Significant increase in natural disturbance impacts on European forests since 1950

Global Change Biology, Journal Year: 2022, Volume and Issue: 29(5), P. 1359 - 1376

Published: Dec. 12, 2022

Abstract Over the last decades, natural disturbance is increasingly putting pressure on European forests. Shifts in regimes may compromise forest functioning and continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central many policies, we lack long‐term empirical data needed for thoroughly understanding dynamics, modeling them, developing adaptive management strategies. Here, present a unique database >170,000 records ground‐based observations from 1950 2019. Reported confirm significant increase 34 countries, causing an average 43.8 million m 3 disturbed timber volume per year over 70‐year study period. This value likely conservative estimate due under‐reporting, especially small‐scale disturbances. We used machine learning techniques assessing magnitude unreported disturbances, which estimated be between 8.6 18.3 /year. In 20 years, disturbances accounted 16% mean annual harvest Europe. Wind was most important agent period (46% total damage), followed by fire (24%) bark beetles (17%). Bark beetle doubled its share damage years. Forest can profoundly impact (e.g., mitigation), affect regional resource consequently disrupt planning objectives markets. conclude that adaptation changing must placed at core policy debate. Furthermore, coherent homogeneous monitoring system urgently Europe, better observe respond ongoing changes regimes.

Language: Английский

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Early detection of bark beetle (Ips typographus) infestations by remote sensing – A critical review of recent research

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 556, P. 121595 - 121595

Published: Feb. 16, 2024

Bark beetle disturbances increasingly threaten structure and functionality of temperate boreal forests globally. The early detection bark beetle-infested trees, i.e. before beetles' emergence from the breeding tree, is essential for an effective outbreak mitigation. Terrestrial control surveys as traditionally employed infestation detection, however, are resource-intensive approach their limits in difficult terrain during mass outbreaks. Developments remote sensing algorithms giving hope that early-infested trees will be detectable remotely, thereby improving success management efficacy. Yet, a comprehensive quantitative evaluation approaches currently being developed lacking to date. This review synthesises state-of-the-art recent research on (or green-attack) by sensing, places it context with underlying biological constraints, technical opportunities potential applications. Since each beetle-host tree system has specific characteristics detectability, we focus greatest impact European forests, spruce (Ips typographus), which attacks Norway (Picea abies). By screening published within period 2000–2022, included 26 studies our analyses. All reviewed were purely exploratory, testing variety data and/or classification relatively limited spatial temporal coverage. Among tested platforms sensor types, satellite multispectral imagery most frequently investigated. Promising spectral wavelength range or index highly varied among regions. Timeliness accuracy found insufficient efficient management, regardless platform, type, resolution applied. main reasons preventing better performance include rapid development I. typographus combination delayed variable vitality response crown, frequent cloud cover spruce-dominated regions across Europe. In conclusion, current survey methods cannot yet replace terrestrial timely management. Nevertheless, they might supportive either back-up regular surveys, situations, e.g. detect hibernation accessibility, extensively managed without sufficient capacity. We suggest term 'early detection' used consistently synonym 'pre-emergence avoid ambiguity. Finally, provide recommendations future based lessons learned analysed, namely use more rigorous targeted study design, ensure interdisciplinarity, communicate results explicitly.

Language: Английский

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Impacts on and damage to European forests from the 2018–2022 heat and drought events

Natural hazards and earth system sciences, Journal Year: 2025, Volume and Issue: 25(1), P. 77 - 117

Published: Jan. 6, 2025

Abstract. Drought and heat events in Europe are becoming increasingly frequent due to human-induced climate change, impacting both human well-being ecosystem functioning. The intensity effects of these vary across the continent, making it crucial for decision-makers understand spatial variability drought impacts. Data on drought-related damage currently dispersed scientific publications, government reports, media outlets. This study consolidates data European forests from 2018 2022, using Europe-wide datasets including those related crown defoliation, insect damage, burnt forest areas, tree cover loss. data, covering 16 countries, were analysed four regions, northern, central, Alpine, southern, compared with a reference period 2010 2014. Findings reveal that all zones experienced reduced vitality elevated temperatures, varying severity. Central showed highest vulnerability, coniferous deciduous trees. southern zone, while affected by loss, demonstrated greater resilience, likely historical exposure. northern zone is experiencing emerging impacts less severely, possibly site-adapted boreal species, Alpine minimal impact, suggesting protective effect altitude. Key trends include (1) significant loss zones; (2) high levels despite 2021 being an average year, indicating lasting previous years; (3) notable challenges central Sweden bark beetle infestations; (4) no increase wildfire severity ongoing challenges. Based this assessment, we conclude (i) highly vulnerable heat, even resilient ecosystems at risk severe damage; (ii) tailored strategies essential mitigate change forests, incorporating regional differences resilience; (iii) effective management requires harmonised collection enhanced monitoring address future comprehensively.

Language: Английский

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A Sentinel-2 machine learning dataset for tree species classification in Germany

Earth system science data, Journal Year: 2025, Volume and Issue: 17(2), P. 351 - 367

Published: Feb. 3, 2025

Abstract. We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom-of-atmosphere reflectance. It is geared towards training classifiers but less suitable validating the resulting maps. The based on German National Forest Inventory 2012 as well analysis-ready imagery computed using Framework Operational Radiometric Correction Environmental monitoring (FORCE) processing pipeline. From data, we extracted positions, filtered 387 775 trees upper canopy layer, and automatically corresponding reflectance from L2A images. These are labeled with species, which allows pixel-wise tasks. Furthermore, provide auxiliary information such approximate position, year possible disturbance events, or diameter at breast height. Temporally, spans years July 2015 to end October 2022, approx. 75.3 million data points 48 3 groups 13.8 observations non-tree backgrounds. Spatially, it covers whole Germany. available following DOI (Freudenberg et al., 2024): https://doi.org/10.3220/DATA20240402122351-0.

Language: Английский

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Forest disturbances change psychological ownership among traditional private forest owners in North Rhine Westphalia

Forest Policy and Economics, Journal Year: 2025, Volume and Issue: 172, P. 103422 - 103422

Published: Jan. 10, 2025

Language: Английский

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Mapping Dominant Tree Species of German Forests

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(14), P. 3330 - 3330

Published: July 11, 2022

The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making site-adapted selection. An accurate assignment in relation to their location allows conclusions about potential resilience or vulnerability biotic abiotic factors. Identifying areas risk helps the long-term strategy conversion towards natural, diverse, climate-resilient forest. In framework inventory (NFI) Germany, data on are collected sample plots, but there is lack full coverage map distribution. NFI were used train test machine-learning approach that classifies dense Sentinel-2 time series with result dominant German forests seven main classes. model’s accuracy type classification showed weighted average F1-score deciduous (Beech, Oak, Larch, Other Broadleaf) between 0.77 0.91 non-deciduous (Spruce, Pine, Douglas fir) 0.85 0.94. Two additional plausibility checks independent stand inventories statistics from show conclusive agreement. results provided public via web-based interactive map, order initiate broad discussion limitations satellite-supported management.

Language: Английский

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UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

ISPRS Open Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 8, P. 100034 - 100034

Published: March 8, 2023

Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting and proven effective the assessment of abrupt large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing events (e.g., drought) are more difficult detect with satellite data – especially across regions forest types. A common limitation this availability spatially explicit reference data. To address issue, we propose an automated generation using uncrewed aerial vehicles (UAV) deep learning-based pattern recognition. In study, used convolutional neural networks (CNN) semantically segment crowns standing dead trees from 176 UAV-based very high-resolution (<4 cm) RGB-orthomosaics that acquired over six in Germany Finland between 2017 2021. The local-level CNN-predictions were then extrapolated landscape-level Sentinel-1 (i.e., backscatter interferometric coherence), Sentinel-2 time series, long short term memory (LSTM) predict cover fraction deadwood per Sentinel-pixel. CNN-based segmentation UAV imagery was accurate (F1-score = 0.85) consistent different study sites years. Best results LSTM-based extrapolation fractional -2 series achieved all available --2 bands, kernel normalized difference vegetation index (kNDVI), water (NDWI) (Pearson's r 0.66, total least squares regression slope 1.58). predictions showed high spatial detail transferable Our highlight effectiveness algorithms rapid large areas imagery. Potential improving presented upscaling approach found particularly ensuring temporal consistency two sources co-registration medium resolution data). increasing publicly on sharing platforms combined mapping will further increase potential multi-scale approaches.

Language: Английский

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TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing

Earth system science data, Journal Year: 2023, Volume and Issue: 15(2), P. 681 - 695

Published: Feb. 8, 2023

Abstract. Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated benefits of multiple sensors automated tree classification. However, transferable deep learning approaches applications still lacking. This gap motivated us to create a novel dataset classification in central Europe based multi-sensor from aerial, Sentinel-1 Sentinel-2 imagery. In this paper, we introduce TreeSatAI Benchmark Archive, which contains labels 20 European (i.e., 15 genera) derived administration federal state Lower Saxony, Germany. We propose models guidelines application latest machine techniques task with multi-label data. Finally, provide benchmark experiments showcasing information can be different including artificial neural networks tree-based methods. found that residual (ResNet) perform sufficiently well weighted precision scores up 79 % only by using RGB bands aerial result indicates spatial content present within 0.2 m resolution very informative With incorporation imagery, performance improved marginally. sole use allows 74 either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since real-world reference data, it high class imbalances. attribute negatively affects models' performances many underrepresented classes scarce species). class-wise best-performing late fusion model reached values ranging 54 (Acer) 88 (Pinus). Based our results, conclude imagery could considerably support forestry provision maps at plan challenges driven global environmental change. The original used paper shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation dataset, refer article.

Language: Английский

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Assessment of social vulnerability to forest fire and hazardous facilities in Germany

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 87, P. 103562 - 103562

Published: Jan. 26, 2023

In recent years, Germany has seen an increase in forest fires, and many fires have occurred military training areas that are difficult to access for firefighting. While casualties still low mostly restricted firefighting personnel, settlements also increasingly threatened. Increasing impacts from extreme events due climate change will likely the ignition spread of fires. More people being affected by need external help evacuate cope with resulting damages losses. Forest threaten site, additional risks created presence ammunition depots. Despite this hazard scenario, so far lacks overview spatial occurrence related risk. This study develops a exposure social vulnerability assessment Germany. The results reveal is important variable determining which potentially exposed fire. Areas fire risk characterised having higher proportion women, higher-than-average age, number young under 18 persons over 65 years foreigners than national average. Furthermore, communities rates unoccupied housing units lower living space per dwelling, as well high population densities within forested areas. can improve emergency management planning prevent development

Language: Английский

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Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: June 23, 2023

Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and size number affected areas. Controlling bark outbreaks requires consistent operational monitoring, is possible using satellite data. However, while many satellite-based approaches been developed, full potential dense, multi-sensor time series has yet to be fully explored. Here, for first time, we used all available multispectral data from Landsat Sentinel-2, Sentinel-1 SAR data, combinations thereof detect Bavarian Forest National Park. Based on multi-year reference dataset annual infested areas, assessed separability between healthy forests various vegetation indices calculated We two compute infestation probability different datasets: Bayesian conditional probabilities, based best-separating index each type, random regression, type. Five sensor configurations were tested their detection capabilities: alone, Sentinel-2 combined, types combined. The best overall results terms spatial accuracy achieved with (max. accuracy: 0.93). detections also closest onset estimated year. detected areas larger contiguous patches higher reliability compared smaller patches. somewhat inferior those 0.89). While yielding similar results, combination did not provide any advantages over or alone 0.87), was unable 0.62). combined three achieve satisfactory either 0.67). Spatial accuracies typically probabilities than forest-derived but latter resulted earlier detections. approach presented herein provides flexible pipeline well-suited monitoring outbreaks. Furthermore, it can applied other types.

Language: Английский

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