Abstract
Forest ecosystems, vital for biodiversity and climate regulation, are increasingly threatened by wildfires and climate change. This book chapter aims to provide a comprehensive analysis of methods used to measure forest resilience against these threats. This involves exploring both quantitative methodologies, focusing on specific ecosystem parameters, and qualitative ones that seek to understand contributing social and ecological factors. Earth Observation is emphasized as a critical tool for monitoring changes in forest health. The chapter underscores that forest resilience is multifaceted and cannot be described by a single metric; diverse approaches, including hydrological monitoring, machine learning, and decision support systems, are needed. Challenges in measuring resilience are discussed, such as dealing with heterogeneous data and the complexity of forest ecosystems. However, advances in technology provide significant opportunities for enhancing our understanding and ability to ensure the continued survival and prosperity of forest ecosystems.
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Keywords
- Forest resilience
- Wildfires
- Climate change
- Quantitative methodologies
- Qualitative methodologies
- Earth Observation
- Biodiversity
- Ecosystem parameters
- Disturbances
- Natural Segmentation and Matching
Introduction
Forest ecosystems are complex, dynamic, and resilient. They are home to a vast array of biodiversity, act as significant carbon sinks, and provide essential ecosystem services such as water regulation and soil conservation. However, these ecosystems are increasingly under threat from a range of disturbances, most notably wildfires and climate change. The capacity of a forest to withstand, adapt to, and recover from these disturbances is referred to as forest resilience [1].
Wildfires, while a natural and essential part of many forest ecosystems, have been increasing in frequency and severity due to changing climatic conditions. Droughts, heatwaves, and shifting precipitation patterns exacerbate the risk and impact of these fires [2]. Climate change, on the other hand, poses a more insidious threat. It alters the very conditions under which these ecosystems have evolved, leading to shifts in species distributions, changes in community composition, and potential disruptions to ecological functions [3].
Understanding and measuring forest resilience against wildfires and climate change is crucial for forest management and conservation. It allows us to assess the health of our forests, predict their response to future disturbances, and develop strategies to enhance their resilience. This is a complex task that requires a combination of quantitative and qualitative methodologies, each providing unique insights into different aspects of forest resilience [4].
This chapter aims to provide a comprehensive overview of the methods and technical approaches used to measure forest resilience against wildfires and climate change. We will explore both quantitative methodologies, which focus on measuring specific ecosystem parameters, and qualitative methodologies, which aim to understand the social and ecological factors contributing to forest resilience. We will also discuss the role of Earth Observation (EO) as a critical tool for monitoring changes in forest health, identifying areas of risk, and informing management strategies [5].
The importance of this topic cannot be overstated. As wildfires become more frequent and climate change continues to alter our environment, ensuring the resilience of our forests is not just an ecological imperative, but a matter of global concern. By understanding how to measure and enhance forest resilience, we can better equip ourselves to face these challenges and ensure the continued survival and prosperity of our forest ecosystems.
Understanding Forest Resilience
Resilience, as a concept, has been interpreted in diverse ways across various scientific fields. In the context of ecology, resilience is often understood as the capacity of an ecosystem to withstand disturbances and recover to its original state [6]. This capacity is not static but varies over time and in response to different types of disturbances. In the context of forest ecosystems, resilience is the ability of a forest to withstand, adapt to, and recover from disturbances such as wildfires and climate change [1].
Forest resilience is a multifaceted concept that encompasses several key aspects. Firstly, it involves the ability of a forest to resist disturbances, such as wildfires, and maintain its ecological functions and services. This includes carbon sequestration, biodiversity conservation, and water regulation, even under changing conditions [1]. Forest resilience involves the capacity of a forest to recover effectively from disturbances. This recovery can be seen in the regeneration of forests, the support of postfire ecosystem processes, and the reduction of the risk of future fires. The rate and extent of this recovery can serve as a measure of the forest’s resilience [6]. Also, forest resilience includes the ability of a forest to adapt to changing environmental conditions. This includes shifting temperature and precipitation patterns, changes in the distribution of species and communities, and changes in the frequency and severity of disturbances such as wildfires [1].
Understanding forest resilience is not just about understanding the forest’s response to disturbances. It also involves understanding the social and ecological factors that contribute to a forest’s resilience. This includes the role of local communities, indigenous peoples, and forest managers in supporting forest resilience, as well as the impact of broader socioeconomic and political factors. Forest resilience is a complex and dynamic concept that reflects the forest’s capacity to resist, recover from, and adapt to disturbances. Understanding this concept is crucial for managing and conserving our forest ecosystems in the face of increasing threats from wildfires and climate change.
Quantitative Methodologies for Measuring Forest Resilience
Introduction to Quantitative Methodologies
Quantitative methodologies provide a rigorous and objective means of measuring forest resilience. These methods focus on measuring specific ecosystem parameters that can indicate a forest’s ability to resist, recover from, and adapt to disturbances such as wildfires and climate change. Quantitative methodologies often involve the collection of numerical data that can be analyzed statistically to conclude the resilience of a forest. This data can be collected through various means, including field measurements, remote sensing technologies, and laboratory analyses.
The quantitative methods are organized into three main categories, (a) Vegetation monitoring, (b) Soil sampling, (c) Hydrological monitoring. This categorization reflects the domain of focus of the techniques. In each of these categories, multiple approaches exist on how to quantify forest resilience. This section provides an overview of the relevant techniques, while the detailed explanation is out of scope.
Vegetation Monitoring
Vegetation monitoring is a key quantitative methodology for measuring forest resilience. It involves monitoring changes in vegetation, such as plant cover and species composition, which can provide insights into the recovery of the ecosystem after a wildfire. Vegetation monitoring can be conducted using various technical means, depending on the specific objectives of the action. Ground observations can provide important information on plant density, height, and other quantitative measurements that can help assess changes in vegetation after a wildfire. Remote sensing methods, such as drones and satellite imaging, can provide a broader view of changes in vegetation over large areas.
Remote sensing methods have revolutionized the field of vegetation monitoring, allowing for the collection of data over large areas and inaccessible locations, and providing a broader view of changes in vegetation over time. These methods are particularly useful for monitoring changes in forest cover, including the extent of deforestation, forest fragmentation, and the impacts of natural disturbances, such as wildfires and climate change.
One of the most common remote sensing methods used in vegetation monitoring is the use of optical satellite imagery. This type of imagery can be used to monitor changes in forest cover, including the extent of deforestation and forest fragmentation, and the impacts of natural disturbances, such as wildfires and climate change [7].
Another important remote sensing technology is Light Detection and Ranging (LiDAR). LiDAR is a remote sensing technology that can be used to measure forest structure and biomass, providing valuable information on forest health and productivity [8].
Radar data is also used in remote sensing for vegetation monitoring. Radar data can be used to monitor changes in forest cover and structure, including the detection of forest disturbance and the mapping of forest biomass. Radar data can also be used to monitor changes in soil moisture levels, which can affect forest health and resilience [9].
Soil Sampling
Soil sampling is another important quantitative methodology for assessing forest resilience. Sampling soil after a wildfire can help to assess changes in soil fertility, organic matter content, and nutrient cycling, which can affect the ability of vegetation to regrow [10]. Soil sampling typically involves the collection of soil samples from various depths and locations within the forest, which are then analyzed in a laboratory for various chemical and physical properties. This information can provide valuable insights into the health of the soil and its capacity to support vegetation recovery after a disturbance.
Hydrological Monitoring
Hydrological monitoring is a crucial aspect of assessing forest resilience, as it provides insights into the impacts of wildfires on the water cycle and the potential for erosion and landslides [11, 12]. The availability and quality of water in a forest ecosystem can significantly affect its ability to recover from disturbances such as wildfires. For instance, a study by Yuan et al. [11] used machine learning models to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features. This approach could be adapted to predict the risk of flooding in forest areas following wildfires, which could inform management strategies for forest recovery and future fire prevention. The study by Tomelleri and Tonon [12] linked sap flow measurements with earth observations to model canopy transpiration. This approach could be used to monitor changes in water availability in forest ecosystems following wildfires, providing valuable insights into the recovery of the ecosystem.
The Role of Earth Observation (EO) in Measuring Forest Resilience
Earth Observation (EO) is a powerful tool for measuring and supporting forest resilience. It allows for the monitoring of changes in forest health, the identification of areas at risk, and the informing of management strategies [13]. Optical imagery, LiDAR data, radar data, climate data, and topographic data are all types of EO data that can be used to measure forest resilience. For instance, the study by Tomelleri and Tonon [12] demonstrated the relevance of Sentinel-2 data for the data-driven upscaling of ecosystem fluxes from plot scale sap flow data.
Ge et al. [13] introduced a novel semi-supervised contrastive regression framework for wall-to-wall mapping of continuous forest variables using multisensory satellite data. This approach could be used to monitor changes in forest cover and structure, including the detection of forest disturbance and the mapping of forest biomass. Furthermore, Filatov and Yar [14] proposed a solution for observing the area covered by the forest and water using the U-Net model, an image segmentation model. This approach could be used to monitor changes in forest cover, including the extent of deforestation, forest fragmentation, and the impacts of natural disturbances, such as wildfires and climate change.
LiDAR is a remote sensing technology that can be used to measure forest structure and biomass. LiDAR data can be used to create detailed 3D models of forest ecosystems, providing valuable information on forest health and productivity. For instance, a study by Sun et al. [15] correlated airborne LiDAR data statistics with the Landsat 8 satellite’s surface temperature product to understand the cooling effect of urban forests. In another study, Finley et al. [16] proposed a two-stage hierarchical Bayesian model to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory plot measurements. This approach provides a model-based method to estimate forest variables when inventory data are sparse or resources limit the collection of enough data to achieve desired accuracy and precision using design-based methods. Furthermore, Huang et al. [17] quantified the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR. This approach leverages the complementarity between digital aerial photogrammetry (DAP) and LiDAR measurements, providing a cost-efficient method for large-scale wall-to-wall AGB mapping.
Qualitative Methodologies for Assessing Forest Resilience
Qualitative methodologies play a crucial role in assessing forest resilience, providing insights into the complex interactions between social, economic, and ecological factors that influence forest ecosystems. These methodologies often involve the use of case studies, interviews, and participatory approaches to understand the experiences and perceptions of local communities, forest managers, and other stakeholders. In contrast with the quantitative methods, qualitative methodologies aim at providing information that cannot be directly materialized using in situ or remote measurements, rather to model the interactions among heterogeneous socio-environmental-economic systems.
For instance, a study by Mukul et al. [18] explored the role of nontimber forest products (NTFPs) in sustaining forest-based rural livelihoods in and around a protected area in Bangladesh. The study used a combination of household surveys and interviews to gather data on the collection, processing, and selling of NTFPs, and their contribution to household income and resilience. The findings revealed that NTFPs played a significant role in providing primary, supplementary, and emergency sources of income for local households, and were particularly important for households located close to the park.
Moreover, Itter et al. [1] developed a hierarchical Bayesian state-space model to investigate the variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest stand dynamics. The study used dendrochronology data from forest stands of varying composition, structure, and development stages in northeastern Minnesota. The results indicated that forest growth was most sensitive to variables describing climatic water deficit, and that forest growth was both resistant and resilient to climate extremes, particularly when these coincided with insect defoliation events.
The Interplay Between Quantitative and Qualitative Approaches
The interplay between quantitative and qualitative approaches is crucial for a comprehensive understanding of forest resilience. These approaches complement each other, providing a holistic view of the complex dynamics of forest ecosystems.
Tinchev et al. [19] introduced Natural Segmentation and Matching (NSM), an algorithm for reliable localization in both urban and natural environments using laser data. The authors highlighted the challenges of working in structure-poor vegetated areas such as forests, where clutter and perceptual aliasing prevent repeatable extraction of distinctive landmarks. The study demonstrated the interplay between quantitative data (laser measurements) and qualitative understanding (recognition of natural features) in achieving reliable localization.
Lade et al. [20] proposed a framework for conceptualizing and modeling social-ecological transformations based on adaptive networks. The authors emphasized the interplay between the structure of a social-ecological system and the dynamics of individual entities. This approach could be used to understand the complex dynamics of forest ecosystems and their resilience to disturbances.
Conclusion
Forest resilience is a complex and multifaceted concept, encompassing the ability of forest ecosystems to withstand, recover from, and adapt to disturbances. The measurement of forest resilience is a challenging task, requiring a combination of quantitative and qualitative methodologies and a deep understanding of the interplay between social, economic, and ecological factors. A key takeaway is the fact that forest resilience is not described by a single metric or parameter, rather multiple viewpoints are needed to assess the resilience of a forest.
The studies reviewed in this chapter highlight the diverse approaches to measuring forest resilience, from hydrological monitoring and earth observation techniques to the use of machine learning models and decision support systems. These methodologies provide valuable insights into the impacts of disturbances such as wildfires, climate change, and deforestation on forest ecosystems and their ability to recover.
However, these studies also highlight the challenges of measuring forest resilience, including the difficulty of dealing with large amounts of heterogeneous data, the complexity of forest ecosystems, and the variety of disturbances they face. Despite these challenges, there are also significant opportunities for improving our understanding and measurement of forest resilience, thanks to advances in technology, data collection, and analysis.
In conclusion, while measuring forest resilience is a complex and challenging task, it is also a crucial one. Forests play a vital role in maintaining global biodiversity, regulating climate, and supporting human livelihoods. Understanding and enhancing their resilience is therefore of paramount importance for both people and the planet. As technology and our understanding of forest ecosystems continue to evolve, so too will our ability to measure and enhance forest resilience.
References
Itter, M. S., Finley, A. O., D’Amato, A. W., Foster, J. R., & Bradford, J. B. (2016). Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest stand dynamics. arXiv, 1602.07228. Retrieved from https://arxiv.org/abs/1602.07228v2
Tomaselli, L., Jen, C., & Lee, A. B. (2020). Wildfire smoke and air quality: How machine learning can guide forest management. arXiv, 2010.04651. Retrieved from https://arxiv.org/abs/2010.04651v2
Ballard, T., Cooper, M., Lowrie, C., & Erinjippurath, G. (2023). Widespread increases in future wildfire risk to global forest carbon offset projects revealed by explainable AI. arXiv, 2305.02397. Retrieved from https://arxiv.org/abs/2305.02397v1
Zhou, W., & Klein, L. (2020). Monitoring the impact of wildfires on tree species with deep learning. arXiv, 2011.02514. Retrieved from https://arxiv.org/abs/2011.02514v2
Imteaj, A., Amini, M. H., & Mohammadi, J. (2019). Leveraging decentralized artificial intelligence to enhance resilience of energy networks. arXiv, 1911.07690. Retrieved from https://arxiv.org/abs/1911.07690v1
Meyer, K. (2015). A dynamical systems framework for resilience in ecology. arXiv, 1509.08175. Retrieved from https://arxiv.org/abs/1509.08175v1
Sannigrahi, S., Bhatt, S., Rahmat, S., Rana, V., & Chakraborti, S. (2018). Effects of forest fire severity on terrestrial carbon emission and ecosystem production in the Himalayan region, India. arXiv, 1805.11680. Retrieved from https://arxiv.org/abs/1805.11680v1.
Ballester-Berman, J. D. (2020). Reviewing the role of the extinction coefficient in radar remote sensing. arXiv, 2012.02609. Retrieved from https://arxiv.org/abs/2012.02609v1
Baur, M., Jagdhuber, T., Link, M., Piles, M., Entekhabi, D., & Fink, A. (2020). Estimation of vegetation loss coefficients and canopy penetration depths from SMAP radiometer and IceSAT lidar data. arXiv, 2012.03318. Retrieved from https://arxiv.org/abs/2012.03318v1
Makarieva, A. M., Gorshkov, V. G., Sheil, D., Nobre, A. D., Bunyard, P., & Li, B.-L. (2013). Why does air passage over forest yield more rain? Examining the coupling between rainfall, pressure and atmospheric moisture content. arXiv, 1301.3083. Retrieved from https://arxiv.org/abs/1301.3083v2
Yuan, F., Mobley, W., Farahmand, H., Xu, Y., Blessing, R., Dong, S., et al. (2021). Predicting road flooding risk with machine learning approaches using crowdsourced reports and fine-grained traffic data. arXiv, 2108.13265. Retrieved from https://arxiv.org/abs/2108.13265v2
Tomelleri, E., & Tonon, G. (2021). Linking sap flow measurements with earth observations. arXiv, 2108.01290. Retrieved from https://arxiv.org/abs/2108.01290v1
Ge, S., Gu, H., Su, W., Lönnqvist, A., & Antropov, O. (2023). A novel semisupervised contrastive regression framework for forest inventory mapping with multisensor satellite data. IEEE Geoscience and Remote Sensing Letters, 20, 1–5. https://doi.org/10.1109/LGRS.2023.3281526
Filatov, D., & Yar, G. N. A. H. (2022). Forest and water bodies segmentation through satellite images using U-Net. arXiv, 2207.11222. Retrieved from https://arxiv.org/abs/2207.11222v1
Sun, W., Sun, Y., Liu, C., & Albrecht, C. M. (2023). DeepLCZChange: A remote sensing deep learning model architecture for urban climate resilience. arXiv, 2306.06269. Retrieved from https://arxiv.org/abs/2306.06269v1
Finley, A. O., Andersen, H.-E., Babcock, C., Cook, B. D., Morton, D. C., & Banerjee, S. (2023). Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska. arXiv, 2302.06410. Retrieved from https://arxiv.org/abs/2302.06410v3.
Huang, R., Yao, W., Xu, Z., Cao, L., & Shen, X. (2022). Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR. arXiv, 2204.06746. Retrieved from https://arxiv.org/abs/2204.06746v1
Mukul, S. A., Rashid, A. Z. M. M., Uddin, M. B., & Khan, N. A. (2015). Role of non-timber forest products in sustaining forest-based livelihoods and rural households’ resilience capacity in and around protected area-A Bangladesh study. arXiv, 1508.02056. Retrieved from https://arxiv.org/abs/1508.02056v1
Tinchev, G., Nobili, S., & Fallon, M. (2018). Seeing the wood for the trees: Reliable localization in urban and natural environments. arXiv, 1809.02846. Retrieved from https://arxiv.org/abs/1809.02846v2
Lade, S. J., Bodin, Ö., Donges, J. F., Kautsky, E. E., Galafassi, D., Olsson, P., & Schlüter, M. (2017). Modelling social-ecological transformations: An adaptive network proposal. arXiv, 1704.06135. Retrieved from https://arxiv.org/abs/1704.06135v1
Acknowledgments
This study has been conducted in the framework of the SILVANUS—Integrated Technological and Information Platform for Wildfire Management project and has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 101037247. The contents of this publication are the sole responsibility of the authors and can in no way be taken to reflect the views of the European Commission.
Declaration of Competing Interest
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
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Demestichas, K. et al. (2025). Measuring Forest Resilience Against Wildfires and Climate Change: Methods and Technical Approaches. In: Gkotsis, I., Kavallieros, D., Stoianov, N., Vrochidis, S., Diagourtas, D., Akhgar, B. (eds) Paradigms on Technology Development for Security Practitioners. Security Informatics and Law Enforcement. Springer, Cham. https://doi.org/10.1007/978-3-031-62083-6_5
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