Mauro Marty

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (> 10 m above ground), to 15% on average.
Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.

Understanding and monitoring the surrounding environment increasingly rely on its 3D representations. However, the often high costs of 3D data equipment limit its wide usage, and low-cost solutions are in demand. Here, we propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3D point cloud reconstruction. In a forest environment, we evaluated 1) the influence of baseline length on point cloud quality and 2) the suitability of the generated point clouds for extracting primary forest attributes (tree position and diameter). Our results show that the proposed approach allows for feasible 3D reconstruction of complex forest plots. The highest point cloud quality was achieved with a baseline of 60 cm. This setup enabled the correct detection of more than 65% of the trees within the forest plots, producing an average tree position error between 30 and 50 cm and clearly outperforming other setups. A Multiscale Model-to-Model Cloud (M3C2) analysis showed signed distances between the generated point cloud and the reference data with zero mean and 1 m standard deviation. We demonstrate that the proposed approach can be a valuable low-cost solution for 3D point cloud reconstruction, facilitating forest assessment and monitoring.

Understanding the dynamics of treeline ecotones under global change requires long-term ecological and environmental data. The Stillberg ecological treeline research site in the Swiss Alps was established in 1975 by planting 92,000 seedlings of Larix decidua, Pinus cembra and Pinus mugo ssp. uncinata, and has been continuously monitored since then. Here, we present curated long-term data acquired over almost 50 years at the Stillberg site, and we synthesise the major research findings. The long-term datasets comprise 6.5 million ecological and environmental records from the 40-year afforestation experiment, as well as from a 9-year free-air CO₂ enrichment experiment crossed with a 6-year soil warming experiment, a 12-year nutrient addition experiment and an 8-year multifactorial tree seedling recruitment experiment. Our datasets further include 38 million records of 25 meteorological parameters measured at an hourly resolution from 1975 to 1996, and at a 10 min resolution since 1997. We provide all datasets and the corresponding metadata as open research data. Almost five decades of research in this treeline ecotone showed high mortality after tree establishment that was closely related to microclimatic variability. The two Pinus species survived at a much lower rate than L. decidua, due to indirect pathogen interactions. Furthermore, CO₂ enrichment only increased growth of L. decidua, while warming increased growth of P. mugo ssp. uncinata and two Vaccinium shrub species. Enhanced nutrient availability stimulated growth in tree and understorey shrub species. In addition, soil warming and CO₂ enrichment stimulated microbial activity and decreased soil carbon stocks. These findings improve our understanding of ecological processes in the treeline ecotone under global change and confirm the importance of tree growth and establishment limitations. The enhanced availability and quality of these long-term data are expected to foster whole-system approaches and transdisciplinary research syntheses, supporting the development of effective global change adaptation strategies.

Europe's semi-natural grasslands support notably high levels of temperate biodiversity across multiple taxonomic groups. However, these ecosystems face unique conservation challenges. Contemporary agricultural practices have replaced historical traditional low-intensity agriculture in many regions, resulting in a spectrum of management intensities within these ecosystems, ranging from highly intensive methods to complete abandonment. Paradoxically, both extremes along this spectrum of management intensity can be detrimental to biodiversity of semi-natural grasslands. Moreover, while anthropogenic climate change is an overarching threat to these ecosystems, rapid changes in land use and its intensity often present more immediate pressures. Often occurring at a faster rate than climate change itself, these land-use changes have the potential to rapidly impact the biodiversity of these grasslands. Here, we divide the ecological processes, threats, and developments to semi-natural grasslands into three sections. First, we examine the different impacts of agricultural intensification and abandonment on these ecosystems, considering their different consequences for biodiversity. Second, we review seminal works on various evidence-based management practices and offer a concise summary that provides support for various conservation and management strategies. However, the socio-economic factors that drive both abandonment and intensification in semi-natural grasslands can also be used to develop solutions through strategic governmental and non-governmental interventions. Accordingly, we conclude with a way forward by providing several key policy recommendations. By synthesizing existing knowledge and identifying research gaps, this essay aims to provide valuable insights for advancing the sustainable management of semi-natural grasslands.

Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km²), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.

Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 m². Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.

Debris flows are a severe hazard in mountainous regions. However, cost-effective long-term studies of debris flows are seldom, which leads to substantial uncertainties in hazard mitigation methods. This paper investigates whether cost-effective remote sensing techniques can be applied to assess the hazards of mountain torrents and to gather accurate long-term information on the development of the watershed. Torrents that are prone to debris flows are often devoid of vegetation and can thus be well surveyed using photogrammetric methods based on uncrewed aerial vehicle (UAV) surveys. The possibility of extracting automatically torrent parameters from high-resolution terrain models, such as cross-sectional area or slope, is explored. The presented methodology yields continuous and automatically derived parameters along the torrent, which is a major advantage over pointwise field surveys. Cross-validation with field measurements reveals a strong agreement. These parameters are very accurate along highly incised sections, while they are severely limited along sections with steep adjacent hillslopes and/or dense vegetation. We show that these kinds of assessments greatly gain from UAV data followed by automatic parameter extraction. The extracted parameters provide insights so that key sections and weak points can be identified and accurately assessed in the field. We find that UAV data can contribute to a comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground-based fieldwork is still essential and further research on remote sensing-based hazard assessment of torrents prone to debris flows is crucial.

Debris flows are a hazard in mountainous regions. Cost-effective, long-term studies of debris-flow torrents, however, are rare, leading to uncertainties in hazard assessment and hazard prevention. Here, we address the question of whether cost-effective remote sensing techniques can be applied for assessment of mountain torrents and possibly further gather accurate, long-term information on the evolution of the catchment. Torrents prone to debris flows are often devoid of vegetation in the near channel area and hence can be well captured with photogrammetrically derived methods using uncrewed aerial vehicle (UAV) surveys. The possibility of automatically extracting specific torrent parameters from high-resolution terrain models, such as cross-section area or gradient, is investigated. The presented methodology yields continuous and automatically derived geometrical parameters such as torrent bed width, inclination and cross-section area, which is a major advantage compared with point-based, often dangerous field surveys. Their cross-validation with field measurements shows strong agreement. Those parameters are accurate along sharply incised sections with strong limitations along sections with steep adjacent slopes and/or dense vegetation. The information along the torrent allows fast identification of key sections and weak spots which can be precisely evaluated in the field. The study highlights that proper classification of real ground points poses the key challenge. We show that photogrammetric routines to derive a high-resolution digital terrain model (DTM) are limited in the case of dense vegetation coverage. In such cases, LiDAR surveys have clear advantages even though they are also limited by very dense vegetation. We find that UAV data can be used for an objective method of estimating debris-flow torrent geometric properties. And the introduced approaches therefore build a stepping stone towards a more comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground-referencing fieldwork remains essential, and further research on remote sensing supported hazard assessment of debris-flow-prone torrents is indispensable.

Mit dem Ziel, mehr auf Messungen statt auf Schät-zungen beruhende Daten auf grösserer Fläche zu erhalten, werden an der Eidgenössischen Forschungsanstalt WSL und im Schweizerischen Landesforstinventar (LFI) derzeit verschiedene Methoden der nahen Fernerkundung getestet. Mit diesen Methoden können hochaufgelöste 3-D-Punktdaten auf Inventurplots und Dauerbeobachtungsflächen effizient erhoben und Merkmale zu Bäumen und Waldstrukturen abgeleitet werden.

National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction.
The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%.
Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated.

Der Zustand und die Veränderung von Wäldern sowie deren Funktionen und Leistungen werden häufig im Rahmen von stichprobenbasierten Waldinventuren aufgenommen und analysiert. Die rasante Entwicklung im Bereich der nahen Fernerkundung ermöglicht die Erhebung der dreidimensionalen Struktur des Waldes in einem noch nie dagewesenen Detailreichtum. Dies könnte Verbesserungen und Ergänzungen in der Aufnahme von inventurrelevanten Merkmalen wie auch neue Analysen zu verschiedensten Waldfunktionen, wie z.B. die Schutzwirkung, dem Kohlenstoffspeicher aber auch zur  Biodiversität ermöglichen. In dieser Studie wurde das Potenzial unterschiedlicher Technologien der nahen Fernerkundung für einen operationellen Einsatz in grossflächigen, nationalen Waldinventuren überprüft und analysiert.

Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles.
When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km²), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

Nel monitoraggio delle risorse boschive, l’altezza di singoli alberi e dei soprassuoli arborei è un’informazione chiave per la determinazione della struttura e consistenza dei popolamenti e delle loro dinamiche di accrescimento. Dati estensivi di questo tipo sono onerosi da rilevare con misure dirette al suolo.
In questo contributo vengono presentate modalità ed esiti di un test effettuato nel parco Naturale di Monte Corno (Alto Adige - Sudtirolo), comparando un modello digitale di superficie (DSM) di derivazione fotogrammetrica, elaborato attraverso tecniche di image matching, con un analogo modello prodotto da dati LiDAR.
L’area di studio comprende diversi tipi di bosco e altre classi di vegetazione naturale, nonché usi del suolo diversi dalla foresta, che sono stati oggetto di indagine comparativa.
Le differenze medie tra i due DSM sono risultate trascurabili o poco significative negli usi del suolo con scarso o nullo accrescimento in altezza, mentre nei tre tipi di boschi indagati (latifoglie, boschi misti, conifere) esse sono più marcate (tra i 3 e i 6 metri), anche se in gran parte attribuibili all’accrescimento ipsometrico avvenuto nei sette anni intercorsi fra la ripresa Lidar (2007) e quella fotogrammetrica (2014).
Aspetto peculiare di questo studio è stato l’utilizzo di materiale aerofotogrammetrico già disponibile, rilevato per la produzione di ortofoto, in luogo di costose campagne di ripresa eseguite ad hoc.

In monitoring forest resources, the height of individual trees and wood stands is a key information for determining their structure, consistency and growth dynamics. Extensive data of this type are burdensome to detect with direct ground measurements.
This paper reports on procedures and results of a test carried out in the Natural Park of Monte Corno (Bolzano province, South Tyrol - IT), comparing a photogrammetric digital surface model (DSM), processed through image matching techniques, with a similar one produced by Lidar data.
The study area includes different types of forest and other classes of natural vegetation, as well as land uses other than forest, which have been the subject of comparative investigation.
The average differences between the two DSMs are very small in land use classes with little or no growth in height; in the three types of forest investigated (broadleaved, mixed, conifers) they are more marked (between 3 and 6 meters) but largely due to the height increase occurred in the seven years between the Lidar (2007) and the photogrammetric (2014) flights.
A distinctive aspect of this study was the use of aerophotogrammetric material already available, acquired for orthophotos production, in place of expensive ad hoc shooting campaigns.

Waldstrukturen lassen sich mit Methoden der Fernerkundung über grosse Gebiete erfassen. Dabei haben sich Stereoluftbilder als effiziente Grundlage für die Berechnung von digitalen Oberflächenmodellen erwiesen. Zieht man von diesen Oberflächenmodellen das Geländemodell ab und eliminiert künstliche Objekte, erhält man Vegetationshöhenmodelle. Je nach Auflösung der Luftbilder liefern diese in den Vegetationshöhen mehr oder weniger detaillierte Informationen über die Kronenoberflächen. Um Daten zu früheren Vegetationshöhen zu erhalten, wurden im Schweizerischen Landesforstinventar (LFI) auf Basis analoger Schwarz-Weiss-Luftbilder des Bundesamts für Landestopografie aus den Befliegungskampagnen 1979–1985 und 1990–1998 landesweite digitale Oberflächen- und Vegetationshöhenmodelle erstellt. Anschliessend wurden mit den historischen Vegetationshöhenmodellen (Zeitschnitte 1980 und 1990) und dem aktuellen Vegetationshöhenmodell des LFI (Zeitschnitt 2010) die Höhenveränderungen im Schweizer Wald in einer Auflösung von einer Hektare bestimmt. Anders als im Offenland und im Siedlungsgebiet funktionierte die Bildkorrelation im Wald mit den gescannten Schwarz-Weiss-Luftbildern zufriedenstellend. Über eine Million Hektaren Wald konnten in die Analyse einbezogen werden. Sie zeigt, dass die Vegetationshöhe auf fast einem Drittel der Waldfläche seit den 1980er-Jahren kontinuierlich zugenommen hat. Auf einer nahezu gleich grossen Fläche waren die Höhenveränderungen zwischen den drei Zeitschnitten gegenläufig mit Zunahmen in der einen und Abnahmen in der anderen Periode. Die grössten Höhenveränderungen waren im Mittelland zu verzeichnen, die geringsten auf der Alpensüdseite. Kleinräumige Veränderungen und Änderungen unter dem Kronendach können mit diesen Daten allerdings nicht erkannt werden. Trotzdem erlauben die historischen Vegetationshöhenmodelle einen Blick in die Vergangenheit der Schweizer Waldstrukturen.

Forest structure can be captured over large areas using remote sensing methods. Stereo aerial images have proven to be an effective basis for the calculation of digital surface models. If the terrain model is subtracted from these surface models, and artificial objects are eliminated, vegetation height models are obtained. These vegetation height models provide information about the canopy surfaces in forest areas, with a level of detail dependant on the spatial resolution. To obtain data on past forest surfaces, nationwide digital surface and vegetation height models were created in the context of the Swiss National Forest Inventory (NFI) based on analogue black-and-white aerial photographs from the Federal Office of Topography from the 1979–1985 and 1990–1998 aerial campaigns. Subsequently, the historical vegetation height models (time periods 1980 and 1990) and the current vegetation height model of the NFI (time period 2010) were used to determine change in Swiss forest height at a resolution of one hectare. Unlike in open landscapes and settlement areas, the image correlation in forest areas was satisfactory with the scanned black-and-white aerial images. Over one million hectares of forest were included in the analysis. The results show that over almost one third of the forest area vegetation height has continuously increased since the 1980s. Over a nearly equal area, height changes over the two change periods were opposite, with increases in one period and decreases in the other. The largest height changes occurred in the Plateau, the smallest in the Southern Alps. However, smallscale changes and changes under the canopy cannot be detected with these data. Nevertheless, the historical vegetation height models provide a view of past Swiss forest structures.

Observing changes in Earth surface topography is crucial for many Earth science disciplines. Documenting these changes over several decades at regional to global scale remains a challenge due to the limited availability of suitable satellite data before the year 2000. Declassified analog satellite images from the American reconnaissance program Hexagon (KH-9), which surveyed nearly all land surfaces from 1972 to 1986 at meter to sub-meter resolutions, provide a unique opportunity to fill the gap in observations. However, large-scale processing of analog imagery remains challenging. We developed an automated workflow to generate Digital Elevation Models and orthophotos from scanned KH-9 mapping camera stereo images. The workflow includes a preprocessing step to correct for film and scanning distortions and crop the scanned images, and a stereo reconstruction step using the open-source NASA Ames Stereo Pipeline. The processing of several hundreds of image pairs enabled us to estimate reliable camera parameters for each KH-9 mission, thereby correcting elevation biases of several tens of meters. The resulting DEMs were validated against various reference elevation data, including snow-covered glaciers with limited image texture. Pixel-scale elevation uncertainty was estimated as 5 m at the 68% confidence level, and less than 15 m at the 95% level. We evaluated the uncertainty of spatially averaged elevation change and volume change, both from an empirical and analytical approach, and we raise particular attention to large-scale correlated biases that may impact volume change estimates from such DEMs. Finally, we present a case study of long-term glacier elevation change in the European Alps. Our results show the suitability of these historical images to quantitatively study global surface change over the past 40-50 years.

The demand for precise mapping and monitoring of forest resources, such as above ground biomass (AGB), has increased rapidly. National accounting and monitoring of AGB requires regularly updated information based on consistent methods. While remote sensing technologies such as airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have been shown to deliver the necessary 3D spatial data for AGB mapping, the capacity of repeat acquisition, remotely sensed, vegetation structure data for AGB monitoring has received less attention. Here, we use vegetation height models (VHMs) derived from repeat acquisition DAP data (with ALS terrain correction) to map and monitor woody AGB dynamics across Switzerland over 35 years (1983-2017 inclusive), using a linear least-squares regression approach. We demonstrate a consistent relationship between canopy height derived from DAP and field-based NFI measures of woody AGB across four inventory periods. Over the environmentally heterogeneous area of Switzerland, our models have a comparable predictive performance (R² = 0.54) to previous work predicting AGB based on ALS metrics. Pearson correlation coefficients between measured and predicted changes in woody AGB over time increased with shorter time gaps (< 2 years) between image capture and field-based measurements, ranging between 0.76 and 0.34. A close temporal match between field surveys and remote sensing data acquisition is thus key to reliable mapping and monitoring of AGB dynamics, especially in areas where forest management and natural disturbances trigger relatively fast canopy dynamics. We show that VHMs derived from repeat DAP capture constitute a cost effective and reliable approach to map and monitor changes in woody AGB at a national extent and can provide an important information source for national carbon accounting and monitoring of ecosystem service provisioning.

Methoden der Fernerkundung erlauben die Erfassung von 3D Strukturen von Wäldern über grosse Gebiete. Landesweite Berechnungen von Oberflächenmodellen mit photogrammetrischen Verfahren aus digitalen Stereoluftbildern oder ALS Daten werden in einigen Ländern bereits operationell eingesetzt. Die Verfügbarkeit historischer Stereoluftbilder erlaubt die Berechnung digitaler Oberflächenmodelle aus der Vergangenheit mittels photogrammetrischer Methoden. Wir stellen einen Arbeitsfluss vor, mit welchem wir für die Schweiz landesweite Oberflächenmodelle für die 1980er und 1990er Jahre berechnet haben. Es wurden zwei verschiedene Bildmatching Verfahren getestet und die Ergebnisse hinsichtlich der Vollständigkeit der Bildkorrelation verglichen. Die Ergebnisse zeigen, dass beide Verfahren trotz der zum Teil geringen Kontraste in den digitalisierten schwarz-weissen Luftbildern zufriedenstellende Ergebnisse erzielten. Auf offenen Wiesen und im Gebiet der Landwirtschaft war die Vollständigkeit gering. In Waldgebieten jedoch deutlich höher.

The landscape-human relationships on the Alps, the more populated mountain region globally, depend on tree species diversity, their canopy height and canopy gaps (soil cover). The monitoring of such forest information plays an important role in forest management planning and therefore in the definition of present and future mountain forest services. In order to gain wide scale and high-resolution forest information, very high-resolution (VHR) stereo satellite imagery has the main benefit of covering large areas with short repetition intervals. However, the application of this technology is not fully assessed in terms of accuracy in dynamic year-around forest conditions. In this study, we investigate on four study sites in the Swiss Alps 1) the accuracy of forest metrics in the Alpine forests derived from VHR Pléiades satellite images and 2) the relation of associated errors with shadows, terrain aspect and slope, and forest characteristics. We outline a grid-based approach to derive the main forest metrics (descriptive statistics) from the canopy height models (CHMs) such as the maximum height (H_max), height percentiles (Hp95, Hp50), the standard deviation of the height values (H_Std) and canopy gaps. The Pléiades-based forest metrics are compared with those obtained by aerial image matching, a technology operationally used for deriving this information. For the study site with aerial and satellite images acquired almost at the same time, this comparison shows that the medians of Pléiades forest metrics error are -0.25 m (H_max), 0.33 m (Hp95), −0.03 m (H_Std) and -5.6% for the canopy gaps. The highest correlation (R² = 0.74) between Pléiades and aerial canopy gaps is found for very bright areas. Conversely, in shadowed forested areas a R² of only 0.16 is obtained. In forested areas with steep terrain (>50°), Pléiades forest metrics show high variance for all the study areas. Concerning the canopy gaps in these areas, the correlation between Pléiades and the reference data provides a correlation value of R² = 0.20, whereas R² increases to 0.66 for gently sloped areas (10-20°). The aspect does not provide a significant correlation with the accuracy of the Pléiades forest metrics. However, the extended shadowed mainly on north/northwest facing slopes caused by trees or terrain shade negatively affect the performance of stereo dense image matching, and hence the forests metrics. The occurrence of strong shadows in the forested areas increases dramatically by ˜40% in the winter season due to the lower sun elevation. Furthermore, due to the leaf-off condition in the winter season dense image matching may fail to derive the canopy heights. Our results show that Pléiades CHMs could be a useful alternative to CHMs based on aerial images matching for monitoring forest metrics and canopy gaps in mountain forests if captured during leaf-on conditions. Our study offers forest research, as well as forest management planning, the benefit of a better understanding of the performance of VHR satellite imagery used for forest inventory in mountainous regions and in similar forest environments.

Until today, snow depth data is mostly estimated based on point measurements, either collected manually or at automated weather stations. New photogrammetric technologies to map the snow depth distribution spatially continuous over larger areas are rapidly evolving. Especially in the field of unmanned aerial systems (UAS) new systems with better cameras and longer flight times are being developed. However, the specific strengths and weaknesses as well as the performance considering accuracy on homogeneous snow surfaces are not yet sufficiently investigated.

Therefore, we have simultaneously tested different photogrammetric platforms and sensors for snow depth mapping in the high alpine Dischma valley close to Davos (Switzerland) during winter season 2017/18. An extraordinarily snow rich winter challenged the data referencing approaches in particular. We acquired data over the whole Dischma valley with satellite and airplane based optical sensors. At two subsets in the Dischma valley, we flew with two different UAS and acquired ground-based data with a digital consumer camera. For the independent validation of the photogrammetric products we applied terrestrial laser scanning and measured snow depth by probing as well as by measurements on fixedly installed snow poles.

In this study we outline the experimental setup for a large campaign with different photogrammetric sensors and platforms. That will allow for an improved understanding of the specific advantages and disadvantages of different photogrammetric systems for operational, spatially continuous snow depth mapping in high alpine terrain over large areas.