Dr. Ruedi Bösch

Tree volume is a key feature in forest monitoring, delivering information, such as wood availability or forest carbon balance. To date, tree volume, i.e. the total volume of the above ground woody parts of a tree, cannot be measured directly with conventional tools. Terrestrial laser scanning (TLS) offers the potential to directly measure tree volume. However, its application in forest monitoring requires a profound understanding of the precision and accuracy of retrieval approaches. In this study, we present a simulation environment for evaluating TLS application in forest inventories. We investigate the influence of understorey density, scanner placement and TLS sensor type on volume estimation of tree parts of varying diameters. Using information from 30 sample plots from the Swiss NFI to simulate 197 sample trees, we evaluate three understorey densities, five scanner locations and their combinations and three realistic and one hypothetic (geometric scanning) TLS sensor types. We show that tree volume estimates from point clouds are biased to certain extent: from about 25% for small trees to a few percent for larger trees above 40 cm diameter at breast height (DBH). Especially small tree parts (diameters 7 cm) lack accurate and precise estimation. In small trees with 12 cm DBH they are overestimated by 110% in average with a high variation, whereas they are underestimated in large trees, i.e. with DBH 75 cm, by 50% in average. Volume estimation of small tree parts is subject to physical limits of TLS, however the estimation of volume of large tree parts could be feasible with appropriate TLS settings and field protocols. Nevertheless, tree volume estimation using TLS must be understood in greater depth before it can be applied regularly in forest inventories.

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.

In recent years, portable laser scanning devices and their applications in the context of forest mensuration have undergone rapid methodological and technological developments. Devices have become smaller, lighter, and more affordable, whereas new data-driven methods and software packages have facilitated the derivation of information from point clouds. Thus, terrestrial laser scanning (TLS) is now well established, and laser-object interactions have been studied using theoretical, modeling, and experimental approaches. The representation of scanned objects in terms of accuracy and completeness is a key factor for successful feature extraction. Still, little is known about the influence of TLS and survey properties on point clouds in complex scattering environments, such as forests. In this study, we investigate the influence of laser beam diameter and signal triggering on the quality of point clouds in forested environments. Based on the Swiss National Forest Inventory data, we simulate the TLS measurements in 684 virtual forest stands using a 3-D content creation suite. We show that small objects lack sufficient representation in the point cloud and they are further negatively influenced by large laser beam diameters, dense stands, and large distances from the scanning device. We provide simulations that make it possible to derive a rationale for decisions regarding the appropriate choice of TLS device and survey configuration for forest inventories.

New technological developments modulate the light levels of LED street luminaires according to traffic volumes: light levels are increased given traffic and reduced in its absence. Such dimming of street lights reduces the level of artificial light at night (ALAN) and may thus contribute to mitigate light pollution. To quantify the impact of traffic-driven dimming of street lights on nocturnal insect abundance and bat activity in comparison to full light (i.e., dimming functions of luminaires switched off), we mounted 20 insect flight-interception traps and ten batloggers on street light poles along two dimmable street light sections. Insect abundance and bat activity were measured alternately with one week of full street lighting followed by a week with light levels modulated by traffic volumes. In total, 16 dimmed and 16 full-light days were investigated. Overall, traffic-driven dimming reduced light levels by 35%. Weather conditions (warm, dry nights) were the main drivers of insect abundance and bat activity, but traffic-driven dimming resulted in lower numbers of insects caught and reduced bat activity. Among insect groups, Heteroptera benefited most from dimming. For bats, urban exploiters (Pipistrellus spp.) benefited from increased availability of prey at brightly lit street lights, while less frequent species (Myotis spp.) did not benefit from street lighting. We conclude that street light dimming technology may contribute to mitigate negative effects of ALAN on nocturnal organisms, although the measure may not be efficient enough to support light-sensitive and threatened species.

Rock glaciers, tonguelike bodies consisting of angular rock debris frozen in interstitial ice, may flow with velocities varying from a few centimeter up to several meters per year. Recent ground warming may generate an accelerated slope movement related to permafrost creep.
Newer generations of commercial off-the-shelf drones like DJI Mavic are cost-effective, lightweight and still have a sufficient pay load reserve for flights at 2800 m.a.s.l. This allows that a two men crew can carry the necessary reference and drone equipment in a high alpine environment and a flight campaign can be conducted within one day.
Using different cameras with similar optical resolution allows to generate DSMs with comparable accuracy. The DSM resolution should be 10 cm or better to achieve robust results. Motion tracking of rock glacier with tie point matching algorithms SIFT or SURF combined with an appropriate filter method allows to distinguish different movement patterns within local neighborhood. Compared to point-wise GPS-based methods or image-based cross-correlation approaches, tie point matching allows to detect inhomogeneous movement patterns, which are typical for rock glaciers in a high alpine environment.

Künstliches Licht bei Nacht wird zunehmend als Bedrohung für die biologische Vielfalt und für Ökosysteme erkannt. Mit dimm- und vor allem individuell steuerbaren LED-Strassenbeleuchtungen kann dieLichtstärke flexibel dem aktuellen Verkehrsaufkommen angepasst werden.

Methods and workflows for creating area-wide products using remote sensing methods have been developed for various reasons. For the sample-based estimates in the Swiss National Forest Inventory (NFI), area-wide data sets are used for the two-phase estimations, which have substantially lower estimation errors than one-phase inventories. Using area-wide data sets, it is possible to skip dense manual interpretation of the stereo-images and thus save resources. Further, many functions of the forest, such as biodiversity and protection against natural hazards, can be described and quantified better with area-wide spatial data than with field plot data.

 

Mapping changes in vegetation cover is essential for understanding the consequences of climate change on Arctic ecosystems. Classification of ultra-high spatial-resolution (UHR, < 1 cm) imagery can provide estimates of vegetation cover across space and time. The challenge of this approach is to assure comparability of classification across many images taken at different illumination conditions and locations. With warming, vegetation at higher elevation is expected to resemble current vegetation at lower elevation. To investigate the value of classification of UHR imagery for monitoring vegetation change, we collected visible and near-infrared images from 108 plots with hand-held cameras along an altitudinal gradient in Greenland and examined the classification accuracy of shrub cover on independent images (i.e. classification transferability). We implemented several models to examine if colour calibration improves transferability based on an in-image calibration target. The classifier was trained on different number of images to find the minimum training subset size. With a training set of ~ 20% of the images the overall accuracy levelled off at about 81% and 68% on the non-calibrated training and validation images, respectively. Colour calibration improved the accuracy on training images (1–4%) while it only improved the classifier transferability significantly for training sets < 20%. Linear calibration only based on the target’s grey series improved transferability most. Reasonable transferability of Arctic shrub cover classification can be obtained based only on spectral data and about 20% of all images. This is promising for vegetation monitoring through multi-image classification of UHR imagery acquired with hand-held cameras or Unmanned Aerial Systems.
 

Zur Orientierung verlassen sich nachtaktive Insekten auf das Licht der Gestirne. Kommen künstliche Lichtquellen wie zum Beispiel Strassenleuchten in die Quere, fliegen die Insekten in spiralförmigen Bahnen auf die Leuchten zu - im Bestreben, den Kurs zu korrigieren, weil die Leuchten viel näher sind als die Gestirne. Zwar verbrennen die Tiere bei modernen LED-Strassenleuchten nicht mehr, aber künstliche Lichtquellen führen nach wie vor dazu, dass die Tiere die Leuchten bis zur Erschöpfung umkreisen. Kann eine geringere Helligkeit der Strassenbeleuchtung Insekten und insektenjagende Fledermäuse positiv beeinflussen und die Barrierewirkung von künstlichem Licht verringern? Die WSL ist diesen Fragen nachgegangen und hat in Zusammenarbeit mit EKZ (Elektrizitätswerke des Kantons Zürich) einen Pilotversuch an dimmbaren LED-Strassenleuchten gestartet.

Les insectes nocturnes se servent des astres pour s'orienter. Dès lors que la lumière artificielle d'un lampadaire, par exemple, entre dans leur champ de vision, ils rectifient leur trajectoire et se mettent à progresser en spirale vers ce nouveau point de repère, bien plus proche que les astres. S'ils ne meurent plus brûles au contact des lampes à LED utilisées pour l'éclairage public moderne, ils continuent toutefois d'être attirés par les sources de lumière artificielle et de voler autour d'elles jusqu'à l'épuisement. La baisse de la luminosité produite par les lampadaires peut-elle avoir une influence positive sur les insectes ainsi que les chauves-souris insectivores et, partant, réduire l'effet barrière de la lumière artificielle? L'institut WSL s'est penché sur ces questions et a lancé un projet pilote sur l'éclairage public a LED d'intensité variable, en coopération avec EKZ (Entreprises électriques du canton de Zurich).

BatScope is a free application for processing acoustic high-frequency recordings of bats. It can import data, including meta-data information, from recorders such as Batlogger. The resulting content can be filtered visually as spectrograms or according to data fields and can be displayed. Automated processing includes detecting and extracting of echolocation calls, filtering noise, and measuring statistical parameters. Calls are classified to species by statistically matching to a reference database. A weighted list of classifiers helps to assign the most likely species per call. Classifiers were trained on 19 636 echolocation calls of 27 European bat species. When classifiers all agree on a species (76.4% of all cases), the mean correct classification rate reaches 95.7%. A sequence's summary statistic indicates the most likely species occurring therein. Classifications can be verified visually, by filtering, and by acoustic comparison with reference calls. Procedures are available for, e.g., excluding dubious cutouts from the statistics and for accepting or overriding the proposed species assignment. Acoustic recordings can be exported and exchanged with other users. Finally, the verified results can be exported to spreadsheets for further analyses and reporting. We currently reprogram BatScope using Java, PostgreSQL, and R to reach a unified and portable software architecture.

BatScope est une application gratuite de traitement d'enregistrements acoustiques de haute fréquence de chauvessouris. Elle peut importer des données, y compris les métadonnées associées, d'enregistreurs comme Batlogger. Le contenu en découlant peut être filtré visuellement sous forme de spectrogrammes ou selon des champs de données, et affiché. Le traitement automatisé comprend la détection et l'extraction de cris d’écholocalisation, le filtrage du bruit et la mesure de paramètres statistiques. Les cris sont affectés à l’espèce par jumelage statistique à des cris dans une base de données de référence. Une liste pondérée d'opérateurs de classement aide à l'affectation des cris à l'espèce la plus probable. L'entraînement des opérateurs de classement repose sur 19 636 cris d'écholocalisation de 27 espèces de chauves-souris européennes. Quand tous les opérateurs de classement sont en accord (76,4 % de tous les cas), le taux moyen de classement correct atteint 95,7 %. Une statistique résumant chaque séquence indique les espèces les plus susceptibles d'y être représentées. Les classements peuvent être vérifiés visuellement par filtrage et comparaison acoustique à des cris de référence. Des procédures sont disponibles pour, par exemple, exclure des portions découpées de spectrogramme douteuses des statistiques et pour accepter ou remplacer manuellement l'affectation d'espèce proposée. Les enregistrements acoustiques peuvent être exportés et échangés avec d'autres utilisateurs. Enfin, les résultats vérifiés peuvent être exportés vers des tableurs pour des analyses plus approfondies et la préparation de rapports. Nous sommes actuellement à reprogrammer BatScope en Java, PostgreSQL et R pour en arriver à une architecture logicielle unifiée et portable.

Climate change will have a significant influence on vegetation health and growth. Predictions of higher mean summer temperatures and prolonged summer draughts may pose a threat to agriculture areas and forest canopies. Rising canopy temperatures can be an indicator of plant stress because of the closure of stomata and a decrease in the transpiration rate.
Thermal cameras are available for decades, but still often used for single image analysis, only in oblique view manner or with visual evaluations of video sequences.
Therefore remote sensing using a thermal camera can be an important data source to understand transpiration processes.
Photogrammetric workflows allow to process thermal images similar to RGB data. But low spatial resolution of thermal cameras, significant optical distortion and typically low contrast require an adapted workflow. Temperature distribution in forest canopies is typically completely unknown and less distinct than for urban or industrial areas, where metal constructions and surfaces yield high contrast and sharp edge information.
The aim of this paper is to investigate the influence of interior camera orientation, tie point matching and ground control points on the resulting accuracy of bundle adjustment and dense cloud generation with a typically used photogrammetric workflow for UAV-based thermal imagery in natural environments.

Operating unmanned aerial systems (UAS) to record imagery of alpine snow cover for photogrammetric applications is challenging due to topographic and meteorological conditions. Furthermore, the homogenous snow surface encumbers the automated detection of matching points, which are required for generating accurate digital surface models (DSMs). On the other hand, there is rising demand for high spatial resolution snow depth and snow surfacetype mapping as well as snow avalanche documentation. UAS have the potential to enable flexible, timely, and cost-efficient data acquisition, even in poorly accessible alpine terrain. This could be a major step forward for many applications in snow hydrology, avalanche research, mitigation measure planning, hazard zonation, and alpine ecology investigations.
In this study, we investigate the applicability and performance of UAS-based structure-from-motion (SfM) photogrammetry on very homogenous snow surfaces, under suboptimal illumination conditions, at two alpine test sites in Tschuggen (2000 m a.s.l.) close to Davos, Switzerland, and Lizum (2000 m a.s.l.) near Innsbruck, Austria. We discuss the topographic and meteorological challenges for flying UAS missions in high-alpine terrain. Additionally, we compare DSMs calculated from the imagery acquired in the visual (VIS, λ = 400–700 nm) and near-infrared (NIR, λ = 700–830 nm) parts of the electromagnetic spectrum. We evaluate the resulting DSMs qualitatively and quantitatively by applying: a) differential Global Navigation Satellite System (GNSS) measurements at the Swiss test site, with an expected accuracy better than 0.1 m within x, y, and z directions; b) terrestrial laser scanning (TLS) at the Austrian test site, with an expected accuracy of ± 0.025 m (1σ), along with a distance-dependent error. The results of this study reveal the potential and limitations of UASbased SfM photogrammetry for applications on snow-covered, alpine terrain in general and in particular the benefit of NIR imagery on the accuracy and precision of the results.

The use of Unmanned Aerial Vehicles (UAV) for photogrammetric surveying has recently gained enormous popularity. Images taken from UAVs are used for generating Digital Surface Models (DSMs) and orthorectified images. In the glaciological context, these can serve for quantifying ice volume change or glacier motion. This study focuses on the accuracy of UAV-derived DSMs. In particular, we analyze the influence of the number and disposition of Ground Control Points (GCPs) needed for georeferencing the derived products. A total of 1321 different DSMs were generated from eight surveys distributed on three glaciers in the Swiss Alps during winter, summer and autumn. The vertical and horizontal accuracy was assessed by cross-validation with thousands of validation points measured with a Global Positioning System. Our results show that the accuracy increases asymptotically with increasing number of GCPs until a certain density of GCPs is reached. We call this the optimal GCP density. The results indicate that DSMs built with this optimal GCP density have a vertical (horizontal) accuracy ranging between 0.10 and 0.25 m (0.03 and 0.09 m) across all datasets. In addition, the impact of the GCP distribution on the DSM accuracy was investigated. The local accuracy of a DSM decreases when increasing the distance to the closest GCP, typically at a rate of 0.09 m per 100-m distance. The impact of the glacier's surface texture (ice or snow) was also addressed. The results show that besides cases with a surface covered by fresh snow, the surface texture does not significantly influence the DSM accuracy.

The need for up-to-date and accurate information on forest resources has rapidly increased in recent years. Forest mapping is an important source of information for the assessment of woodland resources and a key issue for any National Forest Inventory (NFI). Nowadays, new perspectives for automated forest mapping are emerging through the latest developments in remote sensing data and techniques. n this chapter, an overview of current remote sensing data and techniques for mapping woodland and forests, the challenges and requirements for optimization and automation, and the need for validating final products are presented. Special attention is paid to land use - a crucial criterion for forest mapping which, in contrast to land cover, cannot be easily derived from remotely sensed data. Three different approaches for extracting woodland areas (i.e., patches of trees and shrubs) are presented, all of which involve a high degree of automation. Two additional approaches, which are based on NFI forest definitions, are presented. These require the subdivision of woodlands into the classes “used for forestry” and “other use” and implement the criteria “height,” “minimum crown coverage,” “minimum area,” “minimum width,” and “land use”. Special attention is paid to connecting patches using distance criteria from national forest definitions. The main points of this chapter are as follows: (1) forest needs an exact definition which may differ depending on the country, (2) mapping woodland can be highly automated and is indispensable prior to mapping forests, and (3) forest mapping is now feasible using remote sensing data and techniques; however, it is less automated due to the implementation of a forest definition.

Photogrammetric workflows for aerial images have improved over the last years in a typically black-box fashion. Most parameters for building dense point cloud are either excessive or not explained and often the progress between software releases is poorly documented. On the other hand, development of better camera sensors and positional accuracy of image acquisition is significant by comparing product specifications. This study shows, that hardware evolutions over the last years have a much stronger impact on height measurements than photogrammetric software releases.
Snow height measurements with airborne sensors like the ADS100 and UAV-based DSLR cameras can achieve accuracies close to GSD * 2 in comparison with ground-based GNSS reference measurements. Using a custom notch filter on the UAV camera sensor during image acquisition does not yield better height accuracies. UAV based digital surface models are very robust. Different workflow parameter variations for ADS100 and UAV camera workflows seem to have only random effects.

Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km² on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.
Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

Photogrammetry of the snow cover is expected to be difficult, because of its homogenous, low-contrast surface, hindering the automated detection of matching points to generate digital surface models. On the other hand there is rising request for high spatial resolution snow depth and snow surface type mapping as well as snow avalanche event documentation. Unmanned aerial systems (UAS) enable flexible, efficient and economic data acquisition, even within inaccessible alpine terrain. The efficient and economic generation of high-quality digital surface models (DSM) of snow covered surfaces would be a major step forward for many applications in snow hydrology and hydropower generation, avalanche research and warning, for winter tourism as well as for alpine ecology investigations. We investigate the performance of UAS-based structure-from-motion (SfM) photogrammetry on very homogenous snow surfaces at the test site Tschuggen at 2,000 m a.s.l. close to Davos, Switzerland under diffuse illumination conditions, caused by a completely overcast sky, a situation that could be described as “worst case” for photogrammetry. We investigate the benefit of near infrared information (λ > 830 nm), compared to imagery acquired within the visible part of the electromagnetic spectrum (λ = 400 – 700 nm) and a combination of these two. We evaluate the accuracy of the different digital surface models (DSMs) qualitatively and quantitatively by applying differential Global Navigation Satellite System measurements with an expected accuracy better than 10 cm in x, y and z directions. The results of this study enable an assessment of the potential and the limitations of SfM photogrammetry for applications on snow-covered terrain.