Carinthia II / Part 3 - Carinthia Nature Tech
Carinthia Nature Tech (Carinthia II/ Part 3) is a scientific journal dedicated to the application of disruptive technologies in the field of nature conservation. The journal aims to feature advanced testing, research, and development of novel technologies for recording and analyzing biodiversity, including species, populations, habitats, and ecosystems. It also covers monitoring geological, hydrological, and climatic systems, and innovative methods to monitor environmental pressures.
Natural Science Association for Carinthia, Carinthia University of Applied Sciences
How fast must one run to stay in the same place? On the second issue of Carinthia Nature Tech
Copyright (c) 2025 Carinthia II / Part 3 - Carinthia Nature Tech https://creativecommons.org/licenses/by-nc/4.0
Carinthia Nature Tech: a new forum for integrating natural sciences and emerging technologies
Copyright (c) 2024 Carinthia II / Part 3 - Carinthia Nature Tech https://creativecommons.org/licenses/by-nc/4.0
“Tage der Biodiversität 2025” – Collection of abstracts
Making science visible: On the third issue of Carinthia Nature Tech
We are pleased to present the third publication of the journal Carinthia Nature Tech with this issue. While the first release marked the achievement of launching this journal after a long process, the second release provided an opportunity to consolidate certain workflows, results, and presentations. Now, the current issue demonstrates that Carinthia Nature Tech has already evolved into a series. Many more issues are planned to showcase and communicate the potential of new technologies for capturing, monitoring, and conveying nature in general, and biodiversity in particular. In doing so, we know we are in distinguished company worldwide.
Handbuch Naturschutzfachkraft: a guide to getting things done in conservation
Monitoring biodiversity: Hitting the nail on the head with Conservation Technology
Restoring ecosystems, advancing science: The Metschacher Moos outdoor lab
On the occasion of the realignment of research activities at the Metschacher Moos, this article compiles key information about the natural environment, land use history, previous scientific studies, and planned initiatives. The State of Carinthia maintained a long-term lease on the site, located in a side branch of the Glan Valley (Carinthia), to restore wet meadows that were drained in the 20th century. With the acquisition of the land by the Kärntner Sparkasse Foundation, these restoration efforts and scientific activities can now be continued. This presents an opportunity to develop a research site dedicated to restoration and the application of biodiversity technologies (BiDiTechs). The project "S.O.S. Metschacher Moos," funded by the Austrian Biodiversity Fund, enabled initial measures to resume management activities, including control of invasive species, restoration of monitoring plots, and installation of a sensor network. Further research projects are underway, aiming to contribute to the assessment of biodiversity and ecosystem services in restoration initiatives.
Development and field testing of a portable eDNA water sampler
One of the methods currently revolutionizing biodiversity assessment is the analysis of environmental DNA (eDNA). Here we present a prototype of a semi-automatic sampler for water eDNA collection. The sampler is a portable, affordable, battery-powered device that supports hands-free filtration using peristaltic pumps and allows for the processing of multiple water samples under field conditions. The prototype system consists of commonly available components including an ESP32 microcontroller, a 3D-printed battery adapter, voltage converters, and two peristaltic pumps. The system is controlled by a simple push-button starting a 20-minute sampling cycle. The filtration setup ensures that contamination is avoided by placing the filter before the pump in the water flow path. Initial field testing was conducted at Lake Silbersee in Carinthia, Austria, where successful filtrations were carried out using different filters. Concentration measurements of extracted eDNA samples ranged from 9.8 to 24.0 ng/µL, confirming that the device can collect eDNA for water biodiversity assessment. Tests also revealed that filters became clogged after 1 L of filtration in the given water source, supporting the decision to limit operation time. Future improvements to the prototype may be possible by adding further pump capacity, a display, data logging, and a web-based interface to further increase usability and automation.
Establishment of the Miyawaki forest at Carinthia University of Applied Sciences in Villach
The Miyawaki method offers a fast and effective nature-based solution for the restoration of ecosystems, especially in urban areas. Developed by Akira Miyawaki, the approach relies on dense planting of native species to create diverse, multi-layered forest ecosystems, resulting in a so-called Miyawaki Forest (MF). In April 2025, an MF was created on the campus of Carinthia University of Applied Sciences (CUAS) in Villach as part of the Interreg IT-AT project BioBox to promote urban biodiversity, especially for pollinators and birds. Following soil analysis, an area of 91 m² was prepared by plowing and enrichment with straw and compost. Inspired by the species composition of local riparian forests along the Drava River, nearly 300 saplings of native trees and shrubs were planted in three vertical layers by 16 volunteers. The MF at CUAS will serve as an outdoor laboratory for the students to monitor soil, growth and biodiversity. Although they are still rare in Austria, MF — like the CUAS pilot project — demonstrate the potential of small-scale urban forests to increase biodiversity, improve ecosystem services and engage communities. The Miyawaki method offers significant long-term environmental benefits, including better air and soil quality, urban cooling and carbon sequestration.
Applying a slime mold-inspired algorithm to network planning challenges in the Carinthian region
This article presents the application of the slime mold-based algorithm Simulation of Slime Molds (SISMO) using case studies from the Carinthian region, demonstrating its applicability to various network structures. SISMO is inspired by the ability of the myxomycete Physarum polycephalum to process information and solve optimization tasks. The effectiveness of SISMO in network formation and finding shortest paths is first evaluated using parts of Carinthia’s bus and railway infrastructure. Our results show that SISMO can create networks similar to the existing transportation network, consider obstacles, and replicate existing connections. These findings allow conclusions for the future optimization of further networks using bio-inspired approaches. Based on these insights, this paper presents an approach that uses SISMO to create a repair plan for the Carinthian power transmission network after a simulated electromagnetic pulse attack. The algorithm is applied to a corresponding graph model to identify the most critical areas to be repaired, analogous to the nutrient supply of the slime mold between food sources. SISMO was adapted for this grid planning task and fed with the positions of relevant power plants and substations of the Carinthian electricity transmission grid. This approach has significant potential for diverse applications, especially those where the algorithms are not limited by the physical or infrastructural constraints that shaped the original network topology. Further exploration of this approach could yield significant insights into various fields. Overall, the paper provides insights into the potential applications of bio-inspired algorithms, such as slime mold simulation, for solving network planning tasks and presents concrete case studies from the Carinthian region.
Assessing forest structure with LiDAR: A method benchmark in the Rohrach Natural Forest Reserve
The Rohrach Natural Forest Reserve, located in the northern foothills of the Alps, serves as a long-term reference area for preserving and studying natural forest development. In this study, three methods for assessing forest structure—classical field-based inventory, terrestrial laser scanning (TLS), and drone-based LiDAR (UAV-LS)—were systematically compared. Building on an initial survey conducted in 1996, 44 sample plots were revisited and supplemented with high-resolution 3D measurements. The results show that TLS provides highly accurate volume estimates that closely match those obtained through the classical inventory. The UAV-based approach enabled a comprehensive, area-wide survey of the 48-ha study site and yielded average growing stock values of 547 m³/ha—almost identical to those from the classical inventory (549 m³/ha). However, automated detection of lying deadwood using UAV data underestimated the volume by up to 50% compared to the line intersect method. Whether this discrepancy is due to the line intersect method’s assumption of randomly distributed logs being unsuitable for this site, or whether UAV-LS underestimates deadwood due to canopy shadowing or algorithmic omission of fine structures, requires further investigation. The study also highlights persistent challenges in tree segmentation within steep or densely vegetated areas: overlapping crowns often result in misclassifications or undetected stems. While TLS continues to offer the highest geometric accuracy, UAV-LS provides the advantage of rapid, large-scale data acquisition with minimal disturbance to sensitive environments. These findings underscore the importance of integrated methodological approaches for effective long-term monitoring in natural forest ecosystems.
Predictive maintenance in infrastructure: Utilizing 3D point clouds for efficient damage detection
Growing urbanization is driving the demand for infrastructure such as parking lots, roads, and bicycle lanes. While green spaces and trees are often integrated into these development projects to mitigate negative climate impacts, they can cause root-related damage that poses safety risks and requires costly monitoring. Public road networks are typically inspected with advanced but expensive surveillance vehicles that are too costly for private applications, leaving private infrastructure such as parking lots, private roads, and storage areas without comparable solutions. Thus, this paper presents a methodology for detecting and classifying damage areas in 3D point clouds of parking lots, distinguishing root-related damage from construction joints using a combination of deep learning and classical statistics. The approach is evaluated on data from Vienna International Airport and validated against manually labeled ground truth data. Results show that accurate localization and classification of damage is feasible using only a single laser scanner, providing a cost-effective alternative to conventional monitoring. Moreover, the method facilitates predictive maintenance by automatically detecting damage and enabling integration into Building Information Modeling software.
Assessment of biodiversity patterns based on iNaturalist observation data from Carinthia
This study examines the spatial and temporal patterns of citizen science contributions to biodiversity monitoring in Carinthia, Austria, utilizing iNaturalist research-grade observations collected from 2015 to 2022. It investigates potential data collection biases, such as time of day and season, as well as species phenology, including seasonal life cycles, which manifest in temporal patterns of data contributions. Additionally, the study explores how land cover and other variables influence observation counts across 5 × 5 km² grid cells, employing a negative binomial regression model with Eigenvector Spatial Filtering. The temporal analysis also analyzes seasonal shifts in the internationality of iNaturalist contributors in Carinthia. The results reveal significant effects of time of day, season, and land cover on observed species and biodiversity. Most taxonomic families were primarily recorded in forested and semi-natural areas during the summer months. Although artificial surfaces, such as urban fabric, contribute fewer observations in total, they exhibit a bias due to ease of access and longer observation hours during winter, aided by artificial lighting. The study also highlights that iNaturalist contributions in Carinthia during the summer months are predominantly from users who tend to contribute more frequently outside of Austria, suggesting that the summer period attracts more internationally active contributors, such as foreign tourists. This research expands on prior studies of biodiversity monitoring by integrating both local and global scales of contributor behavior.
“Die Pflanzenwelt der Insel Krk”: a richly illustrated field guide for vascular plants, algae, mosses, lichens and macrofungi on the island of Krk
Comprehensive overview of Germany’s biodiversity: Faktencheck Artenvielfalt
BioDivTech Lab – conceptualizing a new research infrastructure for biodiversity monitoring
With the advent of new technological capabilities for recording and monitoring biodiversity, the requirements for research infrastructure are changing. Currently, Carinthia University of Applied Sciences (CUAS) is developing testing facilities designed to enable synchronized biodiversity assessment at various scales in the medium term. This major investment is supported by the Austrian Research Promotion Agency, FFG, and is scheduled to become fully operational in the coming years. The facilities comprise several components, including the SKS research site at Metschacher Moos, stationary measurement and observation networks, a mobile biodiversity laboratory, and laboratory and computational resources at CUAS. These infrastructure components are utilized by researchers and students at CUAS and are also intended to be made available to collaborating scientific institutions and companies.
TREEgital – digital knowledge transfer about forests for schools
The COVID-19 pandemic has accelerated digitalization in education. The TREEgital project is taking advantage of this trend to use digital tools to impart forest knowledge in schools. It explores the forest as a living, recreational and economic space and emphasizes its role in protecting the climate and biodiversity. Key topics are biodiversity, climate change, and career guidance in green industries, all within the framework of Education for Sustainable Development (ESD). In addition, TREEgital teaches pupils digital skills. Digital educational products for schools have been developed, such as the augmented reality app “Öswald - App in den Wald!”, which improves learning by overlaying virtual elements with real environments, and YouTube career guidance videos focusing on STEM topics. In the “TreeCast” video podcast, forest experts discuss topics ranging from climate change to soil health. The social media series “Ask the Expert” provides insights into the professional world of forest research.
Comparative effectiveness of acoustic devices for monitoring bat species: a case study of Plecotus macrobullaris in Thomatal
The current study reports on the presence and activity of bats at the St. Georg Parish Church, Thomatal, Salzburg, Austria. This site was known in previous years to host a maternity roost of the alpine long-eared bat (Plecotus macrobullaris). Passive acoustic monitoring (PAM) and traditional roost emergence counting techniques were used to document bat activity. The study compared the effectiveness of three ultrasonic detection devices: Song Meter Mini Bat, Echo Meter Touch 2 Pro, and batcorder. Following expert verification, the study confirmed the presence and seasonal emergence phenology of P. macrobullaris and documented additional bat species including Eptesicus nilssonii and Myotis daubentonii. The results highlighted variations in the accuracy and species detection capabilities of the devices. While differences in device performance were clear, the findings emphasize the applicability of PAM for monitoring bat populations. Findings underscore the importance of proper device handling and appropriate data analytical techniques to ensure reliable species identification.
Deep-learning based population monitoring of the endangered plant species Gladiolus illyricus: lessons learned for implementation of a technology-based biodiversity monitoring approach
New technologies offer promising possibilities in biodiversity monitoring to increase standardization of sampling methods and improve cost efficiency. Among the former, uncrewed aerial systems (UAS) are widely used today to produce orthomosaics of a particular area. At the same time, computer-intensive methods for automated object detection within images have increased accordingly. While they are widely used in science, applied nature conservation makes little use of these methods. The current study aimed to test the applicability of UAS in combination with a deep-learning based object detection workflow in Schütt-Graschelitzen, a small-scale Natura 2000 protected area near Villach, Austria. For this purpose, we trained a YOLO_v8 algorithm with flowers of Gladiolus illyricus from an orthomosaic. The orthomosaic was split into about 1000 equally sized tiles with 80 tiles used for training and 20 tiles used for validation. For ground truthing, the individual inflorescences were counted manually. Our main findings indicated moderate model performance with the training and validation dataset and also with new data. Moderate – rather than strong – performance is likely a result of too little training data. While object detection worked considerably well, background revealed too high variability, making reliable classifications challenging. Comparing the different work steps (without UAS mission) suggests that creating a representative training dataset is the most time-intensive part of the workflow. For small areas and a single survey, this is likely not efficient compared to traditional field sampling methods. However, its efficiency increases with each resurvey event, as pretrained deep-learning models developed during prior monitoring cycles can be reused. This can reduce the amount of training data required in a subsequent survey. Additionally, UAS- and deep-learning based monitoring can help at sites with high sensitivity to trampling and favors large study areas, as its efficiency increases with the sample size area.
Review of the framework for designing biodiversity monitoring programs: the latest IUCN WCPA technical report series contribution
A comprehensive overview of Carinthia’s endangered fauna: review of the Red List of endangered animals of Carinthia
Critical update on Carinthia’s flora: review of the Red List of endangered ferns and flowering plants of Carinthia
Improvement of accuracy of different automatic visitor counting devices to monitor cyclists in conservation areas
Conservation area managers face increasing challenges in balancing nature-based tourism with environmental protection, as visitor activities diversify and tourism grows in recreational and protected areas. To address this, effective visitor monitoring is crucial for identifying spatial and temporal hotspots of use and potential conflicts that could threaten conservation goals. In this context, a field test on a mountain bike trail near Villach compared the functionality of two automatic visitor counting technologies: an infrared counter and a magnetometer. A wildlife camera recorded videos of passing mountain bikers, serving as the “ground-truth” for the experiment. The infrared counter, detecting infrared wavelengths emitted by people or animals, and the magnetometer, sensing metal parts of bicycles, were evaluated for accuracy. Over 28 days, the wildlife camera recorded 4,004 cyclists. The magnetometer undercounted by 9.6%, while the infrared counter undercounted by 32.8%, with increasing inaccuracy on busy days. A linear regression model provided correction factors, with the magnetometer showing higher prediction accuracy. The study suggests that the magnetometer is more reliable for counting cyclists, but both technologies suffer accuracy issues with higher visitor traffic. Wildlife cameras, while useful, require careful data management and privacy considerations. Future tests could focus on differentiating between hikers and cyclists using these technologies on shared trails.
Blending tradition with innovation: how acoustic sensors are revolutionizing rock ptarmigan monitoring
As part of the annual survey of the presence of the alpine ptarmigan in the Granatspitz group in the Hohe Tauern National Park, Carinthia University of Applied Sciences supported the data collection for the first time using acoustic sensors in the spring of 2023. To obtain an initial comparison with the classic point-counting method, acoustic loggers were placed at previously established permanent survey points. Furthermore, the devices were installed at the boundaries of the reference area and beyond to determine how extensively the area is used by rock ptarmigan. The results demonstrated that, despite challenging weather conditions, the required data could be successfully collected using the two tested sensor types.
GoPro forest: creating a digital forest twin
Terrestrial Structure from Motion photogrammetry images were used to create a digital 3D twin of several forest areas. The aim was to evaluate whether this method can calculate the existing biomass and stored carbon. Instead of an expensive laser scanning device, a GoPro camera was used. The project served to learn how to use the camera and software for digital data acquisition in forests. Using GoPro images, 3D twins were created and the forest stand was compared with conventional methods. An average value per square meter was calculated in four test areas and scaled up to one hectare. The results of the digital point clouds with Agisoft Metashape and ReCap Pro were compared with conventional images. Agisoft Metashape achieved an agreement of 82.5% and ReCap Pro 87.5%.
Advancing technologies: IPAM Toolbox 2.0 for planning and management of Conservation Areas (MCA)
The paper introduces an online tool for integrated management of conservation areas (CAs). The IPAM Toolbox 2.0 is an interactive web application that enables self-assessment of the planning or management status of a CA. The toolbox is built upon the framework of the lifecycle of CAs. Accordingly, the lifecycle can be represented in four phases, I Planning and designating a site, II Managing a site for the long term, III Repeal and termination, and IV Management beyond boundaries. The phases can be divided into 29 Fields of Activity (FoAs). This structure is supported by literature and empirical evidence. Currently, the toolbox is available at the technology readiness level of a demonstrator (TRL 7). It is used for research and educational purposes. In a further step, it will be made available to planners, managers, consultants, and decision-makers in CAs worldwide.
Kunming-Montreal Global Biodiversity Framework (GBF)
A digital framework for automated non-invasive waterfowl detection in Carinthia based on high resolution UAS imagery and machine learning
Automated waterfowl detection from uncrewed aerial system (UAS; “drones”) imagery has become an important task for various environmental applications such as wildlife monitoring, nature conservation, and habitat mapping. This paper presents a digital framework for automated waterfowl detection using high-resolution UAS imagery and artificial intelligence/machine learning (ML). Several UAS missions in Brenndorf, Carinthia, Austria, were conducted simultaneously with a traditional ground-based waterfowl field survey by an experienced expert. Several data pre-processing steps were applied to optimize digital image data pipelines for the generation of high-quality ML training data. The You Only Look Once (YOLO) open-source computer vision and ML object detection model was used to detect waterfowl in the UAS imagery. A transfer learning approach from a large waterfowl study at the University of New Mexico in collaboration with the U.S. Fish and Wildlife Service was used to further improve the model’s performance. Validation results showed promising performance with 80% and 83% classification accuracy on the waterfowl classes ‘duck’ and ‘swan’, respectively. Finally, a spatial projection model and a visualization approach for the ML-based detection and classification results on a map were implemented. The proposed digital framework for automated waterfowl detection provides promising results for standardization and a new paradigm for waterfowl counting to support and extend traditional wildlife monitoring.
Google4Habitat - a novel method for remote sensing-based habitat classification using Google Earth Engine
Global and accelerating loss of biodiversity requires stronger management and protection of ecological resources. In Europe, various habitat types frequently need to be monitored within the framework of the Natura 2000 program. To achieve this, a robust monitoring tool, generating precise habitat maps, is crucial. Because of the specific conditions in mountainous areas, such as steep slopes and hard-to-reach areas that impede large-scale field surveys, remote sensing approaches are increasingly used to generate reliable maps. The novel classification method Google4Habitat, developed in this study, combines globally available satellite data (Sentinel/Landsat) with a series of site characteristics and upstream expert rules. Within Google Earth Engine, habitats are classified via spatial and temporal analysis based on spectral profiles and combined with factors such as elevation, vegetation height, surface roughness (based on LiDAR (light detection and ranging) data), geology, and indices for vegetation greenness (NDVI, normalized difference vegetation index), snow cover (NDSI, normalized difference snow index), and water (NDWI, normalized difference water index) in a supervised classification approach. The following questions were addressed: 1) Do the results meet the stringent habitat classification guidelines of the Red List and the requirements of Natura 2000? 2) What impact do the different qualities of input data have on the accuracy of the results? 3) Is this method suitable for capturing long-term changes in habitat distribution? We tested our model in Seebachtal, an alpine region that includes all habitat types from the montane to the nival zone and is one of the most untouched valleys in the Hohe Tauern National Park. The results are promising both in terms of habitat classification and delineation, largely meeting with the Natura 2000 guidelines. Due to their lower spatial resolution, Landsat data cannot fully detect small-area habitat types such as fens and still water. However, a comparison with the higher-resolution Sentinel-2 data shows that, in consideration of the entire study area, the classification accuracy using Sentinel-2 data did not significantly improve. Changes in habitat distribution over a 30-year-period were captured reliably. Overall, our model allows the rapid classification of large areas with high accuracy, opening new avenues for practical environmental management.