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A multi-year study of ecosystem production and its relation to biophysical factors over a temperate peatland
Active afforestation of drained peatlands is not a viable option under the EU Nature Restoration Law
AbstractThe EU Nature Restoration Law (NRL) is critical for the restoration of degraded ecosystems and active afforestation of degraded peatlands has been suggested as a restoration measure under the NRL. Here, we discuss the current state of scientific evidence on the climate mitigation effects of peatlands under forestry. Afforestation of drained peatlands without restoring their hydrology does not fully restore ecosystem functions. Evidence on long-term climate benefits is lacking and it is unclear whether CO2 sequestration of forest on drained peatland can offset the carbon loss from the peat over the long-term. While afforestation may offer short-term gains in certain cases, it compromises the sustainability of peatland carbon storage. Thus, active afforestation of drained peatlands is not a viable option for climate mitigation under the EU Nature Restoration Law and might even impede future rewetting/restoration efforts. Instead, restoring hydrological conditions through rewetting is crucial for effective peatland restoration.
Automatyczna samojezdna platforma pomiarowa do pomiarów wymiany gazów szklarniowych pomiędzy podłożem a atmosferą oraz do pomiarów charakterystyk spektralnych powierzchni
Potencjał akumulacyjny dwutlenku węgla torfowisk mszarnych w kontekście zmian właściwości optycznych atmosfery i klimatu
Ericoid shrub encroachment shifts aboveground–belowground linkages in three peatlands across Europe and Western Siberia
AbstractIn northern peatlands, reduction of Sphagnum dominance in favour of vascular vegetation is likely to influence biogeochemical processes. Such vegetation changes occur as the water table lowers and temperatures rise. To test which of these factors has a significant influence on peatland vegetation, we conducted a 3‐year manipulative field experiment in Linje mire (northern Poland). We manipulated the peatland water table level (wet, intermediate and dry; on average the depth of the water table was 17.4, 21.2 and 25.3 cm respectively), and we used open‐top chambers (OTCs) to create warmer conditions (on average increase of 1.2°C in OTC plots compared to control plots). Peat drying through water table lowering at this local scale had a larger effect than OTC warming treatment per see on Sphagnum mosses and vascular plants. In particular, ericoid shrubs increased with a lower water table level, while Sphagnum decreased. Microclimatic measurements at the plot scale indicated that both water‐level and temperature, represented by heating degree days (HDDs), can have significant effects on the vegetation. In a large‐scale complementary vegetation gradient survey replicated in three peatlands positioned along a transitional oceanic–continental and temperate–boreal (subarctic) gradient (France–Poland–Western Siberia), an increase in ericoid shrubs was marked by an increase in phenols in peat pore water, resulting from higher phenol concentrations in vascular plant biomass. Our results suggest a shift in functioning from a mineral‐N‐driven to a fungi‐mediated organic‐N nutrient acquisition with shrub encroachment. Both ericoid shrub encroachment and higher mean annual temperature in the three sites triggered greater vascular plant biomass and consequently the dominance of decomposers (especially fungi), which led to a feeding community dominated by nematodes. This contributed to lower enzymatic multifunctionality. Our findings illustrate mechanisms by which plants influence ecosystem responses to climate change, through their effect on microbial trophic interactions.
Comparative Evaluation of CNN and Transformer Architectures for Flowering Phase Classification of Tilia cordata Mill. with Automated Image Quality Filtering
Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition.
Digital Repeat Photography Application for Flowering Stage Classification of Selected Woody Plants
Digital repeat photography is currently applied mainly in geophysical studies of ecosystems. However, its role as a tool that can be utilized in conventional phenology, tracking a plant’s seasonal developmental cycle, is growing. This study’s main goal was to develop an easy-to-reproduce, single-camera-based novel approach to determine the flowering phases of 12 woody plants of various deciduous species. Field observations served as binary class calibration datasets (flowering and non-flowering stages). All the image RGB parameters, designated for each plant separately, were used as plant features for the models’ parametrization. The training data were subjected to various transformations to achieve the best classifications using the weighted k-nearest neighbors algorithm. The developed models enabled the flowering classifications at the 0, 1, 2, 3, and 5 onset day shift (absolute values) for 2, 3, 3, 2, and 2 plants, respectively. For 9 plants, the presented method enabled the flowering duration estimation, which is a valuable yet rarely used parameter in conventional phenological studies. We found the presented method suitable for various plants, despite their petal color and flower size, until there is a considerable change in the crown color during the flowering stage.
Vertical Profiles of Aerosol Optical Properties (VIS/NIR) over Wetland Environment: POLIMOS-2018 Field Campaign
This study aims to present the benefits of unmanned aircraft systems (UAS) in atmospheric aerosol research, specifically to obtain information on the vertical variability of aerosol single-scattering properties in the lower troposphere. The results discussed in this paper were obtained during the Polish Radar and Lidar Mobile Observation System (POLIMOS) field campaign in 2018 at a wetland and rural site located in the Rzecin (Poland). UAS was equipped with miniaturised devices (low-cost aerosol optical counter, aethalometer AE-51, RS41 radiosonde) to measure aerosol properties (scattering and absorption coefficient) and air thermodynamic parameters. Typical UAS vertical profiles were conducted up to approximately 1000 m agl. During nighttime, UAS measurements show a very shallow inversion surface layer up to about 100–200 m agl, with significant enhancement of aerosol scattering and absorption coefficient. In this case, the Pearson correlation coefficient between aerosol single-scattering properties measured by ground-based equipment and UAS devices significantly decreases with altitude. In such conditions, aerosol properties at 200 m agl are independent of the ground-based observation. On the contrary, the ground observations are better correlated with UAS measurements at higher altitudes during daytime and under well-mixed conditions. During long-range transport of biomass burning from fire in North America, the aerosol absorption coefficient increases with altitude, probably due to entrainment of such particles into the PBL.