was regarded during ground truthing and validation as healthy if the stalks were high (above the approximate height of 1.5 m), had an even density with no open water between them, and if the majority of the stalks was vertical.

Die-back reed: Reed areas were categorized as being in a state of die-back if the density and height was very low or if clumps were present [7], separated by open water areas.

Stressed reed: Stressed reed was defined by alternating areas of low and high stalk density [15], with the canopies closing over any open water patches.

Ruderal reed: Ruderal reed areas are those where abundant nutrients and light allow the encroachment of terrestrial species, mainly weeds (e.g., Urtica dioica) or climbing plants (Humulus lupulus, Solanum dulcamara). These are typically found on the shore side of wetlands, around paths and artificial openings and in areas where waves deposit organic debris.

To check against an independent standard in addition to our own ground truth points, we used a set of georeferenced ground photographs collected during the summer of 2010 by the National Water Authority for a different purpose (field-based wetland vegetation categorization). In the first step of quality control, 17 test strips were selected that overlapped with the original ground control areas and were relatively well distributed over the wetland areas. In order to have a similar number of ground truths used for each category but keep the spatial coverage of the reference dataset, the number of images belonging to each class was assessed and a final set of 775 reference images selected. This was done by keeping every 7th image for die-back reed, every 3rd for healthy reed, every 2nd for stressed reed and water/artificial, and by keeping all of the reference images found for the rest of the categories.

This resulted in approximately 100 reference images for major categories and about 50 for the less frequent classes. Ground reference photographs collected within the area of these 17 strips were assigned one or several of the dominant vegetation categories (see Section 3.2) according to visual interpretation of the images. In the rare case that the vegetation was not clearly recognizable on the photo, that particular image was discarded. The ground points where the photos were taken were overlaid on a GIS, the images visualized and their alignment and range estimated from visible shore objects. After this, the area estimated to be covered by the image was inspected on the ALS-based classified vegetation map (Figure 7). For close range photos, this could mean a patch of a few square meters, for longer range images, this would mean 10–20 m of shore. The vegetation categories assigned to the photographs were compared with the vegetation categories shown by the classified map for the same spot, and thus a confusion matrix [82] was created (Table 2).

Quality control of the point cloud revealed that in most cases, echoes from water were lost due to specular reflection and that the canopies were not fully penetrated by the pulses. Multiple echoes were only recorded in trees, as the wetland vegetation canopy height rarely exceeded the minimum vertical distance needed for separation of subsequent echoes of a single pulse. Visual quality control of the resulting maps (Figure 7) revealed that the wetland recognition does not miss any known wetland areas but also classifies agricultural fields as wetlands in some cases. Since the main objective of the process was not to locate wetlands in the landscape, these were not removed manually.

Flat areas and water were well mapped including small lagoons within wetland areas but artificial elevated structures such as piers, platforms and moored boats under a certain size were classified as wetland. Trees and shrubs were accurately categorized but in some rare cases, waves were misclassified as Scirpus or Carex, especially in the centre of the strips where water reflections were the most intensive (Figure 7). The patterns of the wetland vegetation classification showed striking similarity to the patterns of vegetation patches observed in the field or on aerial photographs. The overlapping areas of strips showed high similarity except for some edge effects.

For the class “wetlands” (Table 3), the user’s accuracy was 97.1% and the producer’s accuracy 100%, indicating that all ground truth points that belonged to wetlands were successfully categorized and only very few non-wetland points were categorized as wetland, mainly open water patches. Therefore, the area of wetlands was only very slightly overestimated.

Trees and shrubs were recognized with a user’s accuracy of 100% and a producer’s accuracy of 98.0% (Table 2). Classifying low trees or shrubs as reed was the only source of omission errors and no commission errors were present. No other vegetation was classified as tree and therefore, the area of trees was minimally underestimated.

The Scirpus class has a user’s accuracy of 97.3% and a producer’s accuracy of 92.3%, but since this is a relatively rare vegetation type in the study area, only 39 ground truth points could be used (Table 2). Omission errors are caused by classification as die-back reed, healthy reed or water, while the single commission error was a misclassification of open water (waves). The area of Scirpus is slightly underestimated.

Water/artificial has a user’s accuracy of 99.0% and a producer’s accuracy of 88.9% (Table 2). The only commission error was a Scirpus area recognized as water, but several omission errors were found, mainly because this category also contained artificial structures such as boats and platforms. In the cases when these were not correctly recognized, they were mainly misclassified as die-back reed because they were close to dropout points (open water). Occasionally this class is also falsely determined as Carex due to the relatively low height. Since omission errors were more frequent than commission errors, the area occupied by water/artificial surfaces was somewhat underestimated.

User’s accuracy for Typha was 72.9%, while producer’s accuracy was 88.6% (Table 2). The number of ground truth points found was slightly lower than the 100 used for most categories. Dark colored reed patches were a source for commission errors, which were also caused by young (bright and low) and dense Typha growth categorized as Carex. Omission errors were nearly all caused by Typha patches and gaps of open water being interpreted by the algorithm as die-back reed. Typha was underestimated as shown by the relatively low user’s accuracy.

The performance of the Carex class could only be tested in 48 control points. User’s accuracy was 82.9%, while producer’s accuracy was only 60.4%, the lowest of all classes (Table 2). While commission errors were relatively rare, they strongly affected the calculated accuracy because of the low number of points, with the most important problem being the categorization of artificial structures as Carex. Omission errors were more frequent, as some of the test points were from exceptionally high Carex stands (as revealed by field notes) and classified as reed, while others probably had a low density and thus lower reflectance and were classified as Typha. This also meant that the presence of Carex in the sites used for ground truthing was strongly underestimated by this classification scheme. If the two non-reed wetland vegetation classes were merged, misclassifications between them were not counted as errors (Table 3), and this single class would have a user’s accuracy of 80.1% and a producer’s accuracy of 84.6%.

The category reed (including all four reed health categories) had a user’s accuracy of 91.6% and a producer’s accuracy of 94.0% (Table 3). The main source of commission errors were patchy Typha areas classified as die-back reed, and very tall Carex stands categorized as healthy or ruderal reed. The main source of omission errors was dark colored (low-density) reed being categorized as Typha. However, since the producer’s and user’s accuracy was relatively close, the overall area occupied by reed was only marginally overestimated.

Healthy reed had a user’s accuracy of 80.2%, and a producer’s accuracy of 80.7% (Table 2). Both commission and omission errors were mainly caused by misidentification of die-back reed, and sometimes created by misclassification of stressed reed. The small difference in accuracies suggested that the overall area occupied by healthy reed was correctly measured.

Die-back reed was classified with a user’s accuracy of 62.5% and a producer’s accuracy of 76.5% (Table 2). Commission errors were abundant as water reflections caused by waves could influence the relatively simple dropout counting algorithm. In these cases, the boundary of wetland vegetation was categorized as die-back reed. Artificial gaps in vegetation or artificial structures creating a patchy pattern were often misclassified as die-back reed. Omission errors were mostly die-back reed being categorized as healthy reed. The current algorithm strongly overestimated the presence of die-back reed.

Stressed reed had a user’s accuracy of 80.4% with a producer’s accuracy of 72.9% (Table 2). The main source of errors was the uncertain separation of die-back and stressed reed, while some omission errors were a result of misclassification as Typha. High grid variance introduced by the vicinity of trees also introduced some artifacts: in these cases healthy reed was misclassified as stressed. The relatively lower producer’s accuracy compared to user’s accuracy shows that the area of stressed reed was overestimated. If die-back and stressed reed were merged to a single category, the corresponding user’s and producer’s accuracy was 80.7% and and 85.4% respectively. This implied that the recognition of reed stress itself was relatively reliable, while quantification of the level of damage was uncertain.

Ruderal reed was recognized with an 84.6% user’s and 78.6% producer’s accuracy, although the number of ground truth points was relatively low (Table 2). Commission errors were mainly caused by false identification as Carex, while omission errors usually involved misclassification as healthy reed. This category was slightly underestimated.

The total accuracy of the survey over the full area of the tested strips was 82.7%, with a Cohen’s Kappa of 0.80. This indicated that although errors and artifacts were still present, the general reliability of the classification process was good, with a strong correspondence between ground truths and the classified map.

The planning and setup of the airborne survey of Lake Balaton had to deal with a number of trade-offs due to limited flight time and ground resources. The main problem was the decision between full coverage of the study area with a limited ALS ground sampling density or, alternatively, only partial coverage with high ALS point density. In order to achieve full coverage of the lake with at least the typical point density of regional-level surveys (1 pt/m²), the flight pattern was set to have only single-line coverage of most of the shore areas (Figure 1). Due to this flight pattern, instead of the typical 50–20% overlap between neighboring (parallel) ALS strips, in most cases the surveyed ALS strips overlapped with their neighbors at the ends only (2–10% overlap, see Figure 1). This compromised the absolute positioning accuracy of the data in the vertical sense, creating errors in the range of 10–50 cm between strips. Although the classical parallel block pattern can seem redundant for lake shore surveys as the majority of the surveyed area is open water, it has the advantage that overlaps between strips can facilitate accurate relative and absolute georeferencing [83]. The sinusoid scan pattern of the Leica sensor resulted in slightly uneven point densities (<1%) across track and this sometimes created artifacts in the surface variance and Sigma Z layers near the strip edges.

The ALS sensor used works with an Automatic Gain Control that modulates the recorded echo intensity to keep it within the range of data representation (8 bits). Due to the fact that the exact gain function was unknown, Lehner et al. [80] tested a linear and an exponential gain function based on homogeneous reflecting areas. The exponential function was found to characterize the gain function better and was therefore utilized within the calibration procedure. However, in some regions the calibration method could not reproduce the real surface reflectances due to the time necessary for the gradual adjustment of the gain control voltage.

As the aircraft moved along the shore, most straight strips started or ended with the full width of the strip occupied by open water. In the cases where this was the start, the gain control was initially adjusted to the low reflectance of water, and as the plane moved in over the coast, the gain control took about one second of time to reach the values necessary for recording the higher intensity dry land echoes. The first scan lines covering the shore were thus collected using gain control values which were too high; therefore the echo amplitude oversaturated the receiver. The radiometrically calibrated reflectances of these echoes were erroneously low (since they were normalized by the high gain control) and did not represent the real reflection properties of the surfaces. In these areas, the vegetation was typically (incorrectly) classified as non-wetland for 100–200 m along the shore (reflectances lower than the minimum of the “wetland” category), and classified as Typha for the next few hundred m approximately, as this was the darkest vegetation category. Since the algorithm was applied to each ALS strip separately, and the ends of the flown strips always overlapped along the shore (Figure 1), this did not have a strong influence on the resulting vegetation map.

Surface reflectance reference values have not been measured with an active reflectometer [61,84] but with a passive spectroradiometer. Despite these difficulties, the reflectance range of the reference areas was sufficient for empirical estimation of the gain control function, which could then be included in the radiometric calibration scheme [80].

The usual method for quality control is cross validation: separating the ground truth polygon dataset, reserving some pixels for automatic validation and using the rest for calibration [30,56,89]. In our case, a different approach was chosen because the relatively low spatial resolution (2.5 × 2.5 m) of the output wetland classification raster restricted the number of pixels available from the 10 × 10 m area of the ground truth polygons. Since the number of pixels used for algorithm calibration is a crucial factor of the classification accuracy achievable [90], all of these were used for signature analysis in case of this study. Although field-based information can be slightly less objective, the ground image-based validation provided widespread spatial coverage and a full range of vegetation types. However, since quality control was not automatically performed, errors of the operator cannot be completely excluded.

The fact that ALS surveying is often carried out in preparation for major construction projects means that the method presented can potentially be applied in rapid impact assessment involving wetland areas, providing information not only on the area but also on the condition of the wetlands that are to be affected. The potential of ALS data for accurate topography mapping and change detection was also recognized by several national and state governments in the last decades [95], leading to full surveys of regions or states [96,97]. These datasets are widely used for forest monitoring, natural hazard recognition, infrastructure planning, geomorphologic investigations and even change detection in the regions that have now been surveyed several times [98]. ALS data are thus already available for large areas including many of the largest European lakes (Austria, Switzerland, Southern Germany, Southern Sweden and Norway, Southern Finland, the Netherlands, major Hungarian lakes). Since these regions have been surveyed with ALS settings similar to those used for this study, it can be expected that wetland maps based on the regional datasets would also have similar accuracy. Even if the created maps would not necessarily correspond to the immediate present, they would support the creation of wide-scale wetland inventories such as [99] and hold valuable information for change monitoring, habitat assessment and theoretical ecology.

Remote sensing scientists are familiar with the trade-off between measuring locally with high accuracy (usually by field sampling), or surveying the whole study area with lower accuracy (by remote sensing). The suitability of readily-available national laser scanning data puts this question into a new perspective: is it better to have potentially near-perfect vegetation classification from a dedicated survey with a specialized instrument, or is it better to use regional ALS data for full coverage of the study area even if the classification might be less reliable?

The EU Water Framework Directive requires EU member states to regularly monitor the health of all aquatic ecosystems, including shore wetlands. While hyperspectral imaging can certainly provide the necessary data, it is currently unrealistic for most member states to perform regular hyperspectral campaigns on all wetlands. Airborne multispectral imaging surveys fused with ALS are also proven to be able to provide the data necessary, but ALS as a standalone tool can produce similar or better accuracies with the proposed method.