BG3.9 | Natural Disturbances in a Changing World: detecting, modelling and managing novel threats in forest ecosystems
EDI
Natural Disturbances in a Changing World: detecting, modelling and managing novel threats in forest ecosystems
Convener: Davide MarangonECSECS | Co-conveners: Giulia ManteroECSECS, Maximiliano Costa, Tommaso BaggioECSECS, Donato MorresiECSECS
Orals
| Fri, 19 Apr, 10:45–12:10 (CEST)
 
Room 2.17
Posters on site
| Attendance Fri, 19 Apr, 16:15–18:00 (CEST) | Display Fri, 19 Apr, 14:00–18:00
 
Hall X1
Orals |
Fri, 10:45
Fri, 16:15
Natural disturbances are one of the most important factors that shape the structure and composition of forests. Climate and land use changes are deeply altering forest disturbance regimes, potentially impacting ecosystems balance and structure, increasing hazard and risk for human health and threatening the provision of many ecosystem services (ES). Given the multitude of functions and services required from forests, it is crucial to understand the impact of recent natural disturbances on forests, especially with the alterations introduced by different global change drivers. Such modifications in the environmental conditions trigger new interactions among biotic, abiotic, and anthropogenic disturbances, which in turn can lead to increased severity and significant alterations of post-disturbance environment. Compound disturbances and cascading processes are increasing in frequency and raising their importance as limiting factors in the provision of forest ES. Therefore, higher attention is needed to analyze these phenomena. Despite the increasing awareness of the fundamental ecological role of natural disturbances, scientifically sound practices for increasing the resistance and resilience of forests and promoting natural regeneration are still lacking. A focus on post-disturbance management is needed to choose the more appropriate intervention, in terms of intensity and timing, that promotes effective forest recovery. Moreover, first place must be given to forest restoration and regeneration strategies, to reduce the loss of ES provisioning and re-establish the targeted forest function. This complex scenario requires solid scientific input, calling for multidisciplinary and multiscale analytical approaches. Remote sensing, in-field surveys, statistical and mechanistic models are some of the tools required for the detection, quantification, and management of forest disturbances and their effects on forest functions, ecosystem dynamics, and ecosystem services provisioning. In this session, we invite contributions from all the fields to promote knowledge and new methodologies to assess forest disturbances, from detection and mapping to the investigation of ecological processes and post-disturbance management, also through the use of numerical models. Particular attention will be paid to multiscale and multidisciplinary analysis dealing with the spatiotemporal characteristics of the processes and their interaction with climate, land use, and ES provisioning.

Orals: Fri, 19 Apr | Room 2.17

Chairpersons: Davide Marangon, Maximiliano Costa, Donato Morresi
10:45–10:50
10:50–11:00
|
EGU24-2019
|
ECS
|
Highlight
|
On-site presentation
Colin Bloom, Tiziana Koch, Katrin Meusburger, Daniel Scherrer, Lorenz Walthert, and Andri Baltensweiler

Over the past decade, extreme temperature and drought have resulted in widespread early leaf discoloration in European Beech (Fagus sylvatica) forests across central Europe. Discoloration during the particularly hot and dry summer of 2018 was ultimately associated with increased rates of crown dieback and tree mortality. Given the trend towards hotter and drier growing seasons under a changing climate, there is an increasing demand for site-specific recommendations on drought-resilient forest management practices in Switzerland. Making these recommendations requires a robust understanding of empirical forest disturbance and estimates of future forest health under a range of climatic and management conditions. To that end, using 2018 field observations, manual mapping of forest discoloration in aerial imagery, and multispectral Sentinel-2 imagery, we are developing 10 m/pixel estimates of European Beech discoloration across Switzerland during the 2018 to 2023 foliated periods. To date, we have 1) developed a robust interpolated Sentinel-2 time series from 2018 to 2023 for all of Switzerland, 2) trained a random forest model using 2018 ground control data and several vegetation indices from the Sentinel-2 time series to predict 2018 early leaf discoloration across Switzerland’s European Beech forests with c. 90% accuracy and, 3) used the Chlorophyll Red-Edge Index derived from the Sentinel-2 time series to approximate tree phenology and the length of the foliated period. We estimate that the 2018 foliated period was, on average, 45±19 days shorter for discolored sites as compared to sites without discoloration. Our results generally align well with previous studies of the 2018 drought in Switzerland and additional observational data is being compiled to validate the application of 2018 ground truth data across the foliated periods from 2018 to 2023. In combination with high-resolution soil maps, meteorological data, topographic derivatives, and information on Swiss forest structure, we will use empirical discoloration estimates to train ensemble models of site-specific susceptibility to drought. By artificially varying the meteorological and forest structure variables in these models we will have the unique opportunity to better understand European Beech susceptibility to drought and test the influence of a range of future climate scenarios and forest management strategies on Swiss forest health at a high spatial resolution.

How to cite: Bloom, C., Koch, T., Meusburger, K., Scherrer, D., Walthert, L., and Baltensweiler, A.: Towards high-resolution prediction of drought effects on Switzerland’s Beech forests for improved management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2019, https://doi.org/10.5194/egusphere-egu24-2019, 2024.

11:00–11:10
|
EGU24-7607
|
ECS
|
On-site presentation
Luca Mauri and Emanuele Lingua

Among the main issues affecting European forests, forest fires, windstorm and bark beetles outbreaks nowadays represent the most relevant one. In this regard, local stakeholders are  actually facing with critical circumstances both concerning the implementation of efficient silvicultural management of forest stands affected by such problematics. The storm Vaia occurred in 2018 in northeastern Italy created an unexpected scenario for Italian Alps. Following the windthrow produced by the storm, bark beetles proliferated from the downed logs, therefore moving to the neighbour standing forest and modifying the characteristics and the availability of forest fuel. In this context, the development of remote sensing techniques such as Light Detection and Ranging  (LiDAR) and Unmanned Aerial Vehicle (UAV)-based data acquisition, together with wildfire behaviour models, allow researchers to perform detailed estimation of forest fuels, necessary to simulate fire behaviour over disturbed forested areas over time. The prediction of key factors related to wildfire risk (e.g., fire type, rate of spread, flames lengths) is useful in estimating fire behaviour also in those areas affected by bark beetles proliferation. In this connection, new methods able to overcome notable limitations in forest fire simulations is nowadays needed. In particular, the interaction between bark beetles outbreaks and wildland fire dynamics were investigated focusing on a forested catchment (Veneto region, Italy) recently affected by the VAIA storm and hence involved in a widespread outbreak of bark beetle (Ips typographus). Extensive field data collection and the FlamMap fire behaviour model were coupled with high-resolution LiDAR and UAV-based analysis, to compare wildfire behaviour before and after beetle outbreak. Results could enrich the amount of information available for local administration of the Alpine region, in order to find effective interventions and management options for the areas affected by similar natural disturbances over time. 

How to cite: Mauri, L. and Lingua, E.: Modeling the interaction between wildfire behaviour and bark beetle outbreak from LiDAR data: new perspective for Italian forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7607, https://doi.org/10.5194/egusphere-egu24-7607, 2024.

11:10–11:20
|
EGU24-15123
|
On-site presentation
Samuli Junttila, Minna Blomqvist, Ville Laukkanen, Hannah O'Sullivan, Antti Polvivaara, Markus Holopainen, and Heli Peltola

Simultaneous increases of severe drought and heat extremes with bark beetle outbreaks have recently increased tree mortality globally. The lack of accurate tree mortality data over large areas has limited the development and applications of tree mortality models. Available tree mortality data has mainly been collected using field observations with limited spatial extent. However, the use of high-resolution remote sensing data, such as aerial imagery with automated imagery analysis, may change this situation. 

 

In this study, we analysed the development of tree mortality (standing dead wood) and factors contributing to it during 2017-2023 over an area of 117 366 ha of boreal forested landscape in Southern Finland. For this purpose, we developed a convolutional neural network (CNN) based on U-Net architecture, allowing segmenting of standing dead wood automatically from aerial imagery in 2017, 2020 and 2023 with a spatial resolution of 0.5 m. We trained the model using 22300 segments of manually delineated dead trees from various geographic regions in Finland. The model showed high accuracy in detecting the dead trees with an F1 score of 0.93 based on an independent validation dataset. We also combined the information on detected dead trees with open forest resource information based on extensive field campaigns and airborne laser scanning, to estimate standing dead wood volume during 2017-2023. 


The total standing dead wood volume increased substantially in our boreal study area, by 543 % from 8660 m3 to 52659 m3 between 2017 and 2023. Similarly, the total forest area of standing dead wood increased by 456 % between 2017 and 2023. Both variables followed an exponential growth curve with a nearly perfect fit for the 2017, 2020 and 2023 time series, indicating that tree mortality increased rapidly. Tree mortality occurred mainly in Norway spruce (Picea abies) dominated forests on relatively fertile soils. The mean age of forest stands suffering from tree mortality decreased from 69.7 to 62.6 years between 2017 and 2023, indicating that tree mortality has transitioned towards younger forests. Our findings highlight the increasing risk of tree mortality in boreal forests and the need for large-scale monitoring to keep up to date on the fast-paced changes in boreal forest mortality. This is also required for timely risk management measures in forestry under changing climate, associated with simultaneous increases of severe drought and heat extremes with bark beetle outbreaks.

How to cite: Junttila, S., Blomqvist, M., Laukkanen, V., O'Sullivan, H., Polvivaara, A., Holopainen, M., and Peltola, H.: Significant increase observed in tree mortality in boreal forests in Southern Finland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15123, https://doi.org/10.5194/egusphere-egu24-15123, 2024.

11:20–11:30
|
EGU24-20213
|
ECS
|
Highlight
|
On-site presentation
Yan Cheng, Stefan Oehmcke, Clemens Mosig, Beloiu Mirela, Teja Kattenborn, Christin Abel, Dimitri Gominski, Thomas Nord-Larsen, Rasmus Fensholt, and Stephanie Horion

Tree mortality has escalated worldwide in recent years due to climate warming and unprecedented drought events. However, mapping tree mortality across forest ecosystems has not yet been achieved. Aerial photos provide opportunities to reveal the spatial and spectral characteristics of canopy death at local to landscape scales. In this work, we present a deep learning model for mapping tree mortality from aerial photos in various forested ecosystems across Europe. This model builds on a baseline model trained with data on dead tree canopies from California using sub-meter resolution aerial photos and allows the use of various spatial resolutions of the input images (ranging from 10 to 60 cm). By comparing our results to ground observations and/or state-of-the-art forest disturbance and loss products, we will discuss the advantages and limitations of aerial photo-based tree mortality mapping. The proposed framework can be used for large-scale mapping of tree mortality from multi-year aerial photos. The tree mortality maps provide detailed information that can help understand the mechanisms of tree mortality under climate change. Furthermore, aerial photo-based maps can serve as training labels for mapping pixel-level deadwood fractions from satellite images, which enables seamless spatial coverage and could be an essential step towards a global map of tree mortality. 

How to cite: Cheng, Y., Oehmcke, S., Mosig, C., Mirela, B., Kattenborn, T., Abel, C., Gominski, D., Nord-Larsen, T., Fensholt, R., and Horion, S.: High-resolution mapping of tree mortality in European forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20213, https://doi.org/10.5194/egusphere-egu24-20213, 2024.

11:30–11:40
|
EGU24-5792
|
ECS
|
On-site presentation
Franziska Müller, Laura Eifler, Felix Cremer, Vitus Benson, Gustau Camps-Valls, and Ana Bastos

Global forest ecosystems face unprecedented challenges, such as fire, wind, drought, and insect outbreaks, resulting in rapid forest decline. Analyzing these disturbances on a large scale requires the use of remote sensing techniques, but the spatial and temporal uncertainty in forest disturbance reference data poses a significant obstacle.

In this study, we validate and refine existing disturbance labels of the U.S. Forest Service Forest Health Protection [1] Dataset USDA by using a change detection algorithm [2] based on radar data from Sentinel-1. To this end, we analyze the spatio-temporal overlap of disturbed areas from Sentinel-1 with the USDA labels and further explore spatio-temporal fingerprints of remote sensing indices commonly used for disturbance detection. As the analysis of the remote sensing indices shows, this refinement of the accuracy of disturbance labels provides a more reliable basis for ecological research and land management practice.

 

References:

[1] Coleman, T. W., Graves, A. D., Heath, Z., Flowers, R. W., Hanavan, R. P., Cluck, D. R., & Ryerson, D. (2018). Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States. Forest Ecology and Management, 430, 321–336. https://doi.org/10.1016/j.foreco.2018.08.020

[2] Cremer, F., Gans, F., Cortes, J. & Thiel, C. (2023). Mapping Forest Loss in Europe with Sentinel-1. In European Commission, Joint Research Centre, Soille, P., Lumnitz, S., Albani, S., Proceedings of the 2023 conference on Big Data from Space (BiDS’23) – From foresight to impact – 6-9 November 2023, Austrian Center, Vienna, Soille, P.(editor), Lumnitz, S.(editor), Albani, S.(editor), (pp. 361 - 364) Publications Office of the European Union, 2023, https://data.europa.eu/doi/10.2760/46796

 

How to cite: Müller, F., Eifler, L., Cremer, F., Benson, V., Camps-Valls, G., and Bastos, A.: Improving forest disturbance labels through Sentinel-1 change detection validation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5792, https://doi.org/10.5194/egusphere-egu24-5792, 2024.

11:40–11:50
|
EGU24-19533
|
ECS
|
On-site presentation
Basil Tufail, Emilio Dorigatti, Michele Claus, Alexander Jacob, and Peter James

With recent impacts due to the climate crisis, the number of extreme events has increased globally. Floods, droughts, windthrows, and landslides are affecting the environment around us, increasing the difficulty of mapping and monitoring its conditions.  Forests are particularly suffering from such events and the ones affected by repeated damage usually don’t have enough time to recover and become more vulnerable to other threats. The 2018 Vaia storm, the subsequent snow breaks, and the spread of bark beetles in the forests of Trentino-South Tyrol are prominent examples that caused large-scale disturbances in the region. In this research, we focus on the detection of forest disturbances caused by such extreme events. Approaches like Breaks for Additive Season and Trend (Bfast) have been implemented to detect breaks and vegetation response patterns [1] whereas Continuous Change detection and Classification (CCDC) also performs well with long-term optical time series data like Landsat and Sentinel-2 to monitor vegetation phenology [2]. However, only a few studies have focused on advances in Synthetic Aperture Radar (SAR) data for detecting changes in radar datasets [3]. SAR data can provide timely information on disturbances in areas where frequent cloud cover makes it impossible to map the changes with optical data for long periods. Thus, the ability to acquire imagery regardless of clouds and severe weather conditions makes SAR data a viable solution to map such disturbances, though this still requires further testing. This study aims at benchmarking methodologies applied using open-source software's to create change detection maps with freely available data including both optical and SAR. Using various, already established change detection methods implemented in FAIR (Findability, Accessibility, Interoperability, and Reusability) manner to evaluate the added benefit of fusing data from different kinds of sensors. 

[1] Watts, Laura M., and Shawn W. Laffan. "Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region." Remote Sensing of Environment 154 (2014): 234-245. 

[2] Zhou, Qiang, Jennifer Rover, Jesslyn Brown, Bruce Worstell, Danny Howard, Zhuoting Wu, Alisa L. Gallant, Bradley Rundquist, and Morgen Burke. "Monitoring landscape dynamics in central us grasslands with harmonized Landsat-8 and Sentinel-2 time series data." Remote Sensing 11, no. 3 (2019): 328. 

[3] Hirschmugl, Manuela, Janik Deutscher, Carina Sobe, Alexandre Bouvet, Stéphane Mermoz, and Mathias Schardt. "Use of SAR and optical time series for tropical forest disturbance mapping." Remote Sensing 12, no. 4 (2020): 727. 

How to cite: Tufail, B., Dorigatti, E., Claus, M., Jacob, A., and James, P.: Detection of Forest disturbances using multi source Remote sensing data. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19533, https://doi.org/10.5194/egusphere-egu24-19533, 2024.

11:50–12:00
|
EGU24-15638
|
On-site presentation
Emanuele Lingua, Paul Richter, Lorenzo Faes, Frédéric Berger, Matteo Garbarino, and Raffaella Marzano

Climate change is undeniably responsible for the increase in climate-related disturbances affecting Alpine communities. These phenomena are often the result of compound events, a combination of multiple climate-related hazards that contribute to socio-ecological risks. Among the key drivers of the increased vulnerability of Alpine communities are changes in forest cover, structure and species composition. Forests provide essential ecosystem services that support human well-being and play a crucial role in the mitigation of climate change. However, their health and stability are also particularly affected by large and high-severity climate-related disturbances. Therefore, in the framework of MOSAIC, an Alpine Space Interreg project funded by the European Union, a focus is being devoted to hazard-resilient and sustainable protective forest management, which is essential for managing climate-related hazards.  In order to support Alpine climate action plans, the project partners aim to collect, harmonise and share data on Alpine climate-related disturbances and hazards, as well as forecasting their future trends. MOSAIC strives to raise awareness among foresters, risk managers, decision makers and the public through an Alpine network of forest living labs. Here, some case studies and examples of disturbance interactions and their effects on protective forest stands are presented and discussed, with a special focus on the aftermath of VAIA, a windstorm that affected Northeastern Italy in 2018.

How to cite: Lingua, E., Richter, P., Faes, L., Berger, F., Garbarino, M., and Marzano, R.: Natural disturbances and protective forests: the effects of cascading and compound hazards after the storm VAIA  (Northeastern Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15638, https://doi.org/10.5194/egusphere-egu24-15638, 2024.

12:00–12:10
|
EGU24-13697
|
ECS
|
On-site presentation
Melissa Birch, Sarah Zwiep, Nicholas C. Coops, Andy Dean, Marcos Kavlin, and Frank Martin Seifert

Forests globally are experiencing unprecedented levels of disturbances, negatively impacting ecosystem functioning and services. Ecosystem restoration (ER) is a global priority to counteract and reverse the effects of disturbances, highlighted by initiatives such as the UN Decade for ER and the Convention on Biological Diversity 30x30 target.   

With increased investments in ER, more effective monitoring is required. Conventionally, ER monitoring relies on field surveys which are costly and infeasible for large or remote restoration sites. Recent advances in remote sensing technologies are seeing this technology increasingly being used to evaluate impacts of natural disturbances on forest ecosystems. Previous research has demonstrated strong correlations between remotely sensed spectral data and the recovery of forest ecosystems post-disturbance. These remote sensing recovery monitoring methods have relied on pre-disturbance status to assess recovery progress. However, increasingly multidisciplinary initiatives and ER management in practice require more flexibility in defining recovery targets. Additionally, ER practitioners face barriers to use remote sensing technology due to computational demands and complexity of time series analysis. 

To address these issues, the Pioneer Earth Observation apPlications for the Environment (PEOPLE) ER project, funded by the European Space Agency, developed spectral-recovery, an open-source, flexible, remote sensing tool to support monitoring of vegetation recovery in forested ecosystems. Written in the open-source Python programming language, the spectral-recovery package provides simple computational methods for analyzing Sentinel-2 or Landsat satellite data time series, with straightforward interfaces that allow users to select from a variety of spectral indices and recovery metrics to monitor recovery trends and trajectories over time. To facilitate the integration of the tool with existing ER practices, users have the flexibility to determine recovery targets using either a historic method, based on the restoration site's historical conditions, or a reference method, which uses reference sites for target conditions. The tool produces raster layers for each index and recovery metric, along with recovery trajectory graphs for each restoration site. This allows for flexible post-tool analysis and mapping visualizations. In this presentation, the potential of this tool is demonstrated via case studies in Canada and Europe of detecting and quantifying forest recovery from wildfire verified by using airborne laser scanning (ALS) data. Results in the Canada case study found that 84% of the tool's estimated recovered area also had met structural recovery targets of height and/or cover, supporting the use of the spectral-recovery tool to monitor, quantify, and map post-disturbance forest recovery at multiple scales. The tool’s ability to provide wall-to-wall recovery estimates over entire restoration sites or landscapes enables the comparison of various restoration activities over time and space through continuous monitoring and consistent metrics, addressing the most prevalent limitations of current ER monitoring efforts.  

The spectral-recovery tool is openly available via Github with demonstration notebooks and documentation, and is presented as an important tool for monitoring forest recovery, and assisting European and other countries in monitoring commitments under international agreements, EU policies, and at national level. 

How to cite: Birch, M., Zwiep, S., Coops, N. C., Dean, A., Kavlin, M., and Seifert, F. M.: Monitoring forest disturbance recovery using metrics derived from multi-spectral satellite time-series: introducing the spectral recovery open-source package with European and Canadian use cases, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13697, https://doi.org/10.5194/egusphere-egu24-13697, 2024.

Posters on site: Fri, 19 Apr, 16:15–18:00 | Hall X1

Display time: Fri, 19 Apr 14:00–Fri, 19 Apr 18:00
Chairpersons: Tommaso Baggio, Giulia Mantero
X1.33
|
EGU24-2927
|
ECS
|
Highlight
Temesgen Alemayehu Abera, Eduardo Maeda, Janne Heiskanen, Mohammed Muhammed, Netra Bhandari, Binyam Hailu, Petri Pellikka, Thomas Nauss, and Dirk Zeuss

Tropical montane cloud forest ecosystems contain some of the world's biodiversity hotspots and provide essential ecosystem services, including high quality freshwater and microclimate buffering against climate extremes. The microclimate buffering service provides microrefugia that allow species to persist under climate change, while the ability of montane forests to intercept cloud water from the atmosphere maintains freshwater availability. However, with increasing pressure from anthropogenic land use and climate change, the stability of montane forests to provide such ecosystem services remains unresolved. In this study, we investigated the impacts of deforestation and climate change on the cloud water interception capacity of montane forests in Africa over the last two decades. We predicted 2-m air and dew point temperature using satellite and in situ observations and ensemble machine learning. In addition, we estimated local warming due to deforestation and tested the 'lifting cloud base height' hypothesis attributed to deforestation and climate change separately. Our preliminary results show that 18% of montane forests in Africa were lost between 2003 and 2022, and deforestation increased the air temperature and lifted the cloud base height stronger than climate change. Deforestation weakened the cloud water interception capacity of montane forests in Africa. Overall, this study sheds light on the impacts of montane deforestation on local climate and water supply, which may have far-reaching implications for montane forest biodiversity and ecosystem service provision in Africa. 

This work is part of the MOFESD (montane forest ecosystem service dynamics in Africa) postdoctoral project funded by Alexander von Humboldt Foundation.

How to cite: Abera, T. A., Maeda, E., Heiskanen, J., Muhammed, M., Bhandari, N., Hailu, B., Pellikka, P., Nauss, T., and Zeuss, D.: Impacts of land use and climate change on montane forest ecosystem services in Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2927, https://doi.org/10.5194/egusphere-egu24-2927, 2024.

X1.34
|
EGU24-8180
|
ECS
Li Ma, Liping Yang, Qinqin Chang, Siqing Wang, Chao Guan, Ning Chen, and Changming Zhao

Dryland ecosystems are complex systems that can exhibit alternative tree-cover states, making conservation and restoration efforts challenging. However, our understanding of these states is still limited, particularly at the global level. In this study, we utilized remote sensing data to analyze the distribution of tree cover in drylands and assess the impacts of factors such as the aridity index, temperature, fire frequency, and grazing on tree cover at both the global and continental scales. The results showed that dryland ecosystems in Asia, Australia, and South America exhibited alternative tree-cover states, while dryland ecosystems at the global scale and in Africa, Europe, and North America did not. Livestock density and the aridity index appeared to be the primary drivers of these states in the regions where they occurred. This study highlights the importance of considering the variability in dryland vegetation states across different scales and regions, as small-scale processes may not always accurately predict large-scale dynamics. By examining dryland woody vegetation at both global and continental scales, our work contributes to a more comprehensive understanding of the factors that affect the tree-cover states in these ecosystems.

How to cite: Ma, L., Yang, L., Chang, Q., Wang, S., Guan, C., Chen, N., and Zhao, C.: Alternative tree-cover states of dryland ecosystems: Inconsistencies between global and continental scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8180, https://doi.org/10.5194/egusphere-egu24-8180, 2024.

X1.35
|
EGU24-11577
Liliana Scapucci, Ankit Shekhar, Sergio Aranda-Barranco, Anastasiia Bolshakova, Lukas Hörtnagl, Mana Gharun, and Nina Buchmann

Climate change is increasing frequency and intensity of droughts across Europe, with major consequences for forest ecosystems. Often soil and atmospheric droughts occur simultaneously, resulting in combined soil atmospheric drought (CSAD) events. Which effects such CSAD events have on forest CO2 fluxes is not clear. At the Lägeren site (CH-Lae), a mixed deciduous forest in Switzerland, we identified the three years with the lowest cumulative precipitation and the highest cumulative vapor pressure deficit (VPD) during the growing season (May – September), namely 2015, 2018 and 2022, since net ecosystem CO2 exchange (NEE) measurements started in 2005. We then determined the CSAD events, i.e., periods in which soil and atmospheric drought occurred simultaneously. Our objectives were to (1) quantify the impacts of CSAD events in 2015, 2018 and 2022 on CO2 fluxes against the long-term mean, (2) identify the environmental drivers of net ecosystem production (NEP) in 2015, 2018 and 2022 and forest floor respiration (Rff) in 2018 and 2022 compared to the long-term fluxes, (3) assess the temporal course of the effects of soil and atmospheric drought on NEP and Rff during the CSAD events against the long-term means. CO2 fluxes were measured continuously with the eddy covariance technique at two distinct locations at the CH-Lae forest: above the canopy at a height of 47 meters (from 2005 to 2022) and below the canopy at 1.5 meters (from 2018 to 2022). The drivers of NEP and Rff were determined with machine learning approaches, i.e., random forest conditional variable importance and Shapley Additive exPlenations (SHAP). We found a decrease in NEP of 35%, 38% and 41% during the CSAD events in 2015, 2018 and 2022 respectively compared to the mean 2005-2022, and a decrease of 16% and 41% in the Rff during the CSAD events in 2018 and 2022 compared to the mean 2019-2021. Light is usually the main driver of NEP during the growing season, as we found in 2015, 2018 and in the mean 2005-2022. While soil water content (SWC) was the main driver of NEP for the growing season in 2022, enhancing the key effect of soil drought in the 2022 growing season. The SHAP analysis revealed the negative impacts of high temperature, high VPD, and low SWC on NEP during all CSAD events, with low SWC and high VPD in 2022 having the larger impacts on NEP. Rff was mainly decreased by low SWC in 2018 and 2022. This led to a decrease in temperature sensitivity of Rff during CSAD events compared to the mean 2019-2021. With this study we assessed the impacts CSAD events on the CO2 fluxes of a mixed deciduous forest. Yet, the intensity, the timing, and the pre-conditions of CSAD events are crucial to explain ecosystem responses to such events. Furthermore, the increase in frequency and intensity of droughts and precipitation events with global warming call into question the predictability of forests capacity to store carbon, which is crucial for climate change mitigation through nature-based solutions.

How to cite: Scapucci, L., Shekhar, A., Aranda-Barranco, S., Bolshakova, A., Hörtnagl, L., Gharun, M., and Buchmann, N.: Drivers and impacts of combined soil and atmospheric droughts on the CO2 fluxes of a mixed deciduous forest in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11577, https://doi.org/10.5194/egusphere-egu24-11577, 2024.

X1.36
|
EGU24-12739
|
ECS
Maximiliano Costa and Harald Bugmann

The Horizon Europe project “eco2adapt” aims to provide support to stakeholders, forest owners and practitioners for managing forests and optimise social and environmental resilience using the concept of nature-based solutions. The project relies on a network of study cases (“Living Labs”), for which distinct management objectives are set. The overarching research question is to identify suitable management strategies to face climate change and novel disturbance regimes. In all Living Labs, the model LandClim will be used to evaluate different management approaches and their outcomes. LandClim is a spatially explicit, dynamic vegetation model that simulates the interactions between climate, natural disturbances and forest management at the landscape scale.

The Swiss Living Lab is located in the Surselva valley, in the canton of Graubünden. It represents the Alpine environment, featuring a wide range of elevations and thus biogeographical zones. Particular attention is dedicated to the protective function of forests against natural hazards (e.g., rockfall and avalanches), since it is the most particular feature of this Living Lab. Sustainable timber production is a key ecosystem service that is investigated as well, especially in the different climate change scenarios. The ultimate goal for the Swiss Living Lab is to identify how different management practices affect the sustainable provision of timber and the protective function of forests in future climate scenarios.

To achieve this goal, we are working with different climate change scenarios (RCP 4.5 and RCP 8.5) as well as with a no management scenario, in order to evaluate the potential natural vegetation of the study area, and with multiple management scenarios that are co-created with local stakeholders. The protective efficiency of future stands is evaluated using specific indices for rockfall protection and avalanche mitigation. Simulation results are discussed with stakeholders in order to determine the management scenario to use for the final outputs. The final simulation results are made available to stakeholders so as to support the development of management guidelines and best practices.

How to cite: Costa, M. and Bugmann, H.: Implications of the interactions between climate change, natural disturbances, and management for forest dynamics in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12739, https://doi.org/10.5194/egusphere-egu24-12739, 2024.

X1.37
|
EGU24-15141
|
ECS
Paul Richter and Emanuele Lingua

In mountainous regions, protective forests are crucial for maintaining ecosystem stability. The impact of natural disturbances on these forests and their ability to provide essential ecosystem services is evident. Therefore, evaluating the remaining protection offered by biological legacies and the dynamics of forest recovery becomes highly relevant in the face of climate change.

This research adopts a multiscale methodology, ranging from individual trees to landscape analysis, employing diverse techniques and data sources such as field studies, lidar, satellite imagery, and UAV data. The primary objective of this study is to enhance comprehension regarding the impact, capabilities, and real-time service life of natural disturbance legacies within protective forests, particularly in mitigating rockfall risks. Additionally, the research aims to contribute to a more profound understanding for a more ecologically sound and effective post-disturbance forest management approach. The study zones are located in the North-East of Italy and include areas impacted by windthrow as well as forest fire sites.

Between five to ten years post-event, ongoing field assessments aim to comprehensively evaluate the degradation status of existing deadwood. This analysis takes into account specific conditions, including altitude, tree species, and soil characteristics. This comprehensive analysis involves the deployment of sensors for prolonged monitoring of moisture levels, water content in logs, climate data collection, and sampling for dry-matter content and decay assessment of deadwood. The ultimate objective of this research is to enhance scientific insights into decay conditions, contributing to a substantiated, application-oriented understanding of the "service lifetime" of biological legacies following a disturbance event in protective forests, particularly in their role against rockfall.

How to cite: Richter, P. and Lingua, E.: Biological legacies as nature-based solutions to maintain protective effects in alpine mountain forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15141, https://doi.org/10.5194/egusphere-egu24-15141, 2024.

X1.38
|
EGU24-15147
|
ECS
Xiaoxue Dong, Xinyu Liu, Xiao He, Ning Chen, and Changming Zhao

The canopy height is pivotal in sustaining carbon cycling and upholding the ecological and biodiversity functions, which is also true or particularly the case for dryland ecosystems. Meanwhile, restorations in drylands have seen massive short-forests because of various reasons like insufficient water input. This study is committed to an exhaustive exploration of the biogeographic distribution and pivotal unknown determinants of short-forests in global drylands, offering indispensable insights for devising conservation strategies tailored to dryland forest vegetation. Here we divide satellite global dryland forests into the Tall-forests (17.30 ± 3.00 m) and the Short-forests (9.24 ± 2.57 m). The short-forests are ubiquitously distributed in global drylands, consist of 8.17% planted forests and 91.83% natural forests. These short-forests predominantly occur in ecosystems characterized by substantial climatic (temperature, precipitation) variations, impoverished soil textures, frequent human activities, and moderate to high elevations. Notably, within the short-forests, limitations on the canopy height of the natural forests appear to reach threshold values in specific factors earlier than in the planted forests such as Mean temperature of warmest quarter (11.73 ℃ vs. 12.64 ℃). Our discoveries explicate a nuanced comprehension of dryland short-forests, potentially serving as a crucial guide for informing future ecological restoration and sustainable management practices therein.

How to cite: Dong, X., Liu, X., He, X., Chen, N., and Zhao, C.: Navigating the Biogeography of Short-forests in Global Drylands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15147, https://doi.org/10.5194/egusphere-egu24-15147, 2024.

X1.39
|
EGU24-15598
|
ECS
Antti Polvivaara, Ilkka Korpela, Samuli Junttila, and Mikko Vastaranta

Tree mortality impacts biodiversity, carbon dynamics and the management of forests. Climate change is expected to increase tree mortality, but understanding of tree mortality rates and the underlying processes is limited; thus, more accurate and efficient tree mortality mapping methods are required. In this study, we investigated the feasibility of using airborne light detection and ranging (LiDAR) waveform (WF) features in detecting dead trees to monitor tree mortality and studied how the WF features of dead trees change over time.

We used three consecutive LiDAR campaigns using fixed sensor and flight parameters in a boreal forest in Southern Finland (61.5°N, 24.2°E). The campaigns spanned four years and were carried out in 2011, 2013 and 2015. A Riegl LMS-Q680i LiDAR sensor, which operates at 1550 nm wavelength, provided return WF data to study the geometric-optical properties of living and dead trees and monitor mortality of Norway spruce (Picea abies H. Karst.).

Our findings highlight the differences in radiometric and geometric WF features between living and dead trees. The return WFs from dead trees were consistently elongated and contained more backscattering energy. We also found that as a tree died, the canopy and branch structures became less dense and more irregular, leading to more complex return WFs. The WF features were used for binary classification of living and dead trees, resulting in classification accuracies between 94.7 and 98.5 %, depending on the campaign. Distinguishing between living and dead trees is challenging for trees that have died recently when there are only minor defects in the crown and discoloration of foliage. Tree decay after death improved the discernability between living and dead trees as the geometric-optical properties of the crown change. There is, however, a limit after which further stages of decay might impair the discernability of living and dead trees.

The radiometric and geometric WF features and canopy mortality effects on the WF features are consistent across datasets implying intrinsic quality of information in the WF features. The within-class variance of WF features in dead trees is greater than that in living trees, indicating significant variations in the geometric and radiometric properties of trees between stages of decaying and dying. Our results imply that LiDAR WFs can be used for the accurate detection of dead trees to map tree mortality.

How to cite: Polvivaara, A., Korpela, I., Junttila, S., and Vastaranta, M.: Detecting tree mortality using waveform features of airborne LiDAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15598, https://doi.org/10.5194/egusphere-egu24-15598, 2024.

X1.40
|
EGU24-16902
|
ECS
Wen Gao and Markku Larjavaara

Forests are susceptible to sudden disturbances, particularly those induced by wind, which can cause ecological and economic losses. Researchers have published a large number of articles from different disciplines and perspectives, leading to multidisciplinary intersections and increased literature in this field, which reduces the efficiency of traditional literature review. Employing quantitative techniques, bibliometric analysis has a great advantage in analyzing large amounts of literature and providing a visualized overview of the development and trends of the research field. This study conducted a comprehensive bibliometric analysis to elucidate the evolving landscape of research on forest wind disturbance. The methodology involved a systematic data collection process from the Web of Science Core Collection, resulting in the identification of 839 relevant publications for bibliometric analysis. The results show that there has been a consistent and steady growth in publications, with a distinct spike corresponding to Hurricane Hugo. Publications have mostly come from the United States, which also possesses the leadership in international collaboration. The standout topics include windthrow, tree motion during windstorms, European forests, wind damage risk estimation, hurricanes' impact on forests, and the long-term impacts of wind disturbances. Furthermore, four prospective directions for future research are identified, including studies into hurricanes, forest structure, climate change, and typhoon-related impacts. Our research collectively contributes to a comprehensive understanding of the dynamic landscape of forest wind disturbance research, providing a foundation for future research and strategic planning in this critical field.

How to cite: Gao, W. and Larjavaara, M.: Mapping the Landscape of Research on Wind Disturbance in Forests: A Comprehensive Bibliometric Analysis and Systematic Review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16902, https://doi.org/10.5194/egusphere-egu24-16902, 2024.

X1.41
|
EGU24-17328
|
ECS
Giulia Mantero, Nadav Mouallem, Larissa Yocom, Matteo Garbarino, and Raffaella Marzano

The wildfire regime of dry conifer forests dominated or co-dominated by ponderosa pine (Pinus ponderosa Douglas ex C. Lawson) in the Southwestern United States has been increasingly altered in the last decades. These changes, caused by the ongoing climate change and by the fuel build-up due to several decades of fire exclusion, resulting in a denser and more homogeneous forest structure, are leading to uncharacteristically large and severe wildfires, uncommon for these stands, adapted to fire regimes characterized by frequent low-severity surface fires. Large openings resulting from high-severity fires, together with post-fire drought and competition from herbaceous and shrubby vegetation, may hinder ponderosa pine regeneration, leading to shifts in forest composition or transition towards shrubland or grassland. The lack of pine regeneration caused by the higher severity of a first fire event can be reinforced by subsequent reburning, whose frequency is also increasing in the area. This study investigates how an altered fire regime can interrupt successional pathways in ponderosa pine stands in the Southwestern United States, focusing on the ecological and management implications associated with repeated fire occurrences after an initial high-severity wildfire. We analyzed the spatial and temporal patterns of post-fire regeneration after reburns through a combination of field surveys, remote sensing, and historical fire records, considering fire severity, topography, distance to seed trees, and time between the reburn events. The study aims to enhance our understanding of post-fire regeneration dynamics in the context of an altered fire regime and the ecological consequences and management strategies associated with this phenomenon.

How to cite: Mantero, G., Mouallem, N., Yocom, L., Garbarino, M., and Marzano, R.: Fire regime alteration and regeneration dynamics in Pinus ponderosa stands of the Southwestern United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17328, https://doi.org/10.5194/egusphere-egu24-17328, 2024.

X1.42
|
EGU24-17652
|
ECS
Donato Morresi, Matteo Domanico, Raffaella Marzano, and Matteo Garbarino

Alpine forests have been shaped by human and natural disturbances for millennia, resulting in highly heterogeneous and fragmented landscapes. Interactions between land use change and natural disturbances have been observed in recent decades. In addition, climate change is altering the disturbance regime in many forest ecosystems, including those in the Alps, by increasing the severity, extent and frequency of natural disturbance events. In this study, we aimed to attribute the disturbance agent to forest patches that experienced stand-replacing and non-stand-replacing events during the last four decades in the European Alps. In particular, we considered both natural disturbance agents, i.e., wind, fire, snow, insects, ice, and drought, and human activities. The latter included both primary and secondary disturbances, i.e., salvage logging following a natural disturbance. We trained an eXtreme Gradient Boosting (XGBoost) machine learning model using disturbed forest patches detected annually by an automated algorithm based on Landsat time series from 1984 to 2022. We obtained information on the disturbance agent using both historical field data from several European countries and visual interpretation of remote sensing data, e.g., Landsat imagery, aerial orthophotos, and high-resolution satellite imagery. We built the final classification model after selecting predictor variables from several disturbance, topography, patch, and climate-related metrics. Preliminary results showed that the model had good predictive performance, as highlighted by the accuracy metrics obtained from a fivefold cross-validation approach, i.e., Cohen’s Kappa equal to 0.81 and balanced accuracy of 0.82. The elevation range, pre-disturbance spectral values, the climate moisture index, and the range of the spectral change magnitude of each disturbed forest patch were among the most important variables. In particular, the elevation range emerged as a key predictor for discriminating between natural and anthropogenic disturbances. Similarly, pre-disturbance spectral values were important for distinguishing between certain natural disturbances, such as windthrows and snow avalanches. Spatially explicit results from this study are expected to allow a thorough characterisation of the changes in disturbance regimes in the European Alps that have occurred over the last four decades, and to provide useful information on the main drivers that have determined these recent shifts.

How to cite: Morresi, D., Domanico, M., Marzano, R., and Garbarino, M.: Attribution of forest disturbance agents in the European Alps: a multidecadal analysis based on Landsat time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17652, https://doi.org/10.5194/egusphere-egu24-17652, 2024.

X1.43
|
EGU24-17861
|
ECS
Matteo Domanico, Giulia Mantero, Francesco Pastore, Fabio Meloni, Matteo Garbarino, and Raffaella Marzano

The increase of wildfire severity and frequency in the Mediterranean area combined with the harsher environmental conditions damped by ongoing climate change, can hinder regeneration recruitment, particularly for obligate seeders in mountain ecosystems. Therefore, rethinking current post-fire management strategies in mountain ecosystem is crucial to mitigate the consequences of wildfire regime alterations on forest ecosystems and to re-establish ecosystem trajectories after large and severe wildfires. The high temperatures and scarce rainfall that characterized Autumn 2017 in Piedmont (North-Western Italy) led to an uncommon fire season, with ten large wildfires that burned about 9700 ha. The Susa fire was the largest event, burning with mixed severity almost 4000 ha, with Scots pine (Pinus sylvestris L.) stands being affected with the highest severity. Following the event, a reforestation project was started, aiming to restore the forest cover, particularly in large high severity patches far away from seed trees, likely to be affected by further degradation phenomena. Given the scarcity of Scots pine seedlings available in forest nurseries, direct seeding was considered a valid option, but it needed to be carefully planned, especially because of seed predation and run-off. Indeed, post-dispersal seed predation plays a key role in the natural dynamics of forest ecosystems as it can deeply affect the number of seeds available for recruitment. To assess the dynamics and rate of seed predation by different taxa and to identify the magnitude of seed losses, an experimental approach was then applied. Specific field experiments are being performed in both the fall and spring seasons, starting from October 2023, to evaluate the magnitude of post-dispersal seed predation within the high severity patches of the Susa fire, its spatial distribution considering three different microhabitats (open areas, close to deadwood, under shrubs) and the main predators involved among insects, birds, and rodents. Understanding the impact of post-dispersal seed predation is a crucial aspect to develop targeted post-fire management strategies, possibly reducing restoration costs and improving its success.

How to cite: Domanico, M., Mantero, G., Pastore, F., Meloni, F., Garbarino, M., and Marzano, R.: Guiding post-fire recovery: an assessment of Scots pine seed predation in the framework of active restoration interventions after a high-severity wildfire, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17861, https://doi.org/10.5194/egusphere-egu24-17861, 2024.

X1.44
|
EGU24-18341
|
ECS
Davide Marangon, Tommaso Baggio, and Emanuele Lingua

Disturbance legacy can be defined as the amount, the availability and the distribution of resource, spatial pattern, and habitat left behind after a disturbance, and are key factor in the restoration processes, especially after large high-severity disturbances. Survived trees and green islands are a fundamental source for seed dissemination, while deadwood (e.g., stumps, logs, snags) and other structures (e.g., pit and mound morphology) create favorable microsites for regeneration establishment and survival. After stand replacing disturbance, like high-severity windstorm, disturbance legacies are responsible for most of the terrain roughness in the damaged area. The aim of this study is to test roughness indices as a proxy to infer the role of disturbance legacies on promoting natural regeneration establishment in the short term (3 years after the storm). From a drone-based point cloud we compute high resolution digital surface model (DSM) in four areas affected by a stand replacing windstorm in 2018 in eastern Italian Alps, then we calculate six different metrics for five different roughness indices. In the same areas we established 100 circular plots to measure regeneration density. Logging methods (salvage logging and no-intervention) have also been considered in the analysis. Preliminary results showed that only three indices (standard deviation of profile curvature SD_PC, standard deviation of the residual topography SD_RT, and vector dispersion VD) are significantly correlated with regeneration density, and among them, the 90th quantile of SD_PC is the best fit. Overall, there is a positive significant correlation between roughness and regeneration density in the short term. In conclusion, the results suggest that roughness could be a good proxy for the disturbance legacies abundance and drone surveys are a powerful tool to adopt to estimate the roughness at slope scale. More detailed analysis on the threshold and influence of other parameters is needed and can be implemented to provide valuable management guidelines.

How to cite: Marangon, D., Baggio, T., and Lingua, E.: Post-windstorm natural regeneration dynamics in Italian Alps: roughness indices as a proxy for disturbance legacies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18341, https://doi.org/10.5194/egusphere-egu24-18341, 2024.