捷克生命科学大学

Monitoring biodiversity in agricultural landscapes is essential for addressing global biodiversity decline. Emerging remote sensing and artificial intelligence technologies enable large-scale, low-impact and cost-effective assessments. This chapter provides a concise review of remote sensing applications for farmland biodiversity monitoring, structured around the Essential Biodiversity Variable framework. We examine plants, animals, fungi and microorganisms, and their habitats in productive and semi-natural areas. Spaceborne and airborne sensors effectively assess agroecosystem structure and functioning, and detect plants and large mammals with distinct spectral or phenological traits. UAV imagery and camera traps offer higher resolution for monitoring smaller or cryptic species. Remote sensing also supports indirect methods, such as species distribution modeling, to infer the occurrence of species that are difficult to detect. Key challenges include technological, methodological and operational limitations. Integrating multi-platform data, establishing shared benchmarks, and broadening taxonomic coverage, supported by policy incentives and farmer-friendly technologies, are essential for scaling biodiversity monitoring and promoting sustainable strategies that reconcile conservation with agricultural production.

Bark beetle outbreaks pose a severe threat to spruce forests, with the European spruce bark beetle (Ips typographus L.) being the dominant pest in the Czech Republic. Although multispectral imagery using visible (VIS) and near-infrared (NIR) wavelengths has been employed for early detection, it primarily captures visible infestation symptoms rather than the underlying physiological stress, which can, however, be detected using thermal measurements, highlighting the benefit of integrating or complementing multispectral with thermal imagery. We compared a time series of Unmanned Aerial Vehicle (UAV) based thermal and multispectral imagery over a 650—ha coniferous stand in Central Bohemia, acquired at key phases of infestation (a) a year before infestation (August 2020); (b) closely before bark beetle infestation (April 2021); (c) in the initial phase of the green-attack (May 2021); and (d) in green-attack stage, early detection (June 2021), based on the species phenological model and field survey. Comparisons of canopy temperature and Normalised Difference Vegetation Index (NDVI) showed that thermal imagery successfully discriminated between healthy and infested trees seven weeks after beetle attack (green-attack), whereas NDVI differences remained negligible. These results confirm that UAV thermal imaging outperforms multispectral data for the early, individual-tree detection of bark beetle infestation.

The Przewalski´s horse (Extinct in the Wild in 1996) is currently listed as Endangered. The first individuals were reintroduced to the Great Gobi B Strictly Protected Area (GGBSPA) in Mongolia in 1997. We observed selected horse groups in the GGBSPA during two seasons in one year (2019) and used ecological niche models to (1) model habitat selection for feeding and resting with generalised linear mixed models; (2) identify the influence of origin (Wild-born vs. Reintroduced); and (3) discuss the potential influence of human presence on the habitat used by the horses for these behaviours. We used three types of satellite-derived predictors: (i) topography (ALOS); (ii) vegetation indexes (Landsat); and (iii) land cover (Copernicus). We assessed the spatial similarity between Reintroduced vs. Wild-born models using pairwise comparisons of the two response variables (feeding and resting). We found significant differences and an apparent shift between the areas preferred for feeding and resting by Wild-born and Reintroduced horses, both over the selected periods and between the groups of different origins. For Wild-born horses, the probability of feeding increased with increasing vegetation quality, while Reintroduced horses tended to feed in worse areas in Late Spring. Surprisingly, an opposite trend was observed in Autumn. On the contrary, no clear pattern emerged from the full model that combined both seasons. Our research provides valuable insights that can inform the refinement of conservation practices, offering practical approaches for enhanced management of the species. We propose that habitat suitability, anthropogenic pressure, and reintroduced group size should be considered key factors for the successful reintroduction of Przewalski´s horses. Additionally, our results caution against simple sample-size maximisation and show that stratified, well-defined datasets might prevent dilution of key drivers in habitat-use models for reintroduced ungulates.

Forest disturbances are increasingly understood as social-ecological phenomena involving diverse actors. Here, we focused on one of the Europe’s largest recent disturbances—the drought-triggered bark beetle outbreak in Central Europe (2017–2022). Based on a survey of 165 respondents, we examined perceived disturbance drivers, response actions, and the alignment between them as an indicator of a value-action gap that is often present in social-ecological and governance systems. Climatic and forest structure-related factors were identified as the dominant drivers, aligning with current scientific understanding, whereas socio-economic and management constraints were perceived as less influential. Reported responses reflected awareness of climate-change risks and involved workforce training, adaptive change in species composition, and water-retention measures. Cause–action alignment was observed in nearly half of respondents, particularly among managers and legal entities. We found a relatively sound understanding of disturbance causes and corresponding responses, likely shaped by recent experience with an unprecedented disturbance impact.