Innovative Action for Forest Fire Prevention in Kythira Island, Greece, through Mobilization and Cooperation of the Population: Methodology and Challenges

The island of Kythira in Greece suffered a major forest fire in 2017 that burned 8.91% of its total area and revealed many challenges regarding fire management. Following that, the Hellenic Society for the Protection of Nature joined forces with the Institute of Mediterranean and Forest Ecosystems in a project aiming to improve fire prevention there through mobilization and cooperation of the population. This paper describes the methodology and the results. The latter include an in-depth analysis of fire statistics for the island, development of a forest fuels map, and prevention planning for selected settlements based on fire modeling and on an assessment of the vulnerability of 610 structures, carried out with the contribution of groups of volunteers. Emphasis was placed on informing locals, including students, through talks and workshops, on how to prevent forest fires and prepare their homes and themselves for such an event, and on mobilizing them to carry out fuel management and forest rehabilitation work. In the final section of the paper, the challenges that the two partners faced and the project achievements and shortcomings are presented and discussed, leading to conclusions that can be useful for similar efforts in other places in Greece and elsewhere.

Scandinavian Forest Fire Activity Correlates with Proxies of the Baffin Bay Ice Cover

Understanding factors driving fire activity helps reveal the degree and geographical variability in the resilience of boreal vegetation to large scale climate forces. We studied the association between sea ice cover in the Baffin Bay and the Labrador Sea and observational records of forest fires in two Nordic countries (Norway and Sweden) over 1913–2017. We found a positive correlation between ice proxies and regional fire activity records suggesting that the Arctic climate and the associated changes in North Atlantic circulation exercise an important control on the levels of fire activity in Scandinavia. Changes in the sea cover are likely correlated with the dynamic of the North Atlantic Current. These dynamics may favor the development of the drought conditions in Scandinavia through promoting persistent high-pressure systems over the Scandinavian boreal zone during the spring and summer. These periods are, in turn, associated with an increased water deficit in forest fuels, leading to a regionally increased fire hazard. The Arctic climate will likely be an important future control of the boreal fire activity in the Nordic region.

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.

Synoptic patterns favouring lightning-ignited wildfires in Catalonia

<p>Wildfires cause substantial losses to socio-economic and natural assets, especially in Mediterranean-climate regions. Despite human activity is the main cause of wildfires in Mediterranean European countries, lightning-ignited wildfires should be also considered a major disruptive agent as they can trigger large fires. Besides, recent studies on the potential climate change effects on wildfires pointed out that lightning-ignited wildfires may gain relevance in Mediterranean areas in the years to come.</p><p>In this regard, the present study analyses the meteorological conditions favouring lightning-ignited wildfires in Catalonia (NE Iberian Peninsula). Gaining insight into circulation types favouring thunderstorms that ignite wildfires can be useful in the forest protection tactical decision-making process, i.e. locating ignitions and potential holdover fires, preparing for days with multiple ignitions or routing detection flight paths.</p><p>It is worth noticing that one of the reasons why lightning-caused wildfires are difficult to manage is that they can survive for several days before flaring up. That is, even if forest fuels remain damp after the thunderstorm’ rainfall, lightning ignitions may survive smouldering underneath, emerging days later as surface vegetation becomes dry enough to support sustained combustion.</p><p>For this reason, on a first step, a reliable lightning-wildfire association is needed to properly identify the date and time of the firestarter for each wildfire. Afterwards, the circulation types on the days of ignition are analysed.</p><p>The study relies on a dataset of more than 750 lightning-ignited wildfires, gathered by the Forest Protection Agency of the autonomous government of Catalonia between 2005 and 2018. Lightning data comes from the Lightning Location System operated by the Meteorological Service of Catalonia.</p>

The Effect of Repeated Prescribed Burning on Soil Properties: A Review

Prescribed burning is a tool that is frequently used for various land management objectives, mainly related to reduction of hazardous forest fuels, habitat management and ecological restoration. Given the crucial role of soil in forest ecosystem processes and functions, assessing the effects of prescribed burning on soil is particularly relevant. This study reviews research on the impacts of repeated prescribed burning on the physical, chemical and biological properties of soil. The available information shows that the effects are highly variable, rather inconsistent and generally minor for most of the soil characteristics studied, while a number of soil properties show contrasting responses. On the other hand, ecosystem characteristics, differences in fire severity, frequency of application and the cumulative effect of treatment repetition over time, have possibly made it more difficult to find a more common response in soil attributes. Our study has also revealed some limitations of previous research that may have contributed to this result, including a limited number of long-term studies, conducted at a few experimental sites, and in a limited number of forest ecosystems. Research issues concerning the effects of prescribed fire on soil are presented. The need to integrate such research into a broader interdisciplinary framework, encompassing the role of the fire regime on ecosystem functions and processes, is also highlighted.

The Human Dimensions of Spatial, Pre-Wildfire Planning Decision Support Systems: A Review of Barriers, Facilitators, and Recommendations

Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous DSSs applicable to forest and wildfire planning, which can support timely and relevant information for decision making, but the use and adoption of these systems is inconsistent. There is a need to elucidate the use of DSSs, specifically those that support pre-wildfire, spatial planning, such as wildfire risk assessment and forest fuels treatment prioritization. It is important to understand what DSSs are in use, barriers and facilitators to their use, and recommendations for improving their use. Semi-structured interviews with key informants were used to assess these questions. Respondents identified numerous barriers, as well as recommendations for improving DSS development and integration, specifically with respect to capacity, communication, implementation, question identification, testing, education and training, and policy, guidance, and authorities. These recommendations can inform DSS use for wildfire risk assessment and treatment prioritization to meet the goals of national policies and frameworks. Lastly, a framework for organizing spatial, pre-wildfire planning DSSs to support end-user understanding and use is provided.

Exposure of the Canadian Wildland-Human Interface (WHI) and population to wildland fire, under current and future climate conditions

In Canada, recent fire seasons have demonstrated the threat of wildland fire in the Wildland-Human Interface (WHI) areas, where forest fuels intermingle with or abut housing, industry, and infrastructure. Although fire activity is expected to increase further in the coming decades as a result of climate change, no WHI-specific estimates of wildland fire exposure are currently available. This study combines spatial and demographic information sources to estimate the current and future wildland fire exposures, as reflected by fire return intervals (FRI) of WHI areas and populations across Canada. The WHI covers 17.3% of the forested area in Canada. Within the WHI, we found that 19.4% of the area currently experiences FRI ≤ 250 years but, by the end of the century, this could increase to 28.8% under RCP 2.6 and to 43.3% under RCP 8.5. Approximately 12.3% of the Canadian population currently live in the Wildland-Urban Interface (WUI), which includes 32.1% of the on-reserve First Nations population. Currently, 17.8% of the on-reserve WUI population is exposed to FRI ≤ 250 years, compared to only 4.7% of the remaining WUI population. By 2100, these proportions could reach 39.3% and 17.4% respectively, under the less optimistic climatic scenarios (RCP 8.5).

Forest fuel assessment by LiDAR data. A case study in NE Italy

<p>Fuel management is a crucial action to maintain wildland fires under the threshold of manageability; hence, in order to allocate resources in the best way, wildland fuel mapping is regarded as a necessary tool by land managers. Several studies have used Aerial Laser Scanner (ALS) data to estimate forest fuels characteristics at plot level, but few have extended such estimates at a zonal level.</p><p>In the context of the EU Interreg Project CROSSIT SAFER, a test of the possibilities of ALS data to predict fuels attributes has been performed in three different areas: an alpine basin, a coastal wildland-urban interface and a karstic highland. Eighteen sampling plots have been laid out over 6 forest categories, with a special focus on <em>Pinus nigra</em> J. F. Arnold artificial forests. Low density (average 4 points/m<sup>2</sup>) discrete return LiDAR data has been analysed with FUSION, a free point cloud analysis software tailored to forestry purposes; field and remote sensing data have been connected with simple statistical modelling and results have been spatialised over the case study areas to provide wall-to-wall inputs for FLAMMAP fire behaviour simulation software.</p><p>Resulting maps can be of relevance for land managers to better highlight the most vulnerable or fire prone areas at a mesoscale administrative level. Limitations and room for improvement are pointed out, in the view that land management should keep updated with the latest technology available.</p>