Carinthia II - Part 3

• Carinthia II – Part 1
• Carinthia II – Part 2
• Carinthia II – Part 3

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.

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.

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.

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.

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.

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.

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.

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.