Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Primary succession and its driving variables – a sphere-spanning approach applied in proglacial areas in the upper Martell Valley (Eastern Italian Alps)

Climate change and the associated glacier retreat lead to considerable enlargement and alterations of the proglacial systems. The colonisation of plants in this ecosystem was found to be highly dependent on terrain age, initial site conditions and geomorphic disturbances. Although the explanatory variables are generally well understood, there is little knowledge on their collinearities and resulting influence on proglacial primary succession. To develop a sphere-spanning understanding of vegetation development, a more interdisciplinary approach was adopted. In the proglacial areas of Fürkeleferner, Zufallferner and Langenferner (Martell Valley, Eastern Italian Alps), in total 65 plots of 5×2 m were installed to perform the vegetation analysis on vegetation cover, species number and species composition. For each of those, 39 potential explanatory variables were collected, selected through an extensive literature review. To analyse and further avoid multicollinearities, 33 of the explanatory variables were clustered via principal component analysis (PCA) to five components. Subsequently, generalised additive models (GAMs) were used to analyse the potential explanatory factors of primary succession. The results showed that primary succession patterns were highly related to the first component (elevation and time), the second component (solar radiation), the third component (soil chemistry), the fifth component (soil physics) and landforms. In summary, the analysis of all explanatory variables together provides an overview of the most important influencing variables and their interactions; thus it provides a basis for the debate on future vegetation development in a changing climate.</p

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

The comparison of auditory, tactile, and multimodal warnings for the effective communication of unexpected events during an automated driving scenario

In an automated car, users can fully engage in a distractor task, making it a primary task. Compared to manual driving, drivers can engage in tasks that are difficult to interrupt and of higher demand, the consequences can be a reduced perception of, and an impaired reaction to, warnings. In this study we compared three in-vehicle warnings (auditory, tactile, and auditory-tactile) which were presented during three highly attention capturing tasks (visual, auditory, and tactile) while the user was engaged in a self-driving car scenario, culminating in an emergency brake event where the warning was presented. The novel addition for this paper was that three set paced, attention capturing tasks, as well the three warnings were all designed in a pilot study to have comparable workload and noticeability. This enabled a direct comparison of human performance to be made between each of the attention capturing tasks, which are designed to occupy only one specific modality (auditory, visual or haptic), but remain similar in overall task demand. Results from the study showed reaction times to the tactile warning (for the emergency braking event) were significantly slower compared to the auditory and auditory-tactile (aka multimodal or multisensory) warning. Despite the similar reaction times between the in-vehicle auditory warning and the multimodal warning, the multimodal warning led to a reduced number of missed warnings and fewer false responses. However, the auditory and auditory-tactile warnings were rated significantly more startling than the tactile alone. Our results extend the literature regarding the performance benefits of multimodal warnings by comparing them with in-vehicle auditory warnings in an autonomous driving context. The set-pace attention capturing tasks in this study would be of interest to other researchers to evaluate the interaction in an automated driving context, particularly with hard to interrupt and attention capturing tasks

Applying data visualisation for a rapid overview and comparison of HFE measures to evaluate in-vehicle interfaces

Given the variety of measures which can be used to determine driver performance, user experience or distraction within the field of human factors, selecting which measure to use in user trial evaluations can be a challenging exercise. This is true for those with limited knowledge (novices) or experts who may have a tendency to rely on tried and trusted, familiar measures. This project aims to develop a user-friendly ‘toolkit’, applying data visualisation methods to explore interface metaphors representing measures for usability and user experience in the area of in-vehicle interfaces. Primary data to populate the toolkit were collected via literature review and user requirements through interviews. The concept of data visualisation aims to provide the user planning a user trial with an understanding of the variety of measures, and encourage them to explore measures in the database. The following research questions are explored: (1) Conservation and distribution of practical implicit knowledge in the selection and application of measurements for user studies for HMI experts and novices, (2) An easy comparison of measures applying a graphical presentation, (3) An easyto-use aid for new employees, helping them to understand the basic measurement concepts and criteria important to select measures for user studies

A Link Between Trust in Technology and Glance Allocation in On-Road Driving

This paper examines whether there is an association between preexposure trust in technology and subsequent glance behavior when interacting with a technology that was relatively novel for the majority of participants. After rating their level of trust in technology on a questionnaire, participants drove one of two vehicle models on a highway and engaged in a voice-based navigation address entry task. Subjective ratings of trust in new car technologies were found to be significantly positively correlated with a higher frequency of glances across all coded glance regions during the task. In one of the voice-interface implementations, these higher ratings of trust were also associated with a higher frequency of glances to the user interface, but with fewer long duration (>2s) glances per minute. A lower trust in technology in general showed some association with taking more time to complete interactions. The findings are discussed as highlighting the potential value of further research into the associations between trust and visual scanning behavior

Soil chemical and mineralogical data from moraine chronosequences of the proglacial areas of Stein glacier (Sustenpass) and Griess glacier (Klausenpass) in the Swiss Alps

This dataset comprises soil chemical and mineralogical data of the moraine soil chronosequences from Sustenpass and Klausenpass in the Swiss Alps. The chronosequences span from 30 to 10,000 yrs (Sustenpass, siliceous parent material) and from 110 to 13,500 yrs (Klausenpass, calcareous parent material), respectively. Parameters include: pH (CaCl2), loss on ignition (LOI, 550°C), Corg, N. Elemental contents (measured by X-ray Fluorescence, XRF ) and calculated tau (open-system mass transport function) and mass balances for Na, Al, Mg, Si, P, K, Ca, Mn, Fe. Lastly, the bulk mineralogy of the fine earth (measured by X-ray Diffraction, XRD). Sampling was conducted in August/September 2017 at Sustenpass and Klausenpass and was part of the HILLSCAPE (Hillslope Chronosequence and Process Evolution) project's sampling campaign. pH, LOI and XRF measurements were conducted in 2017/2018 at the University of Zurich (Switzerland), the mineralogy was measured in 2020 at ETH Zurich (Switzerland), Corg and N were measured in 2020 at the University of Zurich. The calculations of the open-system mass transport function and mass balances were performed in 2021 at the University of Zurich. These data were collected to elucidate soil development in siliceous and calcareous parent materials in order to better understand the evolution of hillslope processes over time. They were described and discussed in detail in Musso et al. 2022 (doi:10.3390/geosciences12020099). For hydrological or geobotanical data from the same chronosequences, see for instance the publications of F. Maier (doi:10.1029/2021WR030223, doi:10.1029/2021WR030221, doi:10.1016/j.catena.2019.104353), K. Greinwald (doi:10.1080/15230430.2020.1859720, doi:10.1111/jvs.12993) and A. Hartmann (doi:10.5194/hess-2020-28, doi:10.5194/essd-12-3189-2020)