In Austria, more than a half of all electricity is produced with the help of hydropower plants. To reduce their ecological impact, dams are being equipped with fish passages that support connectivity of habitats of riverine fish species, contributing to hydropower sustainability. The efficiency of fish passages is being constantly monitored and improved. Since the likelihood of fish passages to be discovered by fish depends, inter alia, on flow conditions near their entrances, these conditions have to be monitored as well. In this study, we employ large-scale particle image velocimetry (LSPIV) in seeded flow conditions to analyse images of the area near a fish passage entrance, captured with the help of a ready-to-fly consumer drone. We apply LSPIV to short image sequences and test different LSPIV interrogation area sizes and correlation methods. The study demonstrates that LSPIV based on ensemble correlation yields velocities that are in good agreement with the reference values regarding both magnitude and flow direction. Therefore, this non-intrusive methodology has a potential to be used for flow monitoring near fish passages on a regular basis, enabling timely reaction to undesired changes in flow conditions when possible.

Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of >85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management.

Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion maps, and the model complexity. The current Spatially-Explicit Uncertainty and Sensitivity Analysis (SEUSA) approach employs a cluster-based parallel and distributed Python–Dask solution for large-scale spatial problems, which validates and quantifies the robustness of spatial model solutions. This paper presents the design of a framework to perform SEUSA as a Service in a cloud-based environment scalable to very large raster datasets and applicable to various domains, such as landscape assessment, site selection, risk assessment, and land-use management. It incorporates an automated Kubernetes service for container virtualization, comprising a set of microservices to perform SEUSA as a Service. Implementing the proposed framework will contribute to a more robust assessment of spatial multi-criteria decision-making applications, facilitating a broader access to SEUSA by the research community and, consequently, leading to higher quality decision analysis.

Individual identification of animals is crucial for understanding their behavior,ecology, and conservation, yet traditional methods are often invasive,labor-intensive, and prone to observer bias. Recent advancements in artificialintelligence (AI), particularly deep learning (DL) and computer vision(CV), offer non-invasive solutions. Existing AI-based primate facial recognitionsystems (AI-PFRS) are often limited by reliance on manual annotationand a strong frontal-view bias. This study addresses these gaps by developingand validating a scalable AI-PFRS for Japanese macaques (Macaca fuscata).Research was conducted on 33 focal individuals housed in a semi-free, 4-hectare forest enclosure, providing a naturalistic social and environmentalcontext. We introduce a novel, mostly-automated annotation pipeline thatleverages an isolation filming protocol and strict video naming conventions totransform generic YOLO-based face detections into per-identity annotatedtraining data with minimal manual effort. Applied across four seasonal datacollection periods (Summer 2023 through Winter 2025), this approach generatedover 720,000 annotated frames encompassing diverse poses, lighting, anddistances, critically capturing the high diversity in the environment due toseasonal changes and temporal variability in morphological features during mating and non-mating seasons. Model robustness was enhanced througha content-aware, difficulty-stratified train/validation split. Performance significantlyimproved over time, with the correct identification rate increasingfrom 57.60% to 93.30%. While validation performance was strong, accuracyon a more challenging test set that more closely reflects day-to-day field conditionswas 82.1%, consistent with real-world deployment complexities. Thiswork presents an ecologically grounded framework for non-invasive primateidentification, with direct implications for long-term behavioral monitoringand Citizen Science initiatives, fostering more effective and ethical wildlifemanagement strategies.