This message contrasted with the still overwhelming presence of politicians talking after fires and promising aid for farmers and affected communities or a firm hand against the arsonists and criminals behind the fires. This was along the same lines as the suppression frame in which policies are seen as palliative and mostly associated with recovering from tragedy and loss. In this respect, the notion of financial aid is common. Affected communities claiming aid, or politicians going to the affected regions and discussing aid packages, were part of the suppression frame. This discourse had local communities playing the role of victims and, though demanding improvements, in a rather passive position. Here, the focus on the human drama, which is also triggered by the mediatization of wildfires, can activate human empathy in readers but not necessarily gives agency to the victims. The agency of locals is other of the unresolved aspects of the resilience frame. In the discourse of suppression, it is very clear: locals are affected, and they are evacuated, helped, and must be protected. However, it would be desirable and expected to notice greater agency in the resilience frame and read stories of communities preparing their properties, organizing meetings with policymakers and explaining how they protect themselves (or authorities promoting this). These elements are almost completely unnoticed in the stories. The agency of the local people needs to be further developed in the future of the resilience frame storytelling. Surprisingly, the suppression frame appropriated this gap and colonized the device of high agency of locals. Although coloured green (Fig. 3), it is the weaker element but added more to suppression than to resilience. This is because we find stories about locals helping to extinguish fires by fighting against them. To date, the suppression discourse has a major role reserved for communities: contributing to "the fight" when "the enemy" is at their door.

As well as this, some stories include the theme of rural depopulation, but this is usually articulated by experts or politicians. In the study, this issue was included under the framing device of structural causation. However, we noted that it is not a claim from the grassroots that evidences empowerment from the communities. Sometimes "the rural" is even used politically and vindicated by all parts of the ideological spectrum. All these aspects reveal that resilience building is a political matter, part of the realm of "policies", whilst suppression is part of the "politization" of wildfires (political blaming, political statements after disasters, political promises of aid, disqualifications and counterstatements, etc.). Paradoxically, this sort of media politization, which is part and consequence of mediatization processes, depoliticizes wildfires.

#### *4.3 Elements of Mediatization*

The resilience frame gained momentum during the period analysed thanks to the process of wildfire mediatization. From the "mediatized sources" involved in wildfire communication, we made a list of the agents who were profiling the frame throughout the analysed period. Some of the sources are from the emergency services (firefighting bodies, civil protection). These, and local people closer to affected areas, are promoters of the suppression frame. But for the resilience frame, we tagged other sorts of sources, such as experts and scientists, institutions, and universities, some of which are specialized centres or sections of official bodies. There was some correlation between the presence of these sources and the presence of the resilience frame: they use a certain type of language, articulate particular arguments and offer complexities and background. This is a salient result that explains why resilience stories are gaining space within the media: there is a greater presence of specialized sources replicating a different discourse. Also, this would problematize the idea that resilience is a narrative coming from the grassroots, it is not (at least in the media), but this is a matter that I do not have space to properly discuss here.

More sources of this type would lead us to expect still more stories within the resilience frame. And this is a challenge: the resilience frame does not readily adapt to media logic and mediatization processes. As said, suppression stories are about emergency, action and drama: they tell of firefighters attacking the front, people having to leave their homes and authorities sending more forces. The information and drama fit the media requirements perfectly. However, explanations from experts and scientists are of a different ilk. They need to be modelled and adapted. Mediatization of experts and technical staff could have a better role in wildfire stories when deployed at the right time, delivered within attractive formats and pictures and used easy-toread metaphor and language. However, the process can also lead to simplification. These ideas are connected with Plana's (2011) consideration of the challenges that experts face to connect with non-specialized publics and the media. Ultimately, they present a cross-current narrative: they try to explain that extinction is not so effective, and they adopt discourses on "good fires" and focus not on flames but on soils, not on the evil arsonists but on the lonely farmer, not on the villagers tragedy but on rural abandonment and policies. Resilience stories are anti-climax almost by default. There is a huge task here: resilience stories are not "sexy" enough, so far, for high mediatized stories in networked times.

One of the tasks to do is to transform risk stories. Risk was tagged as a neutral element; it is a wild device. It was often found as part of the discourse of prevention; all stories can appropriate risk. Under a functionalist perspective, media are tools for social risk awareness. The media play the role, and stories of risk are attached to both the suppression and resilience frames. They are especially active during heat waves, when the media replicate the warnings coming from the authorities, and when they inform about restrictions and even prohibitions on accessing certain areas. Thus, the risk is related to climate change and, in some stories, is a consequence of structural causalities (heat, the density of fuels, drought, firework displays, etc.).4 Risk is however a multifaceted frame, a sort of joker card, and media should rethink their role in mixing risk, drama and suppression stories and explore how to better articulate stories in which risk is linked to collective responsibility, effective policies and social response to fires.

#### **5 Discussion and Conclusions**

This research shows that the resilience discourse on wildfires in Spain is growing. Like all research, it has limitations. The most evident is that the analysis used a dichotomized structure within the media framing approach. It also focuses on written journalism and did not attempt to analyse visuals, despite the role they play in framing stories. Even so, it accomplished its objectives. Suppression and resilience frames are not opposed. They both use some of the frame devices that we labelled as neutral, for example, fire damage (material, environmental or human) or risk. The results are also in tune with advances in media wildfire framing, and we noted that the approach depended on the "disaster timeline" ("frame changing") (Crow et al., 2017a, 2017b; Houston et al., 2012). We could even mention that the suppression frame is closer to what the framing literature refers to as an episodic frame (Gamson et al., 1992; Iyengar, 1991), in our case, attached to the fire season.

How the media explain fires is undergoing an important change because of superfires, or "6th generation" fires, a concept that emerged during the five years of the study. The resilience frame is a complex and reflective frame that is gaining momentum in the media, partially thanks to a process of mediatization. Part of this process has an impact on the sort of information sources that the media are using. Most of them are still official sources, as it was stated by previous research (Fabra-Crespo & Rojas-Briales, 2015; Vicente-Mariño & Delgado-Arango, 2019), and replicate suppression frames. But now, new, more specialized agents are taking their place in the Spanish media, with a different story to tell, putting the accent on resilience and technical aspects. In our study, we also identified opportunities for improvement around the agency of rural and local communities. The media should pay more attention to the role of farmers and villagers, to the structural elements of the rural contexts and its challenges (e.g. depopulation, agri-food model), as well as

4 Here, it is worth mentioning that in Catalonia, it is a custom to set off fireworks on Saint John's night to celebrate the summer solstice. *La Vanguardia,* a leading newspaper in the region, emphasizes the risk almost every year to some extent or another.

to the measures to take in the territory to, as Castellnou said, "vaccinate" it against superfires. Here, we propose a rural scope on news, reports and media storytelling on wildfires.

The resilience frame must overcome many challenges if it is to become more preeminent in the media, but it is difficult to predict how the discourse is going to evolve. However, one scenario is that the global warming indicators will strength its establishment and even promote its hegemony. In the field of wildfires, both resilience and suppression are being reactivated. They do not oppose each other; they feed off each other and evolve. Therefore, it is not a process of substitution. Moreover, although mainstream media are limited in terms of space, there are numerous other places in the networks and social media that are ready and waiting for more content about wildfires, new understandings and stories. Although perhaps less influential, they should not be underestimated.

**Acknowledgements** This article is part of the research project "Nuevos imaginarios del rural en la España contemporánea: cultura, documental y periodismo" (PID2021-122696NB-I00) funded by MCIN/AEI/10.13039/501100011033/ and ERDF "A way of making Europe".

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Fire Severity as a Determinant of the Socioeconomic Impact of Wildfires**

**E. Marcos, J. M. Fernández-Guisuraga, V. Fernández-García, A. Fernández-Manso, C. Quintano, S. Suárez-Seoane, and L. Calvo**

**Abstract** Fire has played a crucial role in shaping Earth's landscapes for millions of years and has been used as a cultural tool for human development for millennia. However, changing fire regimes driven by global change drivers and human influences are reshaping landscapes and leading to more severe wildfires, with significant socioeconomic and environmental consequences.  In general, severe wildfires disrupt the functioning of ecosystems and the subsequent recovery of the ecosystems services. Remote sensing techniques represent efficient tools for monitoring post-fire ecosystem service recovery. On the other hand, the impact of large fires is perceived differently by society influenced by socioeconomic factors, previous experiences, and community dynamics. Understanding these perceptions is essential for developing effective wildfire management and mitigation strategies at the local level. To address the challenges posed

J. M. Fernández-Guisuraga

Centro de Investigação e de Tecnologias Agroambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal

A. Fernández-Manso

Department of Agrarian Science and Engineering, School of Agricultural and Forestry Engineering, University of León, 24400 Ponferrada, Spain

C. Quintano

Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004 Palencia, Spain

S. Suárez-Seoane

E. Marcos (B) · J. M. Fernández-Guisuraga · V. Fernández-García · L. Calvo Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, 24071 León, Spain e-mail<EMAIL_ADDRESS>
Department of Electronic Technology, School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain

Department of Organisms and System Biology, University of Oviedo, Ecol Unit, Oviedo, Mieres, Spain

J. Rodrigo-Comino and L. Salvati (eds.), *Fire Hazards: Socio-economic and Regional issues*, https://doi.org/10.1007/978-3-031-50446-4\_12

by changing fire regimes, a Fire Smart Territory (FST) approach could be considered, emphasizing integrated fire management that considers prevention, effective response, and post-fire restoration. This approach presented in this book chapter also promotes the involvement of local communities in fire prevention and management. So, the conservation of ecosystem services in fire-prone areas requires a holistic and adaptive approach that incorporates ecological knowledge, societal needs, and sustainable land management practices. By adopting an FST framework and considering the complex interactions between fire, ecosystems, and human communities, it is possible to mitigate the impacts of wildfires and promote resilience in these vulnerable landscapes.

**Keywords** Ecosystems services · Fire regime · Fire smart territory · Post-fire management · Remote sensing methodologies · Social perception

#### **1 Introduction**

Fire is a key environmental disturbance that has shaped biomes since 400–350 million years ago across most of Earth's land surface (He et al., 2015). Specifically, for more than 9000 years, fire has been used as a management cultural tool targeted to the opening and maintenance of pastures and croplands, thus largely contributing to human development (Carracedo et al., 2018). Nevertheless, during the last decades, historical fire regimes are shifting due to multiple interactions among global change drivers (mainly climate and land use change) and due to direct human influences, such as ignition and suppression (Rogers et al., 2020). Complex socioeconomic changes (i.e., rural depopulation, land abandonment, or proliferation of rapid-growing forest plantations) have imposed shifts in fuel amount and connectedness (due to massive woodland and shrubland increases in the European countries of the Mediterranean Basin) across traditional landscape mosaics (Delgado-Artés et al., 2022). This landscape homogenization, occurring in a context of climate change (i.e., warmer and drier conditions), is driving a significant change in wildfire typology from small, fuel-limited fires to drought-driven fires (Chergui et al., 2018).

In global terms, it has been noticed an increase in the number of mega-fires and extreme wildfire events that are strongly severe, such as those that occurred in 2017 in Portugal (Turco et al., 2019), in 2020 in California (Higuera & Abatzoglou, 2021) and, recently, in 2022 in Spain. Indeed, the frequency of heat-induced fire weather is projected to increase by 14–30% by the end of the century (2071–2100) depending on the climate change scenario, being suggested that the frequency and extent of large wildfires will increase even more throughout the Mediterranean (Ruffault et al., 2020) and the Boreal region (Drobyshev et al., 2021). The higher the warming level is, the larger the expected increase of burned area, ranging from 40 to 100% across the IPCC scenarios, with significant benefits if warming is limited below 2 °C (Turco et al., 2018).

These new patterns of fire regime are generating multiple (economic, social, and environmental) impacts on society that are driving extraordinary damages (Thomas et al., 2017), increasing direct risks on human lives, and also on good provision and poverty (Paudel et al., 2021), particularly at the wildland–urban interface (Radeloff et al., 2018; Rosenthal et al., 2021). The main impacts of these severe wildfires on society are direct such as human losses, damage to homes and other infrastructures and physical and mental health (Rosenthal et al., 2021). In addition to direct impacts on people and economic losses, severe wildfires also have other substantial effects on society through indirect impact as the alterations of the ecosystem services, defined in the Millennium Ecosystem Assessment (MEA, 2005) as "conditions and processes through which natural ecosystems sustain and fulfil human life".

Wildfires are highlighted as one of the major disturbances that negatively trigger ecosystem services. In this context, some studies (Taboada et al., 2021) have demonstrated these negative effects on the provisioning services in a fire-prone landscape dominated by pine forests, because they interrupted the capacity of providing timber, mushroom, firewood, and animal hunting, all of them very relevant for the local rural economy of the surrounding areas. Furthermore, wildfires affect negatively most of the regulating services due to the effects on soil erosion, runoff, water quality, and soil fertility, among others (Roces-Díaz et al., 2022). Finally, cultural services, such as cultural heritage and recreation are also negatively affected (Roselló et al., 2020). However, the negative effects on ecosystem services will depend on the type of vegetation affected and their adaptations to recover easily after a fire since in areas dominated by species with fast regeneration some services may be favored, but always depending on the fire severity. Mola & Williams (2018) reported a positive influence of a low severe fire affecting shrublands and grasslands by the creation of open landscapes with an increase in the floral density that represents a more favorable environment for pollinators' net. Therefore, fire severity is one of the main constraints of the effects of forest fires on the provision of ecosystem services to society.

### **2 Impacts of Fire Severity of Large Wildfires on Ecosystem Services**

Comprehensive analysis of the socioeconomic impacts of large wildfires is often addressed through the ecosystem services approach. These wildfire impacts have been studied across the globe (Roces-Díaz et al., 2021), revealing both positive and negative effects depending on the type of service assessed (provisioning, regulating, and cultural), and the type of ecosystems and society in terms of development (Pausas & Keeley, 2019; Pereira et al., 2021; Roces-Díaz et al., 2021). However, the impacts of fire severity on ecosystem services are less known. The fire severity impacts are the result of immediate impacts on ecosystems and biogeochemical cycles, and of indirect influences on post-fire recovery (Huerta et al., 2022). Concerning the immediate impacts, fire severity is inherently linked to a different degree of vegetation consumption and mortality (Keeley, 2009), to a different magnitude of change in litter and soil properties (Fernández-García et al., 2019), and affections to fauna (Jager et al., 2021). This unavoidably extends to the inter-relationships of all these elements, as well as to ecosystem structure and processes. Focusing on ecosystem responses, fire severity shapes vegetation recovery, as plants have differential responses depending on their functional traits, ones being favored by severe fires (i.e., some herbaceous, and many shrub species with adaptative traits) while others temporarily decline (usually arboreal vegetation) (Fernández-García et al., 2020). Similarly, fire severity influences the post-fire trajectories of soil properties in different ways (Fernández-García et al., 2019). Pioneer work in heterogeneous mountainous landscapes (Huerta et al., 2022) has shown how several ecosystem services and related functions change in function of fire severity one year after the fire (Fig. 1). The analysis revealed decreases in the supporting service proportional to fire severity, a consequence of the loss of photosynthetic activity, nutrient cycling capacity, and soil quality. An increase in the provisioning service was found after low-severity burning and a decrease in severely burned areas, in response to analogous shifts in grass production for livestock and wood production. The regulating service decreased proportionally to fire severity, as it caused significant decreases in carbon stocks and erosion protection. The cultural service, studied through the woody species diversity and aesthetic value (diversity of floral colors of woody species), decayed mainly at moderate severities. Among the studied functions, only soil fertility increased proportionally to fire severity. These results are in line with those reported in previous studies comparing prescribed fires (usually less severe) and wildfires (usually more severe) (Fig. 1) (Pereira et al., 2021; Roces-Díaz et al., 2021), suggesting similar underlying mechanisms. Despite the foregoing, further work is necessary to achieve a better understanding of the complexity of fire severity's influence on ecosystem services, as multiple environmental site-dependent factors might intervene (Fernández-Anez et al., 2021).

### **3 Ecosystem Services Recovery After Large Wildfires: A New Methodological Approach**

Current and predicted fire regime shifts in the Mediterranean Basin may harm the high resilience to fire that fire-prone Mediterranean ecosystems have exhibited under historical fire disturbance regimes. In this context, the natural recovery of ecosystem functions and services provided by these ecosystems could be endangered by unprecedented fire impacts on the soil and vegetation, as well as by transitions to alternate stable ecosystem states (Johnstone et al., 2016). Therefore, the assessment of how the functional indicators of ecosystem functions and services recover to a pre-disturbance state is essential for supporting adaptive management strategies to safeguard the ecosystem services' flow for human well-being worldwide, especially in the most vulnerable regions.

**Fig. 1** Influence of burn severity on four ecosystem functions or services, each one included in a different major group of ecosystem services is shown in the upper part, with balloon sizes proportional to the service provision. The influence of prescribed burning and wildfires on these services is shown in the lower part. Based on Pereira et al. (2021), Roces-Díaz et al. (2021), Huerta et al. (2022)

Field-based inventories have been traditionally used to measure soil and vegetation functional indicators as proxies of the recovery of ecosystem functions and services with high reliability. However, in the context of global change, with increasingly large burned areas encompassing several plant communities, this approach is no longer versatile. Field inventories may not capture the total ground spatial heterogeneity in large, heterogeneous burned areas and do not allow wall-to-wall (i.e., spatially explicit) estimates. In this sense, the synoptic nature of active and passive remote sensing earth observations, in combination with precise field measurements, offers nowadays an efficient way to achieve this goal.

Conventionally, open data from multispectral passive optical sensors have been used to estimate supporting services' recovery, mainly ecosystem productivity. For instance, McMichael et al. (2004) applied a normalized difference vegetation index (NDVI)–leaf area index (LAI) model using multi-temporal Landsat data to estimate LAI in a chronosequence approach as a proxy of ecosystem productivity recovery in chaparral shrublands in central California. The temporal dynamics of ecosystem productivity in burned sagebrush communities of the western United States were examined through a dynamic global vegetation model and the gross primary production product from MODIS (Pandit et al., 2021). Fernández-Guisuraga et al. (2022a) proposed the use of physical-based approaches (radiative transfer and pixel unmixing models) for retrieving fractional vegetation cover from a pre- and post-fire time series of Sentinel-2 data as a vegetation productivity resilience metric in several forest and shrubland communities in the western Mediterranean Basin.

Passive optical reflectance data is related to the general trends in the recovery of ecosystem services related to the top-of-canopy vegetation traits in multilayered

**Fig. 2** Appropriate remote sensing products for the assessment of several ecological variables

plant communities (Fig. 2) such as ecosystem productivity. The recovery assessment of other ecosystem services closely related to the vertical profile of the vegetation structure (e.g., aboveground carbon stocks and wildlife habitat), or to the soil properties, requires the implementation of active remote sensing techniques (Fig. 2), or fusion approaches with passive data.

The sensitivity to the quantity and distribution of vegetation scatterers throughout the canopy of light detection and ranging (LiDAR) and synthetic aperture radar (SAR) sensors has been exploited to evaluate the ecosystem services of the recovery of aboveground carbon stocks (Fernández-Guisuraga et al., 2022b) and structural complexity for wildlife habitat (Fernández-Guisuraga et al., 2022c) in Mediterranean fire-prone plant communities worldwide. Regarding the ecosystem services supported by the soil, Fernández-Guisuraga et al. (2022d) leveraged the increased interaction of SAR backscatter data in the L-bandwidth with soil surface properties for retrieving soil organic carbon and nutrients content as proxies for postfire recovery of belowground carbon stocks and nutrient cycling services. Although LiDAR and SAR data have the potential to monitor other types of ecosystem services such as timber production (Yoga et al., 2018) or edible fungi production (Peura et al., 2016), to date these methods have not been applied to the assessment of ecosystem services recovery after fire.

The remote sensing-based research presented above has provided a sound methodological basis and reliable results on the recovery trends of specific ecosystem services after different scenarios of fire severity, which also allowed the estimation of their driving factors regarding the fire regime, the dominant vegetation lifehistory traits, and the environmental conditions in Mediterranean landscapes. These studies evidenced that plant communities dominated by resprouting species exhibit an enhanced recovery of ecosystem services such as ecosystem primary production, habitat structural diversity, and aboveground carbon stocks in the short term after a fire, as compared to communities dominated by obligate seeders. Likewise, fire severity undermines ecosystem services recovery by affecting both resprouting and seeding capability of the dominant species in the communities, but the magnitude of this effect seems to be modulated by environmental resource availability. Enhancing the resilience of ecosystem services provided through the promotion of resprouter species and the implementation of adaptive management strategies to prevent high fire severity burnings should be a priority in Mediterranean fire-prone landscapes.

#### **4 Social Perception of the Impact of Large Wildfires**

In the last few decades, the greater occurrence of large and destructive wildfires leads to an increase in the impact on social systems. Although in most studies this impact is measured in terms of economic losses (e.g., home loss or suppression costs), significant research has been developed on the human dimensions of wildland fire since 2000 (McCaffrey et al., 2013), by addressing topics such as social dynamic change after a wildfire, public response during fires, or attitudes toward implementation of forest fire mitigation measures (McCaffrey et al., 2013; Paveglio et al., 2015). These studies were carried out through surveys of the population, workers and volunteers in medical facilities, social services agencies, and non-profit organizations located in communities affected by wildfires (Rosenthal et al., 2021).

Social perception of wildfires can be shaped by their previous experiences, people's standard of life, their interaction with land and forests, and their worldviews. For instance, Yulania et al. (2021) found that the inhabitants of rural areas of Indonesia had a perception that usual practices of burning forest and land will be detrimental to their society, so their participation and supervision of forest and land use would be fundamental to manage the environment properly. Other populations in the northwest of Spain have a similar perception of the damage of wildfires both for the ecosystem and for the people living in these areas. In this case, they attribute the responsibility to mitigate the fire damage to the local and regional authorities but did not perceive a social co-responsibility in this problem (García-Mira et al., 2008), because they consider that wildfires are caused by criminal behavior. A different perception was found in a study following the largest wildfire that occurred in Sweden in 2014 (Lidkogs et al., 2019). The population did not blame forestry companies or fire departments, rather they considered this disaster as a learning opportunity to improve the prevention of future events and to create a nature reserve with positive consequences (higher biodiversity). However, people's perceptions change when, in addition to living in areas affected by frequent fires, they live under poverty conditions, and overcrowded and structural issues, such as what happens in some areas of Chile. In such a context, people perceived that authorities were not concerned about the risk, and so, they felt very vulnerable to the occurrence of a disaster caused by wildfires. Sapiains et al. (2020) recommended that in these situations local communities should have responsibilities to improve the negative perception of themselves and should create more social interactions. After destructive fires in California, Rosenthal et al. (2021) found that wildfire survivors perceived (i) accessing safe and secure shelter mainly for those whose homes were lost; (ii) the economic instability which increases anxiety or depression; and, (iii) mental and emotional well-being and access to health resources as the main social problems. Participants in this survey pointed out the deficiency of private and public recovery resources used to restabilize following the fire, and therefore, the administrations should implement structural changes to cover the needs of wildfire survivors.

The studies mentioned above showed the variability in the social perception of the impacts of large forest fires, which implies that the solutions to mitigate this problem are complex. Further study and new methodologies for its assessment are needed to understand the social consequences at the local level (Paveglio et al., 2015). Future research could help to strengthen the role of the community in designing and implementing preventive measures together with governmental agencies.

### **5 Management and Conservation of Ecosystem Services in Fire-Prone Areas**

Despite the intense efforts dedicated to forest fire suppression in western countries, the challenge of reducing the impact on ecosystem services is far from being solved. Current forest fire policies in these countries have not solved the problem, and probably they will not be effective in the future, as all initiatives focus on fire suppression and minimize the traditional use of fire based on the ecological knowledge of former European communities. This traditional use of fire as a tool for land management has been manipulated and almost criminalized by an urban-centric perspective and an anti-fire bias (Tedim et al., 2016).

From an anthropogenic, technological, and ecological approach, the most "holistic" concept is the so-called "Fire Smart Territory" (FST), which puts at the center of the problem the "territory" understood in a broad sense, not only as landscape but as the social and psychological interpretation of the landscape. The fundamental assumptions of FST are that fire is a dual and ambiguous process, and that it is not simply a biophysical process with social connotations but a social process. It is a complex issue that can be understood only in coupled human and natural systems. The local understanding of the wildfire problem prepares strategically each territory to make it less prone to wildfire and its inhabitants less vulnerable and more resilient, in the scope of economic valorization, sustainable development, and security of the territory's resources.

We have designed a new model, based on a triad or group of three factors especially linked to each other, to carry out an integral and coherent management of forest fires within the framework of ecosystem services conservation (Fig. 3). This figure depicts

the triangle that serves to define an FST and expresses the interaction among risk management in mosaic landscapes (prevention), the effective response when a fire occurs, and the post-fire restoration supported by proven ecological knowledge.

In this sense, strategic thinking and planning that FST posits must recognize and accept fire as a natural process necessary for the maintenance of many ecosystems and strive to reduce conflicts between fire-prone landscapes and people. Thus, Integrated Fire Management implies a greater integration of the different components of fire management (prevention, detection, suppression, and use).

The first question to be asked when the goal is to create an FST is: how to design fire prevention in fire-prone agroforestry landscapes? As indicated above, the socioeconomic change in land uses has created a continuity of wooded forest stands together with high fuel accumulation that could favor the occurrence of large wildfires, with crown fires and massive secondary outbreaks. In this sense, fire prevention and suppression approaches that have relied exclusively on silvicultural measures and containment infrastructures are increasingly ineffective in stopping the spread of wildfires. Given that agroforestry landscape mosaics composed of a mix of different land cover and land use types are considered less prone to fire than forests, approaches that support the involvement of rural people in agricultural and forestry activities should be proposed (Bertomeu et al., 2022).

This preventive management model aims to defend the vulnerability of ecosystem services, i.e., the physical, social, economic, and environmental factors that increase the susceptibility of individuals, communities, settlements/buildings, ecosystems or systems to damage, and loss from wildfires. This model contributes to increasing the effectiveness of resistance (reducing fire severity to give trees a chance to survive) and promotes resilience (post-fire rehabilitation and landscape diversification to give vegetation a chance to regenerate in case of future fires). Marino et al. (2014) carried out a quantitative Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis applied to Spain to analyze fuel management for forest fire prevention. The study highlighted how solutions to forest fires should be based on rural development that includes sustainable forest management and an integrated defense against fires. The impulse of the public administration with active policies that allow taking advantage of these opportunities is proposed as a priority if the challenge of reducing the severity of forest fires in the context of climate and socioeconomic change is to be faced with guarantees.

As a complement to risk management in mosaic landscapes, Madrigal et al. (2019) proposed the creation of Strategic Management Zones, areas of the territory defined and prioritized according to a specific methodology that, taking into account the fire risk, the fire behavior in the study area and the vulnerability of its natural, rural, or urban values to be protected, allows to establish and optimize space-temporal planning of fuels and infrastructures that limits the fire potential, detecting extinction opportunities, and anticipating an effective and safe defense strategy for large forest fires type for which it has been designed.

The second key question related to the creation of an FST is: what should be the fire suppression approach to avoid the wildfire paradox? To answer this question we have followed the approach taken by projects such as "Fire Paradox" (Silva et al., 2010) which is based on the paradox that fire can be "a bad master but a good servant", so it is necessary to take into account the negative impacts of current wildfire regimes (understanding initiation and spread) and the beneficial impacts of controlled fires in vegetation management and as a planned mitigation practice (prescribed burning along with some traditional fire uses) and for fighting wildfires (suppression fire).

Faced with the challenge of climate change, this second FST strategy proposes to take an unbiased view on the value of both the use of fire to mitigate risks—through prescribed, indigenous, and controlled burning—and to allow some wildfires considered to be low risk to run their natural course. This path also requires identifying the most at-risk ecosystems from large and severe wildfires and prioritizing mitigation measures to balance their impacts.

Finally, the third and last question is: how to deal with post-fire restoration when the first two strategies have not worked? According to the Spanish National Ecosystem Assessment (SNEA), in Spain, 45% of the ecosystem services assessed have been degraded or are being used unsustainably, with regulating services being the most affected. One of the main drivers of degradation has been large, high-severe wildfires.

#### **6 Conclusions and Final Remarks**

Post-fire restoration aims to mitigate or reverse the negative ecological and socioeconomic impacts of fire. These impacts are related to the fire regime and its interactions with ecosystem resilience to fire. In the case of severe fire regimes, the main environmental impacts affect nutrient balance, soil erosion risk, and biodiversity reduction. Post-fire restoration planning requires the identification of specific fire-triggered degradation processes, including their temporal and spatial dimensions, and vulnerable ecosystems. Restoration should address the identified vulnerable areas and mitigate the risk of soil erosion and runoff in the short term, as well as the recovery of nutrient cycling and key plant species in the long term (Vallejo & Alloza, 2015).

An important decision in post-burn forest management, when restoration of the old forest type is the primary objective, is whether to use natural regeneration (indirect restoration), if present or expected to occur, or active restoration (plantings and seeding). Depending on the objectives, this may involve thinning, shoot selection, and control of unwanted vegetation. The costs associated with assisted natural regeneration can be much lower compared to active restoration, meaning that a much larger area can be effectively treated with a similar amount of available funds. Of course, the decision of active *versus* natural restoration will be conditioned by vegetation type, ecosystem response, burned area objectives (Mola et al., 2018), and the societal needs of these burned landscapes.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Forest Ecosystems, Forest Fire Internet of Things (FFIoT), and Socioeconomic Aspects**

**Asimina Skouteri, Konstantinos Spanos, Peristera Kourakli, and Panagiotis Koulelis**

**Abstract** Forests play a significant role in any circular economy strategy aimed at achieving sustainable development, human well-being, and national welfare. Forest fires are responsible for substantial losses in forest ecosystems and the valuable ecosystem services they provide. The Internet of Things (IoT) constitutes a collaborative ecosystem comprising smart devices, networking infrastructure, and advanced processing technologies that work together to create smart environments for endusers. This revolutionary technology ensures continuous access to information and facilitates the integration of people and data, contributing to a greener future. It offers effective methods and substantial technical support for forest and environmental sciences and sustainable forest planning and management. Despite the transformative impact of technology in various sectors, the forestry industry has been slow to embrace digital technologies. IoT can be effectively designed and implemented across all phases of forest fire management, transitioning it from a manual system to a digital one with widespread remote participation and governance. This transformation results in the development of more resilient forest landscapes in the face of climate change and external disasters. Achieving a consensus on measures, including hardware, software, and skill requirements, is crucial for ensuring effective information provision. Additional research methods and approaches are needed to address emerging economic, environmental, and social challenges, and there must be a general agreement on what aspects to measure and how to measure them.

**Keywords** Internet of Things (IoT) · Forest fires · Sustainable development · Resilient landscapes

A. Skouteri (B) · P. Koulelis

Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization—Dimitra, 11528 Athens, Greece e-mail<EMAIL_ADDRESS>
K. Spanos Forest Research Institute, Hellenic Agricultural Organization—Dimitra, Thessaloniki, Greece

P. Kourakli State Forest Service of Thessaloniki, State Forest Service, Thessaloniki, Greece

#### **1 Introduction**

Historically, the concept of IoT has its roots in the ancient Beacon Tower system. This system was employed in China and other countries to rapidly warn about unexpected fires on hills or towers as an early alert for potential enemy invasions (Zhao et al., 2013). IoT is an emerging and innovative technology, and many definitions and frameworks have been studied, analyzed, developed, and proposed. Generally, the architecture framework of IoT includes four key layers: (a) a device layer, (b) a network layer, (c) a data analytics layer, and (d) an application layer (Sahal et al., 2021; Singh et al., 2021). IoT-based technologies find applications in various aspects and areas of life and science (Maksimovic, 2018; Nižeti´c et al., 2020). The primary objective of this revolutionary technology is to enhance the quality of life by transforming processes in different fields. Figure 1 illustrates the main IoT-related applications related to forest topics.

According to the Food and Agriculture Organization (FAO), a forest is defined as "a land area of more than 0.5 ha, with a tree canopy cover of more than 10%, which is not primarily used for agricultural or other non-specific non-forest purposes" (Foundation, C.D.R., 2020). As of 2016, the total global forest area was estimated at 39,958,245.9 km2 (Forest Area, 2022).

Forests are recognized as dynamic providers of ecosystem services (Spanos et al., 2021), and any significant disruption to their existing structure can have various impacts on different aspects of human well-being. Forest fires, along with insect infestations and forestland fragmentation, are among the major causes of unsustainability. Conventional forest fire management (FFM) often lacks coherence and efficiency, hindering the development of an integrated approach and complicating decision-making processes, resulting in the inefficient use of human and material resources. The primary objectives of integrated FFM are twofold: (a) to minimize the damage caused by unintentional fires and (b) to maximize the benefits of intentional, prescribed fires (Nyongesa & Vacik, 2018). Prescribed fires offer several positive effects, such as controlling insect pests, reducing competition for nutrients,

**Fig. 1** Applications of Internet of Things (IoT), Environmental Internet of Things (EIoT) and Forest Internet of Things (FIoT) (Asghari et al., 2019; Bayne et al., 2017; Bo & Wang, 2011; Choudhry & O'Kelly, 2018; Hock et al., 2016; Li et al., 2019; Zhao et al., 2005)

and providing habitats for nesting birds, making them an integral component of FFM due to the ecological benefits that often outweigh their negative effects.

It is widely acknowledged that an integrated, sustainable, human-centered, and proactive approach to FFM should address the root causes of extreme forest fires (wildfires) (Wunder et al., 2021). Many international initiatives have been launched in relation to forest land restoration, which are highly connected with the main goals of an IoT ecosystem:

IoT is a new technology around the globe and has the potential to contribute to socially and ecologically sustainable forestry. Regarding the agriculture sector, the social impacts and inequalities linked to digital agriculture need to become a key concern of policy makers so that the above-mentioned potential will be determined (Hackfort, 2021). Agriculture is undergoing high transformation due to the technological assets. On the other hand, forestry is running behind the majority of the industries that have already adopted that practice (Choudhry & O'Kelly, 2018). This has recently started to change. In forestry, digital technologies confront a system which has been established for a long time—76% of forests globally are public-owned with the remainder being held by holders of average less than one-hectare land. Management of state or public forests is usually conservative and private forests lack scale and expertise needed for adoption of new technologies (Choudhry & O'Kelly, 2018). Forest Internet of Things (FIoT) is a category of IoT and refers to smart devices distributed in forests mainly for monitoring, management, fire detection, and prevention (Sahal et al., 2021).

Figure 2 illustrates an FFM framework that highlights the primary facets and associated activities (pre-, at-, and post-fire management) where IoT can serve as a valuable tool. Numerous suggestions emphasize the urgent need for a digital forest system. Choudhry and O'Kelly (2018) have emphasized that there is no time to waste, and action should begin immediately, rather than waiting for technology to fully mature. Therefore, it is imperative to develop a roadmap by initiating smallscale projects initially and subsequently expanding them on a larger scale. However, it is important to consider a structure that can mitigate operational and financial risks. Additionally, Nassef (2022), a leading figure in climate adoption policy at the United Nations, noted in an interview that the substantial transformation of our actions over the next decade, including the adoption of digital technologies, is crucial to preventing human extinction.

**Fig. 2** A conceptual fire management framework (Daskalakou et al., 2014; Mavsar et al., 2011; Nyongesa & Vacik, 2018; Salam, 2020)

### **2 IoT Technologies in FFM—Forest Fire Internet of Things (FFIoT)—Socioeconomic Aspects**

Wildfires are climate-related events with significant impacts on both climate change and human well-being. Technology plays a crucial role in understanding the causes of "green swan" events, providing innovative solutions to address them. Emerging technologies like the Internet of Things (IoT) and artificial intelligence (AI) have become valuable and powerful tools for responding promptly, efficiently, and innovatively to these situations (Nassef, 2022).

Remote participation in forest management can give rise to specific dynamics of power and politics, as forests are increasingly viewed as global resources (Gabrys, 2020). Figure 3 presents a visual example of Forest Fire Internet of Things (FFIoT) in a forest landscape. Various smart devices are collecting various types of data and communicating wirelessly through a gateway. The messages received by the base gateway are stored and then transferred to an online database. This collected information is readily accessible for further analysis and informed decision-making. Figure 4 provides a detailed breakdown of the four layers of FFIoT for end-users.

**Fig. 3** A diagram of architecture of FFIoT (Icons were inserted from painting and draw.io software)

**Fig. 4** A conceptual framework of FFIoT

#### **3 Related Work—Review**

We conducted this review and analysis based on the principles of forest fire management (FFM) and IoT (Internet of Things). We compiled an indicative list of Forest Fire Internet of Things (FFIoT) articles published from the beginning of 2017 through May 2022, using various databases. The search was concluded in May 2022. The keywords employed for the search included "Forest Internet of Things," "Smart Forests," "Digital Forestry," and "Forestry 4.0." We focused on studies primarily related to forest fires. While this list is not exhaustive, it provides comprehensive coverage of the topic in terms of geography and ecology, highlights key findings, addresses gaps in the literature, and outlines future challenges in the field.

Addressing the critical issue of air pollution resulting from wildfires, Mahajan et al. (2017) developed a framework utilizing IoT for predicting the concentration of particulate matter with a size of 2.5 µm or less (PM2.5), a significant air pollutant released into the atmosphere following a fire. These particles have severe adverse effects on human health, and this innovative study aims to achieve highly accurate predictions to mitigate potential health risks. The study utilized data from 119 Airbox Devices stations in Taiwan for experimentation and evaluation. Prior studies had only presented a generic model applicable to all stations. This limitation was overcome by introducing the concept of clustering monitoring stations into grids based on their distances from each other. The results have the potential to enhance air quality monitoring systems, support local and national environmental objectives, and contribute to meeting sustainable development goals (SDGs).

Dubey et al. (2018) introduced a cluster-based approach for forest fire prediction in Spain, leveraging sensor network technology. This approach involved three key steps: (a) Collecting forest fire images from sensors distributed across various geographical areas; (b) Analyzing these images using a clustering algorithm designed for forest fire prediction. This algorithm grouped clusters based on different colors and textures corresponding to specific locations; (c) Implementing a system for early forest fire detection, minimizing response time by calculating the average value of cluster trees within the sensor network.

By upgrading the existing wireless sensor network (WSN) fire detection approach, this system achieved a reduction in false alarms. The hardware components of this system included:

This application offers several advantages:

. Early detection of fires with the ability to promptly inform authorities.

New findings from this approach include the implementation of a highly accurate fire prevention and detection system, significantly reducing false alarms. This method outperformed satellite-based forest fire detection in terms of effectiveness. Early and accurate wildfire detection plays a crucial role in limiting the fire's extent, and the user-friendly model enhances coordination efforts.

In the same year, Toledo-Castro et al. (2018) developed a forest fire controller based on a fuzzy logic model. The primary objective was to analyze environmental information, including meteorological data, oxygen levels, and polluting gases, to rapidly assess potential forest fire risks and detect ongoing fires. They also introduced decision-making methods aimed at reducing real-time analysis of environmental data. The proposed wireless sensor network (WSN) had the capability to conduct environmental monitoring, including parameters such as temperature, humidity, wind speed, rainfall, carbon dioxide and monoxide levels, and oxygen levels. This system was built upon a prototype of an IoT device distributed across various forest areas. This innovative approach to designing an effective environmental monitoring network, capable of providing scientific data essential for ecosystem management and decision-making processes, addresses one of the key challenges in environmental monitoring (Li et al., 2019). Moreover, there is potential for improvement by incorporating new sensors into the system.

Choudhry and O'Kelly (2018) highlighted that fire monitoring is among the 15 promising practices within the technological landscape of precision forestry, encouraging the participation of forestry companies. To illustrate how forest fires are perceived by society and the resulting chain reactions, they focused on the 2017 wildfire in South America, which resulted in forest owners losing at least four percent of their forests. In response to these losses, owners are now exploring ways to enhance detection and control systems using digital technologies. Regarding national parks, Peinl (2020) introduced the design and implementation of a novel, flexible, and open architectural system called ASPires, aimed at early prevention and detection of forest fires. ASPires evaluated the capabilities of a new low-power LoRa wireless technology, capable of covering long distances of up to 15 km. The system was tested for viability, accuracy, and effectiveness in three national parks: (a) Pyrin, Bulgaria; (b) Mavrovo, North Macedonia; and, (c) Pelister, North Macedonia. The system included the following components: (a) sensors for measuring temperature, air and soil humidity, carbon dioxide and monoxide levels, dust particle concentrations, wind speed and direction, and noise levels; (b) a power regeneration component or rechargeable battery; and, (c) an optional local data storage facility and wireless transmission technology. The key advantages of this system were:

These advancements represent significant progress in forest fire prevention and mitigation efforts.

In the same year, Srividhya and Sankaranarayanan (2020) recognized the need for periodic monitoring and surveillance of forest fires in vulnerable areas due to the accelerating impact of global warming on the frequency and severity of forest fires. In their work, they proposed an IoT-Fog-based framework for forest fire monitoring systems, with the goal of addressing limitations in other wireless sensor networks. The IoT-Fog framework allows for the balancing of computing and data analysis operations, distributing these functions across aggregator and central cloud layers. The cloud component plays a pivotal role in managing all fire-related notifications, issuing alerts to forest offices and individuals in the surrounding areas. Some of the main advantages of this system include low bandwidth usage, reduced latency, and the ability to handle heterogeneous data computation effectively. Future challenges in this sector include optimizing energy consumption and strategically placing Fog nodes to enhance the system's efficiency and coverage.

Post-fire forest management can benefit from the adoption of an alternative approach to reforestation, complementing the traditional method of manual planting (Gabrys, 2020). While the traditional method remains significant and widely practiced, emerging digital technologies offer the potential to plant billions of trees annually through aerial techniques involving drones and the mass planting of polycultures (mixed forests) using smart machines capable of separating various species. Additionally, planting at scale from the sky is being explored (Nassef, 2022), among other innovations. The idea that forests can transform into digital environments managed through technology to address mitigation, climate change, and environmental challenges has the potential to shift the paradigm in forest management. This vision is not distant and may become a routine practice in future forest management, accelerating the adoption of alternative reforestation methods.

A novel methodology for forest fire warning based on IoT was designed and implemented in the Jizhou forest district of China by Han (2021). This forest plays a crucial role as the primary provider of ecosystem services for the nearby major cities of Beijing and Tianjin. The system consisted of three key components: sensing, transmission, and application. The application layer of the system enabled rapid and effective communication with the fire-affected region. In general, the forest fire warning system was complex and required professional expertise and scientific input. The technical aspects involved the use of smart devices for data acquisition, transmission, control, display, monitoring, and management. This innovative system has the potential to be implemented not only in remote forest areas but also in urban and peri-urban forests, enhancing the capacity to detect and respond to forest fires effectively.

In their comprehensive review, Sahal et al. (2021) outlined the transition from Industry 4.0 to Forestry 4.0, facilitated by advanced technologies like the Internet of Things (IoT). This transition is expected to bring about social, environmental, and economic benefits, and the review provided a roadmap for this transformation. Forest fires are recognized as one of the major risks negatively impacting the environment and are a significant dimension of Forestry 4.0. Given the limited practical studies available, the review emphasizes the research gap regarding the potential adoption of the Internet of Forest Things (FIoT) for environmental sustainability. It particularly focuses on forest fire management and detection, considering it a central component of Forestry 4.0.

Singh et al. (2021) introduced a novel IoT system designed for forest fire detection and prediction in India, aiming to address previous limitations such as connectivity, edge device fire detection, and power consumption. The proposed framework improved the short-term estimation of forest fire risks and the early detection of fires through the utilization of LoRa communication and edge computing-based gateways. To detect the emergence of fires, sensor nodes with integrated sensors were deployed. These sensors measured various parameters, including temperature, humidity, light intensity, rainfall, wind speed, and infrared values. The system was powered by a battery, and local storage was available for data backup. Additionally, a computer vision node (CVN) worked in tandem with the sensor node to provide precise information.

Ntinopoulos et al. (2022) emphasized the connection between the Fire Weather Index (FWI) and climate conditions in Greece. They utilized datasets derived from daily wind speed, precipitation, relative humidity, minimum, mean, and maximum temperature, as well as solar irradiance. Regions with high FWI, indicating drier and hotter conditions, exhibited a higher likelihood of experiencing more forest fires. Meteorological information was instrumental in assessing the fire danger classification in these areas. Maintaining, expanding, and connecting databases could prove to be a valuable tool for predicting forest fires at regional, national, and global levels.

In a recent analysis, the focus was on biodiversity products that can be retrieved from space using available space assets. Among these products, those describing the abiotic drivers of ecological disturbance, including the biological effects of fire disturbance, were ranked as some of the most important products in terms of relevance, feasibility, accuracy, and maturity (Skidmore et al., 2021). The study highlights the importance of detecting the planet's dramatic changes over decades rather than relying solely on periodic (annual or seasonal) observations to align with operational and research-oriented management and reporting needs. Ensuring the continuity of free and open data, affordability, and establishing links between in-situ and remote sensing observations are essential for this purpose.

#### **4 Conclusions and Challenges**

The potential for high-value creation through improved forest fire management is one of the key environmental advantages of Forest Fire Internet of Things (FFIoT). It has the potential to enhance operational ecosystem monitoring, contributing to national and global ecological security (Li et al., 2019). The development of a monitoring system that combines remote sensing observations, in-situ measurements, and model simulations ensures high accuracy.  Additionally, FFIoT can improve health and safety by helping to avoid dangerous locations (Hock et al., 2016). From a societal perspective, FFIoT applications shift participation from local actors to a more global stage, promoting democratic engagement and including all relevant stakeholders, thereby contributing to diverse and inclusive forest management (Gabrys, 2020; Gabrys et al., 2022). This collaborative approach fosters the development of sustainable and durable databases, and digital technologies can serve as a catalyst for environmental initiatives and regulations, transforming and expanding previous practices and technologies (Gabrys et al., 2022). Moreover, technology adoption in environmental issues can motivate the business sector to become more involved due to economic benefits.

However, there are drawbacks to adopting these new technologies from an environmental perspective. IoT can accelerate the depletion of natural resources and increase the environmental footprint due to the production of electronic devices, leading to higher waste generation and emissions (Nižeti´c et al., 2020). Effective recycling methods and advanced e-waste management are essential for a greener future. High energy consumption also poses financial challenges, necessitating technical advancements like energy-efficient communication and long-term batteries. Conducting life cycle assessments (LCAs) or environmental impact analyses (EIAs) is critical to evaluating the environmental footprint of these new products.

From a socio-political perspective, digital technologies can exacerbate environmental injustices and inequalities, with privileged actors having easier access. The specialization of workers in response to changing job requirements can lead to social impacts, potentially affecting mental well-being, necessitating an emphasis on the development of soft skills. Preparing the new generation for the transition into the era of big data requires an educational structure. The next steps should focus on further developing technology for data collection, statistical tools for analysis, and suitable infrastructure for data management. Creating a list of selected parameters that can be adopted universally is crucial. Starting with small-scale FFIoT projects and gradually scaling up is advisable. Collaboration across disciplines, better-established research, and a focus on avoiding socio-political inequalities and undemocratic governance are essential (Gabrys, 2020). The alignment of legal frameworks and the development of digital forest legislation are also necessary.

Lastly, in the face of significant planetary changes in which wildfires play a crucial role, the urgency for institutions to recognize the value of "born digital data" and invest in standards for informative data records that contribute to the management and conservation of natural resources is paramount.

**Acknowledgements** The work presented in this paper was completed in the framework of COST Action CA 18135: Fire in the Earth Systems: Science & Society (FIRElinks). We express our gratitude to Danae Panagiotopoulou, head of the library of Institute of Forest Mediterranean Institute, for helping in bibliography.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# **Socioeconomic Impacts and Regional Drivers of Fire Management: The Case of Portugal**

#### **Joana Parente, Marj Tonini, Malik Amraoui, and Mário Pareira**

**Abstract** Wildfires are uncontrolled and unwanted fires that usually occur in forested/rural areas and burn forests, agricultural areas, and wildlands. Land abandonment, with the consequent growth of the rural–urban interface, increases the exposure and vulnerability of fire-prone regions around the World. In the last two decades, Europe experienced a high number of wildfires causing large burnt areas mainly concentrated in the Mediterranean Basin. This high fire incidence seems to be the result of human activities including land use/land cover changes, but also of climate variability and change. In the present study, we analyse the current situation in Portugal, which is the European country with the highest total number of wildfires and the second-highest total burnt area. The spatial and temporal variability of the wildfires within the country is very heterogeneous, due to the human and biophysical drivers. In this regard, four main aspects are considered and discussed: (1) the spatial and temporal distribution of wildfires in mainland Portugal; (2) the main human and biophysical fire drivers; (3) socioeconomic impacts; and (4) the main strategies for fire risk mapping and management. The main results indicate high spatial heterogeneity of the fire incidence, with higher fire activity in the northern region than the southern region, mainly promoted by a higher irregular topography and significantly different types of climate and land use/land cover characteristics. We highlight how fire incidence is strongly dependent on many biophysical and human factors/drivers and the direct and indirect socioeconomic impacts of wildfires. Methodologies and indexes developed by Portuguese authorities to map fire risk and assess fire danger

M. Tonini

M. Amraoui · M. Pareira

Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

J. Parente (B)

cE3c - Center for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute; Faculty of Sciences, University of Lisbon, Lisbon, Portugal e-mail<EMAIL_ADDRESS>
Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland

M. Pareira Instituto Dom Luiz, University of Lisbon, Lisbon, Portugal

are described. The elements discussed in this chapter result from research and lessons learned in recent years on the fire regime in Portugal and Europe. These findings can contribute to improving forest, landscape, and fire management, in Mediterranean European countries which share similar characteristics.

**Keywords** Wildfire · Human drivers · Spatial patterns · Fire incidence · Biophysical drivers · Burnt area · Portugal

#### **1 Introduction**

Wildfires have been an essential component of Mediterranean ecosystems since 24 million years ago (San-Miguel-Ayanz et al., 2013). This area is highly prone to fires, mainly due to the vegetation heterogeneity and the favourable climate conditions characterized by wet and mild springs and hot and dry summers, increasingly affected in recent decades by heat waves and droughts that promote the thermal and hydric stress of the vegetation (Pereira et al., 2013). Human factors may also contribute to the extent of the fire incidence in the Mediterranean basin, such as the migration of rural populations to urban areas over the last 50 years (Bowman et al., 2011). Decreasing precipitation and increasing air temperature, combined with the abovementioned factors, increase the fire risk in the Mediterranean region, especially during the summer months (San-Miguel-Ayanz et al., 2013).

In the last two decades, wildfires have affected more than 6.2 million people and were responsible for more than 2400 fatalities, and losses with an amount of 61 billion euros around the world (Wallemacq & House, 2018). Extreme wildfire events with catastrophic consequences have become more frequent in the recent period. For example, in Portugal, wildfires of 2017 burnt 1.2 million hectares of natural land, including 25% of Natura 2000, causing 127 human fatalities and losses of about 10 billion euros (San-Miguel-Ayanz et al., 2018). Despite its smaller land area in comparison with the other European Mediterranean countries, Portugal is the European country with the highest total number of wildfires and the second-highest total burnt area (San-Miguel-Ayanz et al., 2018). The growth of the rural–urban interface has increased the likelihood of fire ignition by humans (Tonini et al., 2018). In addition, the rural abandonment contributed to the decrease in forest fuel consumption and the subsequent biomass accumulation (Pereira et al., 2014). The combination of these factors with the climate and weather conditions makes Portugal a highfire-risk area, especially in summer (San-Miguel-Ayanz et al., 2013). The variation in fire incidence is related to landscape characteristics that can act as biological, socioeconomic, and physical drivers (Parente et al., 2018c). Regardless of the fire cause (lighting, accidental, negligent, or intentional), wildfires may have significant impacts. Wildfire's impacts and characteristics highly depend on several human and biophysical drivers, such as (1) socioeconomic parameters (Oliveira et al., 2017; Parente et al., 2018c); (2) land and forest management practices (Parente et al., 2016); (3) topography (Fernandes et al., 2016b; Parente et al., 2018c); and (4) weather and climatic conditions (Parente et al., 2018a, 2018b, 2019). In this context, identifying the regions where those fire impacts and fire drivers have more influence is of greater importance for increasing the efficiency of fire management actions.

Following this line of reasoning, the present chapter aims to briefly discuss and characterize for mainland of Portugal (hereafter, Portugal): (1) the spatial and temporal distribution of wildfires; (2) the main fire drivers, causes, and impacts; and (3) the main fire risk management characteristics.

#### **2 Spatial and Temporal Distribution of Wildfires**

Wildfires display a complex spatiotemporal pattern strongly related to forest landscape and neighbouring anthropogenic developments, such as urban and rural areas, and road and pathways networks. Fire occurrences tend to present clusters (Vega Orozco et al., 2012), where events are found closer in space and time than expected for a random distribution. Thus, cluster analyses can be applied to investigate these patterns and to disclose vulnerable areas and frame periods where wildfires are more likely to occur (Vega Orozco et al., 2012).

Portugal can be divided by the Tagus River into two regions of about the same size: (1) the northern region has a temperate type of climate with dry and warm summer (Peel et al., 2007), and is characterized by an irregular topography, flat along the western coast and mountainous towards the central and eastern side (with a pick of 1993 m), the predominance of forest and semi-natural areas, and a dense river network; (2) the southern region has dry and hot summers (Peel et al., 2007), and is characterized by lowlands dominated by agricultural areas, with the only exception of two patches of mixed and broad-leaved forest extending near the south coast at mid-altitude (with a pick of 1000 m). Not surprisingly, the northern region is much more affected by wildfires than the southern half (Parente et al., 2016; Tonini et al., 2018). Also, considering the fire dataset available by the *Instituto da Conservação da Natureza e Florestas* (ICNF, https://icnf.pt/), before the 1980s, the burnt area had never reached 10,000 ha for a single occurrence. Instead, from the 1990s to nowadays (2021, the year of the last available burnt area dataset), this threshold has been exceeded several times, especially with the catastrophic wildfire events in 2003, 2005, and 2017. Still, it must be underlined that the number of wildfires has been steadily decreasing since the early 2000s, and this tendency is followed up by its decrease with a Daily Severity Rating above five (Observatório Técnico Independente et al., 2020).

Investigating Land use/Land cover changes occurring in Portugal for two decades (1990–2012), Tonini et al. (2018) revealed an increase in the rural–urban interface more pronounced in the northwest sector and along the coast, where the transition from heterogeneous agricultural areas to urban fabric was particularly intense. This development was mainly due to urban growth and the intensification of the road network. Comparing the evolution of the rural–urban interface with the mapped burnt areas, these authors discovered that the first increased by more than two-thirds, while the latter decreased by about one-third; nevertheless, the burnt area within the rural– urban interface doubled, highlighting the importance of monitoring this interface area for adequate fire management and prevention. Even if not included in the mentioned study, the recent extreme fire season of 2017, which burnt an area of 539,920 ha of forests, shrubland, agricultural land, and artificial surfaces in one hundred and fifty municipalities in the north-central Portuguese regions (San-Miguel-Ayanz et al., 2021), is highly representative of the same undergoing process. In recent studies (Comissão Técnica Independente et al., 2018; Observatório Técnico Independente et al., 2020; San-Miguel-Ayanz et al., 2021), the list of significant factors that can potentially increase the frequency of these extreme events includes not only extreme climate conditions, but also fire causes (e.g. arsonist), fuel management, land use/land cover changes, increasing depopulation in rural areas, and increase in the rural–urban interface.

A deeper investigation of the spatiotemporal distribution of wildfires in Portugal revealed to be fundamental to detecting over-densities and trends in fire risk, allowing to address prevention and forecasting measures. Indeed, in a fire-prone country like Portugal, where thousands of events occur each year, extracting useful information from the original raw data (i.e. mapped ignition points or burnt areas and related attribute tables) is a demanding task. In this respect, statistical methods initially developed for stochastic point processes have been successfully employed in the past (Parente et al., 2016; Pereira et al., 2015). In a recent study, Tonini et al. (2017) combined different methods to detect wildfire overdensities: (1) Geographically Weighted Summary Statistics (GWSS) to explore how the average burnt area varies in space; (2) the Cross K-function to assess whether or not burnt areas of different sizes are independently distributed; and (3) the 3D-kernel density estimator (3D-KDE) to elaborate smoothed maps representing the continuous spatial density distribution of wildfires and their temporal evolution.

The results for GWSS (Fig. 1 on the right) indicate regions with (1) local mean burnt area above 500 ha localized in the central regions in Portugal (districts of Coimbra, Castelo Branco, Santarém, and Portalegre) and along the southern coast (Faro District); (2) local mean burnt area below 200 ha quite dispersed in the southern half of the country; (3) meagre and low local means predominant and concentrated in the region between Bragança and Vila Real (to the east), between Porto and Viana do Castelo (to the west) and around Lisbon (to the centre). Additionally, hot spots of the high local mean for the burnt area were identified due to a few events with a very large burnt area surrounded by several events with a small burnt area.

The cross K-function results revealed the propensity of medium wildfires (15 ha ≤ burnt area < 100 ha) to aggregate around small wildfires (5 ha ≤ burnt area < 15 ha), while large wildfires (burnt area ≥ 100 ha) aggregated at a larger spatiotemporal scale, indicating a return period longer and more complex than for small and medium wildfires. In more detail, the cluster behaviour of small and medium wildfires increased with the distance, with picks of cluster every 3 and 4 years, while large wildfires resulted in being randomly distributed within a distance lower than 3 km and clustered above, with two picks of cluster each 6 and 10 years.

**Fig. 1** 3D-kernel density (on the left) and geographically weighted local means (on the right) of the burned area in Portugal for the period 1990–2013

The 3D-KDE results (Fig. 1 on the left) revealed the highest densities located in the northwestern area of Portugal, nearby the regions of Viana do Castelo, Braga, and Vila Real, and in the northern-central area, nearby the regions of Viseu, Guarda, and Castelo Branco. Lower spatial densities of wildfires were present in the southern coastal regions of Lisboa, Santarém, Leiria, and Coimbra. Paying attention to the wildfires' temporal evolution, the higher densities were registered between 1995 and 2004, while the lowest densities were disclosed in the two years, 1992 and 1993, characterized by a very low number of wildfires. The southern half of the country exhibited lower smoothed densities than the northern regions. In the central period (1998– 2006), medium–high densities were spatially homogeneously distributed, with the highest values in the period 2000–2004. Finally, the volume rendering technique was employed to elaborate 3D maps representing the spatiotemporal evolution of these smoothed densities into a unique map, allowing one to visualize at glance regions and frame periods with more clusters of wildfires.

We conclude by mentioning that the wildfires' spatial and temporal distributions are two factors strongly correlated, meaning that events closer in space are also closer in time, with an increased risk for emerging megafires events.

#### **3 Fire Drivers: Human and Biophysical Drivers**

It is easier to understand the high spatial and temporal variability of the fire incidence (number of wildfires and burnt area) in Portugal by discussing the influence of the role of human and biophysical drivers on this variability. This variability presents a south-north gradient with much higher values in the North region and high differences at national and regional scales. Additionally, understanding the role of human and biophysical drivers on this variability is of fundamental importance to support forest and fire management as well as the implementation of legislation. It is worth a mention that fire characteristics (e.g. size, intensity, duration, and behaviour) and their consequences highly depend on several human and biophysical drivers, including socioeconomic factors (Nunes et al., 2016; Oliveira et al., 2017), landscape and forest management practices (Godinho et al., 2016; Parente et al., 2016), topography (Fernandes et al., 2016b), organization, shape, size, and characteristics of patches of different types of vegetation cover (Bond & Keane, 2017; Jan et al., 2018), and finally weather and climate conditions (Parente et al., 2018b, 2019; Russo et al., 2017).

At the national level, in Portugal, about 85.5% of the forest areas are privately owned (Bouillon et al., 2020). The combination of rural land abandonment within these areas and their split of land ownership creates a complex landscape mosaic of neglected shrublands and small- to large-scale forest plantations (Bouillon et al., 2020). Also, the rural–urban interface has increased by more than two-thirds between 1990 and 2012 (Tonini et al., 2018). So, the combination of such factors with the fact that most of the fire ignitions are human intentional (Parente et al., 2018c) makes it no surprise that several studies have identified as the most critical drivers of fire incidence the population density and topography/landscape characteristics (Catry et al., 2007; Nunes et al., 2016). Additionally, Fernandes et al. (2016a) have shown that wildfires larger than 500 ha were increasingly controlled by fuel-related variables, such as fuel composition, fuel connectivity, and pyrodiversity. Also, they have found that climate-weather variables only have an importance of 15% as bottom-up variables on the influence of fire size. On the other hand, extreme weather conditions (e.g. drought and heat waves), in combination with high fuel load drive extremely large wildfires (burnt area ≥ 2500 ha). Nevertheless, it is important to highlight the following points (Parente et al., 2018c): (1) fire occurrence (total number of wildfires) increases with population density; (2) fire incidence decreases with the distance to the nearest road; (3) fire ignition is higher at a lower altitude, and in the artificial surfaces, pastures, and heterogeneous agricultural areas; (4) total amount of burnt area increase with altitude and is higher in agricultural areas, forests (mainly *Eucalyptus globulus* and *Pinus pinaster*), and semi-natural areas; (5) slope has the lowest influence on fire occurrence but has the strongest influence in the total amount of burnt area; and, (6) fire extent is essentially driven by fire weather conditions and ignition density (Davim et al., 2022). Finally, the extent of a large wildfire is mainly a function of human and biophysical drivers that land-use planning, forest, and fuel management can partially address (Fernandes et al., 2016a).

#### **4 Fire Causes and Fire Socioeconomic Impacts**

In the last decades, EU Member States have experienced critical fire events that have resulted in the loss of human lives and significant economic and environmental losses (San-Miguel-Ayanz et al., 2021), and Portugal is no exception. Portugal has been marked by substantial landscape transformation related to land cover/land use changes and rural depopulation, especially in its interior (Medeiros et al., 2022; Poeta Fernandes, 2019). Those depopulated areas have experienced a dramatic increase in biomass amount and continuity, contributing to the rise of new fire regimes (Pausas & Fernández-Muñoz, 2012), with even more frequent catastrophic wildfires events (San-Miguel-Ayanz et al., 2018). There is no significant cause–effect relationship between the atmospheric conditions favourable for fire occurrence and fire occurrence, in the sense that ignitions in Portugal are essentially due to human activity (Amraoui et al., 2013). Additionally, human ignition is the primary cause of wildfires nowadays, accounting for about 99% of total ignitions with known causes (Parente et al., 2018c). Nevertheless, the Portuguese *Instituto da Conservação da Natureza e das Florestas* (ICNF, www.icnf.pt) categorize fire causes in a three-level hierarchical structure, and each level identifies several major categories. The first level identifies six major categories of fire cause, namely: (1) use of fire; (2) accidental; (3) structural; (4) incendiarism; (5) natural, and (6) unidentified. The second level discriminates the causes of the previous one, identifying them in groups and discriminating against specific activities. Finally, the third level divides activities into subgroups and discriminates specific behaviours and attitudes leading to a total of 70 different fire causes. For the sake of simplicity, four major categories may cluster all this information, namely: (1) negligent wildfires that comprise the use of fire and/or accidental fire; (2) intentional wildfires that include structural fire and/or incendiarism; (3) natural wildfires; and (4) unidentified wildfires. Considering the temporal distribution of such categories between 2001 and 2014, they exhibit two different trends: the number of unidentified wildfires decreases with time, and the number of negligent and intentional wildfires increases (Parente et al., 2018c).

In Portugal, most wildfires are small (burnt areas below 100 ha), as a fire extinction/ exclusion policy prevails in the country (San-Miguel-Ayanz et al., 2013). However, some of these events escape to the initial fire attack and the subsequent firefighting operations, mainly of the times due to the lack of a proper number of firefighters, which might be the consequence of the tremendous amount of fire ignitions at the same time (Comissão Técnica Independente et al., 2018). Consequently, these wildfires may become large and impact a diverse range of economic, social, and environmental assets and values (Stephenson et al., 2013). Measuring these latter impacts is not easy due to the difficulty in obtaining measurable data that allow establishing scales of social, economic, or even emotional and family impacts (Viegas et al., 2017). Nonetheless, we can point to several direct socioeconomic impacts: (1) loss of human life, registering more than 110 fatalities, between civilians and firefighters, in the last 25 years in Portugal (Tedim et al., 2020); (2) short- or/and medium-term health issues due to smoke inhalation/intoxication, burns, wounds, and also mental health problems in civilians and firefighters; (3) buildings loss, such as houses and agroforestry assets (Tedim et al., 2018, 2020); (4) large expenditures on fire management, e.g. the joint budget of Greece, France, Italy, Portugal, and Spain is e2500 million each year, more focused on fire detection and suppression (Doerr & Santín, 2016).

An example of such extreme events was the unprecedented 2017 fire season, from which can be highlighted the wildfires from 17th June. Here, 64 people (between 5 and 88 years old) lost their lives, more than 200 injured people, and 458 structures were destroyed (San-Miguel-Ayanz et al., 2021; Tedim et al., 2018). Beyond the impacts described above, wildfires may also have several indirect socioeconomic impacts such as (1) reduction of tourism; (2) decrease of landscape and home values; (3) changes in soil physical and chemical properties (Jiménez-Morillo et al., 2016); (4) soil erosion (Parente et al., 2022); (5) pollution of water bodies (Basso et al., 2021; Pereira et al., 2016), which may change water quality and quantity, limit the consumption of water by humans even its disruption, and provoke the death of aquatic animals; and (6) increase of air pollution, which may contribute to premature deaths (Johnston et al., 2016).

#### **5 Fire Risk Mapping, Modelling, and Management**

Fire risk assessment on ecological, social, and economic systems is a fundamental tool for fire management and the development of cost-effective mitigation strategies. Fire risk can be evaluated using quantitative or qualitative indicators allowing the evaluation of the probability of an area being ignited by natural or artificial means in a certain period, eventually providing information on the expected positive or negative impacts in that area (Parente & Pereira, 2016). Fire risk is divided into two major components according to explanatory variables of control fire characteristics, namely: (1) a dynamic component related to variables that significantly change in time, such as weather and soil moisture conditions; (2) a structural component associated with static variables, which barely change in time, such as vegetation composition or topography.

In Portugal, the *Instituto Português do Mar e da Atmosfera* is a public institute integrated into the indirect administration of the Portuguese State that computes and publishes the Conjunctural and Meteorological Index (RCM) of rural fire danger (IPMA, 2020), which is used by firefighters and civil protection and fire management authorities. The Conjunctural and Meteorological Index is calculated daily and results from the combination of two indices: (i) the Fire Weather Index (FWI), which is updated once a day, and (ii) the rural fire hazard index, which includes a decadal structural component and an annual conjunctural component, that considers the burnt areas of the last three years, both under the responsibility of the *Instituto da Conservação da Natureza e Florestas* (ICNF, https://icnf.pt/). The integration of the two indices is performed by applying a risk weighting matrix at the pixel level of 1 km spatial resolution, and the agglutination by administrative unit (municipality and district) is carried out by weighting the highest risk values, considering the threshold of 20% of the most serious classes (IPMA, 2020).

The Fire Weather Index is the fire intensity index of the Canadian Wildland Fire Information System (Van Wagner, 1987) used worldwide at regional and global scales to assess the fire hazard (McElhinny et al., 2020; Vitolo et al., 2020). It comprises six sub-indices (three fuel moisture codes and three fire behaviour indices) calculated with observed and predicted values at 12 UTC (Coordinated Universal Time) of air temperature, relative air humidity, wind intensity, and accumulated precipitation in the last 24 h (Pereira et al., 2020). The Fire Weather Index is then classified into five classes (1-minimum to 5-maximum), and the annual conjunctural hazard is into five classes (0-zero hazard, which essentially corresponds to urban areas and water plans, to 5 maximum hazards) (IPMA, 2020).

The fire structural hazard map is based on the fact that the spatial distribution of fire incidence (namely of the burned area) is not uniform (Parente et al., 2016; Pereira et al., 2015; Tonini et al., 2017) and that the propensity of the fire depends on and can be modelled based exclusively on spatial data, namely slope, altitude, and land use and occupation (IGOT & pahl\_consulting, 2020; Pereira et al., 2014). Verde and Zêzere (2010) further details this fire susceptibility modelling and fire hazard mapping that form the basis of the structural fire risk mapped for mainland Portugal by Parente and Pereira (2016). In this study, the adopted concept of fire risk includes two components: (i) the fire hazard, defined as the probability of an area being affected by a fire during a specific period, comprising the susceptibility of that area and the probability of occurrence; and (ii) the potential damage, which accounts for the vulnerability of an area to the fire and the economic value of the damage caused. In a recent study, the statistically-based approach developed by Verde and Zêzere (2010) has been compared with a stochastic approach (Leuenberger et al., 2018), opening the way for further implementation of Machine Learning based approaches for fire susceptibility mapping (Tonini et al., 2020; Trucchia et al., 2022).

Other relevant approaches to evaluate fire risk include coupling fire behaviour modelling and stand characteristics (Botequim et al., 2017) or with a social-ecological approach (Tedim et al., 2016). Like other fire hazard and fire risk assessments, the Conjunctural and Meteorological Index integrates dynamic and structural variables observed in loco or remotely using geographic information system technologies (Caetano et al., 2004; Chuvieco et al., 2010). The fire structural hazard map was developed solely based on altitude, slope, land use, and occupation. However, the explanatory and predictive value of a much broader set of population, biophysical, and socioeconomic spatial variables has been demonstrated (Oliveira & Zêzere, 2020; Oliveira et al., 2012; Parente et al., 2018c) which has already led to a reassessment of fire susceptibility and hazard for mainland Portugal (Oliveira et al., 2021).

#### **6 Conclusion**

This chapter was devoted to the characterization of socioeconomic impacts and regional drivers using the example of Portugal. To this end, we have presented and discussed: (1) the use of several simple statistical methods to characterize wildfires' spatial and temporal patterns at the national scale; (2) the main fire drivers, causes, and impacts; and (3) the main indices used and for fire risk assessment and management in the country.

We can conclude that: (1) statistical methods developed for spatial and temporal stochastic point processes allow to highlight of local fire over-densities and mapping them; (2) despite the methodology used, the distribution of wildfires is very heterogeneous in Portugal; and (3) the fire incidence patterns and the ignition dates are strongly dependent on many human and biophysical drivers strongly. Characterizing the socioeconomic impacts of wildfires remain a challenge, especially in light of the current climate changes that increase the fire risk, especially the occurrence of megafire events in fire-prone countries such as Portugal. Finally, we discussed the fire risk mapping in Portugal resulting from the combination between the fire weather danger index and the fire hazard index. The resulting maps can be used to implement and support educational activities, awareness-raising initiatives, and prevention campaigns, as well as to support planning for better allocation of monitoring systems and firefighting.

#### **References**

fire regimes on Earth. *Journal of Biogeography, 38*, 2223–2236. https://doi.org/10.1111/j.1365- 2699.2011.02595.x

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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# **Regional Issues of Fire Management: The Role of Extreme Weather, Climate and Vegetation Type**

#### **M. G. Pereira, J. P. Nunes, J. M. N. Silva, and T. Calheiros**

**Abstract** It is of paramount importance to discuss and reflect on the influence that climate and land management have on fire regimes and, consequently, on the regional character of fire management. Our focus is on the Iberian Peninsula, although the results and conclusions presented here are common to many other regions of the world with a Mediterranean climate. The discussion is based on the concept of risk, starting with conjunctural risk factors, moving on to structural factors and including their interconnection. Conjunctural risk factors of weather and climate are discussed in the context of requirements for fire to occur, which include the existence of fuels, a state of dryness suitable for combustion and a source of ignition. The influence of climate and weather is discussed at different space–time scales and related to characteristics of vegetation and land management. The roles of atmospheric patterns, types of weather, extreme events, meteorological hazard indices in patterns of fire incidence, their grouping and definition of pyro-regions are presented. Within structural factors, human populations shape fire regimes by changing the ignition patterns, fighting wildfires, modifying landscapes (e.g., urbanization, deforestation and afforestation) and changing land management. Finally, the interconnection between these risk factors in the context of global changes, in particular

M. G. Pereira (B)

Instituto Dom Luiz, University of Lisbon, Lisbon, Portugal

J. P. Nunes

J. M. N. Silva

Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro, Vila Real, Portugal e-mail<EMAIL_ADDRESS>
J. P. Nunes · T. Calheiros Faculdade de Ciências, cE3c: Centre for Ecology, Evolution and Environmental Changes, Universidade de Lisboa, Lisboa, Portugal

Soil Physics and Land Management Group, Wageningen University and Research, Wageningen, Netherlands

Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal

J. Rodrigo-Comino and L. Salvati (eds.), *Fire Hazards: Socio-economic and Regional issues*, https://doi.org/10.1007/978-3-031-50446-4\_15

climate change, and the challenges they pose to the regional landscape, forest and fire management are highlighted.

**Keywords** Fire management · Fire risk · Structural/conjunctural risk factors · Extreme fire weather · Climate · Land management · Vegetation type

#### **1 Introduction**

Wildfire risk mapping is a key determinant tool for fire management because is closely associated with socioeconomic impacts. Fire risk can be defined as the combination of fire hazard, which is the probability of a region being affected by a fire in a given period, which depends on the probability of occurrence and the susceptibility of the region; and potential damage, which depends on the region's vulnerability to fire and the economic value of the damage caused by the fire (Parente & Pereira, 2016).

The concept of fire risk can be divided into two components: one structural and the other conjunctural (Bergonse et al., 2021; Lourdes & Pessanha, 2009; Verde, 2010). The first has to do with essentially stationary variables or variables that vary in the long term, such as topography, land use/land cover (LULC), population or socioeconomic activities. The second depends on variables that vary in the short term, such as atmospheric conditions, in particular the occurrence of extreme events such as drought and heat waves (HW).

Weather monitoring (e.g., drought) and forecasting (e.g., HW in the short term and drought in the medium term) and, consequently, the assessment of meteorological fire danger/hazard are key components of fire management, namely for short-term fire weather risk mitigation, resource allocation and a better understanding of wildfire dynamics for active firefighting planning.

On the other hand, the type of vegetation plays a key role in the structural component of fire risk and, therefore, of fire management. For example, the wildfire risk map in Portugal includes LULC as one of the risk factors and the fire weather index (FWI) of the Canadian Forest Fire Weather Index (FWI) System (Van Wagner, 1987), one of the most used fire hazard indices in the world, is based on the relationship between atmospheric conditions and vegetation (IPMA, 2020). The role of vegetation type is fundamental both before and after a fire, whereas (agro)forestry management activities (preventive silviculture and restoration) also play an equally fundamental role in the risk and management of fires.

All these aspects have an essentially regional character, as the spatial distribution of fires, vegetation type and atmospheric conditions are not uniform across the territory, but are characterized by great diversity and even clustering of events (Parente et al., 2016). This chapter aims to describe the role of climate, atmospheric conditions and vegetation in the danger and risk of fire and, consequently, in the management and socioeconomic impacts of fires. The focus will be on the Iberian Peninsula (IP) and Portugal in particular, as Portugal and Spain are the most affected countries in Europe both in terms of the number of wildfires (NW) and burned areas (BA). This region is a case study characteristic of the Mediterranean basin and regions of the world with a temperate/Mediterranean type of climate with dry and hot summers. The Mediterranean basin is also a hot spot for studies of climate change impacts, adaptation and vulnerability (Paeth et al., 2017; Tuel & Eltahir, 2020).

#### **2 The Role of Climate, Weather and Extreme Events**

The climate and atmospheric conditions are decisive for the occurrence of a wildfire that requires the existence of fuels, an adequate state of dryness and an ignition source (Ventura & Vasconcelos, 2006; Whitlock et al., 2010). In summary, climate determines the existence, type and life cycle of the vegetation; weather determines the state of the fuels and influences all stages of the fire; and, both, explain the spatial–temporal patterns of fire incidence (NW and BA) that are observed at global and regional scales (Pereira et al., 2019).

Climatic elements, such as surface air temperature and precipitation, determine the water availability to plants and the spatial distribution of the world's vegetation, major biomes and ecoregions (Kelly & Goulden, 2008; Woodward & Williams, 1987; Woodward & Woodward, 1987). The role of climate is also revealed by the seasonality observed in the vegetative cycle and, consequently, in the incidence of fire (Accatino & De Michele, 2013; Alvarado et al., 2017; Giglio et al., 2015; Krawchuk et al., 2009; Le Page et al., 2008; Saha et al., 2019). For example, the Mediterranean climate type is characterized by a rainy and mild period, which promotes the development of vegetation, and a hot and dry period, which promotes the thermal and hydric stress of the vegetation, when wildfire weather danger is higher (Parente et al., 2016; Pereira et al., 2005; Trigo et al., 2016a).

If climate explains the large-scale patterns of the spatial–temporal distribution of vegetation and fire incidence, the climate variability and weather types explain the characteristics of the fire regime at local and regional spatial scales, and shorter temporal scales (Dwyer et al., 2000; Rodrigues et al., 2020; Trigo et al., 2016a; Turco et al., 2018). At typical wildfire time scales (hours to days), atmospheric conditions play an admittedly important role in all fire phases (Calheiros et al., 2022; Pereira et al., 2019).  Wind, humidity and air temperature are important drivers of fire propagation and behavior (Benson et al., 2008; Pastor et al., 2003), while precipitation and air humidity help fight and extinguish fires (Awad et al., 2020).

 For example, several studies show that about 2/3 of the interannual variability of the burned area in IP pyroregions is explained by atmospheric conditions (Pereira et al., 2005; Sousa et al., 2015a; Trigo et al., 2016a). In each region, the fire size distribution tends to be highly skewed to the right, which means the existence of a high number of small wildfires and a few numbers of extreme wildfires that tend to be responsible for the majority of the total burnt area. In the case of Portugal, 10% of the largest wildfires account for 80% of the total burnt area (Pereira et al., 2005).

A power law can be fitted to the heavy-tail wildfire size distribution and help to illustrate and explain the rarity of large fires (Kanevski & Pereira, 2017; Malamud et al., 1998; Telesca & Pereira, 2010). This distribution reveals the existence of different classes of fires, for which the fire propagation is dominated by different atmospheric/climatic conditions: drought is responsible for the propagation of large fires, the relative humidity of the medium fires, and the wind governed the propagation of smaller fires. These results are consistent with the idea that fire propagation involves limits of scale, with small-scale drivers allowing fires to propagate after ignition, but limiting further spread only when large-scale drivers exist (Slocum et al., 2010).

For regional hazard assessment and fire management support, the influence of atmospheric conditions on fires has been studied and objectively quantified in several ways, including hazard indices and meteorological fire risk (e.g., San-Miguel-Ayanz et al., 2003); fire propagation and behavior models (e.g., Pastor et al., 2003); burnt area models (Pereira et al., 2013; Sousa et al., 2015b; Trigo et al., 2016b); identification of atmospheric patterns and weather types associated with high fire incidence (Amraoui et al., 2015; Rodrigues et al., 2020); assessment of the role of extreme meteorological (e.g., HW, storms) and climatic (e.g., drought) events.

HW and droughts are the main climatic drivers of fire incidence and, in particular, extreme fires (Pereira et al., 2005). For this reason, the HW characteristics (frequency, duration, seasonality and intensity) and their influence on extreme wildfires (wildfires with BA ≥ 5000 ha) in mainland Portugal were evaluated for recent past and future climate conditions (Parente et al., 2018). About 130 HW were identified in 1981–2010, between May and October, but concentrated in July and August. HW characteristics show great interannual variability, clearly associated with the temporal and spatial distribution of extreme wildfires: 97% of the total number of extreme wildfires were active during an HW, 90% of the total days of extreme wildfires were also HW days; 82% of the extreme wildfires had duration contained in the duration of an HW; and 83% of extreme wildfires occurred during and in the area affected by HW. Results also show that HW should increase in number, duration and amplitude, most significantly for RCP 8.5, and the end of the twenty-first century. The results of this study should support the definition of climate change adaptation strategies for fire hazard and risk management.

Other unsuspected extreme weather events can promote extreme wildfires. On October 15, 2017, after an exceptionally hot and dry season, with unusually intense droughts and heat waves, the fire weather was extreme and fuel moisture very low when the tropical storm (former hurricane) Ophelia passed along the west coast of Portugal, promoting strong south winds over the mainland, advected hot and dry North African air, increased the rate of fire spread and growth and gave rise to several mega fire events (Augusto et al., 2020; Castellnou et al., 2018; Sánchez-Benítez et al., 2018; Turco et al., 2019).

Recently, a study characterized the drought regime in the climatic conditions of the recent past (1981–2017) using various drought indices (Standardized Precipitation Index SPI, Standardized Precipitation Evapotranspiration Index, SPEI, Reconnaissance Drought Index, RDI and Vegetation Condition Index, VCI) and evaluated the influence of drought on the occurrence of extreme wildfires (Parente et al., 2019). Results reveal that: all extreme wildfires occurred during the drought evaluated with SPI or SPEI; more than 95% of the number of extreme wildfires and 97% of the burned area occurred during the drought evaluated with one of these indices; and 85% and 87% of extreme wildfires occurred in drought-affected areas evaluated with SPI or SPEI, respectively. The relationship between drought and fire incidence is statistically significant for 3-month SPI, 3-month and 6-month SPEI, and particularly strong for moderate and severe drought. It is not clear which is the best index, but drought is decisive for the occurrence of large wildfires.

The evaluation of fire weather danger and risk is useful to support firefighters and fire management stakeholders in several activities. The Canadian Forest FWI System (Van Wagner, 1987) evaluates the severity of fire weather conditions and comprises: three indices to numerically rate the fuel moisture in three relevant forest fuel layers, including the Drought Code (DC), which assesses the effect of drought on forest fuels; and four relative indices of fire behavior, including the Fire Weather Index (FWI) and the Daily Severity Rating (DSR) (Wotton, 2009). The DSR rates the difficulty of controlling fires because it reflects more accurately the expected effort required for fire suppression (Pereira et al., 2013; Wotton, 2009). The FWI System rates the relative fire potential just based on weather data (Stocks et al., 1989), namely daily values registered at noon of four meteorological variables, namely air temperature, relative humidity, wind speed and daily accumulated precipitation. The FWI System was developed for Canada, but for a common standardized forest type and is globally and regionally computed and extensively used to rate the fire weather danger, especially in the Mediterranean basin (Bedia et al., 2012; Flannigan et al., 2016; Guenni et al., 2022; Pereira et al., 2013; Silva et al., 2019a, 2019b; Vitolo et al., 2019).

A cluster analysis carried out on the normalized burnt area revealed the existence of four pyroregions in the IP (Fig. 1), namely in the N—North, NW—Northwest, SW—Southwest and E—East (Sousa et al., 2015a; Trigo et al., 2016a). The four pyroregions differ in the intra-annual variability of the normalized burnt area, namely by the existence and dimension of peaks and month of occurrence of maximum values (Fig. 2). A recent study repeated the analysis for a longer period and, despite confirming the existence of the four pyroregions, revealed differences in the administrative regions that constitute them (Calheiros et al., 2020). The authors calculated the Number of Extreme Days (NED), defined as the days with DSR and DC above the 95th percentile, and found a relationship between the intra-annual variability of the NED and the normalized burnt area, with high and statistically significant correlation, in each pyroregion and capable of explaining the changes in pyroregions. Recent changes in the intra-annual variability of the normalized burnt area were observed in some border provinces of the pyroregions, associated with the NED and confirmed that large values of BA are strongly linked to extreme weather conditions.

This robust link was used to project the future pyroregions of the Iberian Peninsula, using simulated data from a wide ensemble of climate models for two scenarios (RCP4.5 and RCP8.5) to estimate future FWI indices (namely DSR and DC) and NED (Calheiros et al., 2021). Results revealed a significant increase of the NED, especially in the south and central Iberia, for the end of the century and the RCP8.5 scenario, mostly due to significant increases in the DC, associated with the substantial

**Fig. 1** The pyroregions identified in the Iberian Peninsula in the period 1980–2015 (NW in blue, N in green, SW in yellow and E in brown), the administrative regions are part of them (right panel) and the monthly averages of the normalized burnt area, computed for each month and the administrative region as the quotient between the monthly burnt area and the area of the administrative region (in per mileage) for the period (right panel)

decrease in precipitation and increase in air temperature. Changes in NED intraannual pattern should drive the pyroregions' future configuration and it is related to changes in future climate types (Beck et al., 2018). Pyroregions will move northward: The N pyroregion can be confined to the Cantabrian Mountains; the NW may include several provinces of the current N pyroregion; the SW will comprise most of the current provinces of the NW; and, E is expected to maintain its current boundaries, but changes in vegetation could lead to a new pyroregion in the extreme southeast (Calheiros et al., 2021).

#### **3 The Role of Land Management**

Besides climate, contemporary fire regimes are also impacted by human activities. Human populations modify fire regimes by suppressing natural ignitions or increasing human-caused ignitions, by increasing or decreasing the fuel loads (e.g., agriculture abandonment or prescribed fire), or by modifying the landscapes (e.g., urbanization, deforestation and afforestation). In Portugal, as in most Mediterranean countries, the land cover is largely the result of anthropogenic activities. Currently, the area of the country covered by forests is about 36% (ICNF, 2019), compared to less than 10% at the end of the XIX century (Mather & Perreira, 2006), and eucalypt and maritime pine plantations account for 48% of the forest area (ICNF, 2019). Cork and holm oak woodlands cover 34%, and the rest of the forest area comprises stone pine (6%), other oaks (3%), chestnut (1%) and other forest species (7%). An important area of the country is also covered by shrub formations and pastures (31%), and agriculture (23%).

On average, from 1996 to 2021, 41% of the annual burned area in Portugal occurred in forests, and 53% in shrublands and pastures (data from ICNF, the Portuguese Forest Services). Analyzing the fire–vegetation relationship, namely the fire incidence by vegetation type is complex due to the firefighting activities, which modify this relationship given the decision of protecting a specific area with a specific land cover or vegetation type. Silva et al. (2019a), analyzing the long-term spatiotemporal trends of bunt area in the Iberian Peninsula, observed increasing trends of burnt area in northwestern Portugal (Braga, Porto, Aveiro districts) and decreasing trends in Galicia, Spain, with the first region showing a decrease in the area of forest and the second an increase of this area, revealing how firefighting and forest management shape the fire–vegetation relationship. The spatial pattern of the land cover types in a region may also have an impact on the fire incidence by land cover. In areas of the rural–urban interface (which is the case of northwestern Portugal), characterized by a mixture of different land cover types (urban areas, agricultural fields and forest areas), it is expected to have a different fire–vegetation relationship compared to regions largely dominated by forests or shrublands.

A fire frequency analysis in Portugal, based on burnt area maps derived from Landsat imagery for the period 1975–2005, revealed that a large portion of central and north Portugal had a fire return interval of fewer than 25 years (see Oliveira et al., 2012, for details on the imagery used and on the frequency analysis). In the regions covered predominantly by forests, there were not many fires but they were large, whereas in regions dominated by shrublands, the fire regime was characterized by more frequent but smaller fires (Oliveira et al., 2012). The size of wildfires is a very important factor in the fire–vegetation relationship, with large fires being less selective in terms of land cover (Barros & Pereira, 2014; Nunes et al., 2005). Nunes et al. (2005), using 506 fires that occurred in Portugal, investigated if fires select given land cover types for burning by comparing the proportions of land cover types present in burned areas and their respective surroundings. Results showed that fires are selective, with small fires exhibiting stronger land cover preferences than large fires, and that there is a marked preference for shrublands followed by forests, while agriculture is avoided. Following a different methodological approach, Barros and Pereira (2014) obtained similar results. The ranking of land cover types according to fire proneness, from less to most fire-prone was as follows: annual crops, evergreen oak woodlands, eucalypt plantations, pine stands and shrublands. All land cover types exhibited reductions in fire preference as fire size increased. Other studies obtained similar rankings (Moreira et al., 2001, 2009; Silva et al., 2009). The lower selectivity of eucalypts compared to pine may be explained in part by their economic value and active management, and suppression effectiveness, which contributes to mitigating the effect of severe weather conditions (Barros & Pereira, 2014).

Population and land cover determine much of the complex fire patterns in Portugal (Costa et al., 2011; Nunes et al., 2016). Nunes et al. (2016) analyzed the drivers of forest fires in Portugal at the municipal level. Concerning land cover, they found that uncultivated land was a factor that contributed to an increase in the burnt area. Uncultivated land, resulting from agricultural abandonment, more than doubled in the last five decades, and it is covered largely by vegetation that is very prone to fire. An analysis of the land cover changes in Portugal in the last 100 years concluded that from 1907 to 1990, the area covered by forests and woodlands increased, a process called forest transition (Mather & Perreira, 2006) and that after 1990, there was a substantial conversion of forest areas to shrublands (Oliveira et al., 2017).

(Calheiros et al., 2022) assessed the relationships between vegetation/land use, fire weather and burnt area at high resolution (municipalities) for Portugal with recent data (2001–2019). ERA5-Land reanalysis dataset was used to compute the FWI indices, and the percentiles of the DSR (DSRp) were assessed with the large wildfires (BA > 100 ha). In detail, each wildfire was associated with the highest DSR recorded during the wildfire duration in the municipality or municipalities. This analysis revealed that the days with DSR above the DSRp between 85 and 95 were responsible for more than 80% of the total BA in mainland Portugal. Nevertheless, this threshold presents significant spatial diversity at the municipal scale. A cluster analysis revealed the clusters with different thresholds that explains 80% or 90% of BA. Further investigation showed that distinct vegetation and/or land use justifies this spatial variability. Indeed, it was demonstrated that the clusters located in coastal regions are predominantly covered with forest and have a large BA with the most extreme meteorological conditions, or very high DSR percentiles. On the contrary, clusters placed in the eastern parts of the country have the largest amount of shrublands, and the BA occurred more frequently with less extreme meteorological conditions or lower DSR percentiles. These results should be used in firefighting and regional fire management (Calheiros et al., 2022).

Sá et al. (2018) modeled fire incidence with vegetation, precipitation and anthropogenic drivers, finding that urban and agricultural areas control fire absence, while forests and especially shrublands area are the main drivers of fire incidence. They also found the need to discriminate between irrigated and rainfed agriculture when studying fire–agriculture relationships. Eucalypt plantations are often viewed as highly flammable due to the nature and structure of the fuel complex, but the burnt area of this species did not increase over time (1980–2017) even with an increase in the area of eucalypt (Fernandes et al., 2019). This study also found that large-scale conversion of maritime pine to eucalypt stands implied lower fuel accumulation. Fernandes and Guedes (2011) analyzed each type of forest in Portugal the fire risk, how species respond to fire, and identified preventive silvicultural treatments. For eucalypts, fire danger (defined as the ease of ignition and fire propagation and its difficulty of extinction) is reduced in 39% of situations and it is extreme in 42% of cases. Tall eucalyptus (especially if open) is less vulnerable to fire; low and closed formations occupy the opposite extreme. Selection by fire is proportional to its availability in the landscape. For pines, fire danger is high and extreme in 60% and 23% of cases, respectively. The low maritime pine forest is very vulnerable to fire. Maritime pine tends to burn in a higher proportion than its availability in the landscape, especially in large fires, and its probability of burning is maximum.

Population density and distribution can also influence the fire regime through its contribution to ignitions. In the work by Calheiros et al. (2020) described earlier, an analysis of fire occurrence, burnt area and weather conditions (FWI) for the IP between 1980 and 2015 revealed clustering into four pyroregions with distinct fire regimes. One interesting result of this analysis is how different frequencies of ignitions influence these regimes. The Northwestern Iberia pyroregion has a large number of ignitions, and hence, a large number of small fires which can evolve into large fires when weather conditions are extreme. Therefore, the climate is a determinant of the occurrence of large fires. In other pyroregions in the Southwestern and Eastern parts of the Peninsula, extreme weather conditions are more common but ignitions are less frequent, thus making climate relatively less important. Ignitions have been related to population density by Catry et al. (2009), and the northwestern part of Iberia has a relatively higher population density due to more dispersed settlement patterns than elsewhere in the peninsula.

Land management also affects the socioeconomic impacts of fires. The most obvious impact is through the exposure of population and property to fire impacts, potentially resulting in damage or fatalities (San-Miguel-Ayanz et al., 2020); areas with larger population densities and/or longer rural/urban interfaces are more exposed to impacts. But there are less obvious secondary impacts, of which the potential impacts on water resources are an example.

Water supplies are often collected from fire-prone watersheds (Robinne et al., 2021) since agricultural watersheds present higher rates of water contamination. But fires can have significant impacts on water quality, as discussed by Nunes et al. (2018) and Robinne et al. (2021). The ash created by fires can be mobilized to the stream networks, leading to excessive turbidity and increasing the concentration of nutrients, heavy metals or organic compounds; these problems can overwhelm the capacity of existing water treatment systems, and there are many cases of water supply limitations as a consequence of fires, often for several years afterward. In this case, the exposure problem might be reversed, as areas with lower population density might be more affected since they are preferential sources of water supply, and since water supply systems for smaller communities tend to have smaller water treatment capacities and therefore be less resilient to disruptions.

#### **4 Conclusions**

This chapter showed how climate combines with land management patterns to affect the regional impacts of fires, therefore presenting unique challenges to regional fire and forest managers. Climate drives the occurrence of fires by promoting the growth of vegetation, and thereby the creation of fuel; and a dry period which promotes fire occurrence and spread. The spatial distribution of population and vegetation is important in determining sources of ignition, ease of fire spread (especially for less intense fires), and difficulty of fire suppression. They also determine the potential socioeconomic impacts on populations and natural resources. In Mediterranean landscapes, and particularly in Portugal, vegetation distribution is mostly derived from human decisions, and therefore, land management plays a crucial role in fire risk.

While not much can be done about climate and extreme weather patterns, land management can be used to mitigate fire risk. This can include special attention to the distribution of forest and agriculture areas, promoting land-use patterns which limit fire spread; or differentiated prevention measures according to the distribution of population and natural resources (e.g., water supplies). Since these factors have a strong spatial variability, the best level of planning to address them is the regional level, where best practices and solutions can be adapted to existing and projected conditions.

#### **References**

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