Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly ...

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A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.

Sustainable Forest Operations (SFO): A new paradigm in a changing world and climate

Green infrastructure (GI) has been identified as a promising approach to help cities adapt to climate change through the provision of multiple ecosystem services. However, GI contributions to urban...

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Challenges of mainstreaming green infrastructure in built environment professions

Lubricating oils used in machines with an open cutting system, such as a saw or harvester, are applied in forest areas, gardening, in the household, and in urban greenery. During the operation of the device with an open cutting system, the lubricating oil is emitted into the environment. Therefore, the use of an oil base and refining additives of petroleum origin in the content of lubricants is associated with a negative impact on health and the environment. The current legal regulations concerning lubricants applicable in the European Union (EU) assess the degree of biodegradability. Legislation permits the use of biodegradable oils at 60% for a period of 28 days. This means that, in practice, lubricating oil considered to be biodegradable can contain up to 50% of the so-called petroleum oil base. The paper aims to draw public attention to the need to reduce the toxicity and harmful effects, due to their composition, of lubricating oils emitted into the environment on health. The authors discuss the impact of petroleum oil lubricants on soils, groundwater, vegetation, and animals, and the impact of petroleum-origin oil mist on health. An overview of test methods for the biodegradability of lubricating oils is presented, including the Organization for Economic Cooperation and Development (OECD) 301 A–F, 310, and 302 A–D tests, as well as their standard equivalents. The current legal regulations regarding the use and control of lubricating oils emitted into the environment are discussed. Legal provisions are divided according to their area of application. Key issues regarding the biodegradability and toxicity of petroleum fractions in lubricating oils are also addressed. It is concluded that lubricating oils, emitted or potentially emitted into the environment, should contain only biodegradable ingredients in order to eliminate the negative impact on both the environment and health. Total biodegradability should be confirmed by widely applied tests. Therefore, a need to develop and implement low-cost and simple control procedures for each type of lubricating oil, ensuring the possibility of an indisputable conclusion about the presence and total absence of petroleum-derived components in oil, as well as the content of natural ingredients, occurs.

Ecological and Health Effects of Lubricant Oils Emitted into the Environment

Damage to forest soil caused by vehicle traffic mainly consists of soil compaction, displacement, and rut formation. Severity of the damage depends on vehicle mass, weight of the carried loads, ground morphology, and soil properties, such as moisture. This paper investigates the impacts of two types of vehicles (tracked or wheeled tractor), traffic intensities (one or five skidding cycles) and soil moisture (24% or 13% by weight) on compaction of a loam textured soil in a mixed conifer stand of central Italy. Changes in porosity, bulk density, shear and penetration resistances were analyzed. The latter three parameters were significantly higher in the trafficked soil portions than in the undisturbed ones in all treatments, while the opposite was true for porosity. The impact on soil bulk density and porosity was stronger for the wheeled tractor working on moist soil, while no significant effect of soil moisture was recorded for the tracked tractor. Shear and penetration resistances increased as a consequence of traffic, depending on both tractor type and soil moisture. The largest impact on shear resistance was recorded for the wheeled tractor on moist soil, while significant differences in penetration resistance were observed only between tracked and wheeled tractors in dry soil conditions. In order to preserve soil quality during logging activities, we recommend to operate under dry soil conditions and to limit vehicle movement on existing or new planned trails.

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Impact of wheeled and tracked tractors on soil physical properties in a mixed conifer stand

A well-planned forest road network is essential for meeting the goal of sustainable forest management. Forest roads play a key role in commercial purposes, fire prevention, and recreational activities. The aim of this work was to apply precision forestry in the analysis of the forest network of two forest ownerships in Tuscany. A proposal was formulated based on the information obtained regarding future forest road construction. This proposal takes into consideration technical effectiveness and environmental sustainability, with particular attention paid to low-impact logging. Two systems were tested so as to gain a better comprehension of different technical approaches. One system was reported by other researchers and the other was developed by the authors of this paper. The aim was to provide a valid instrument and possible alternative for forest managers involved in decision making. This study highlights the importance of precision forestry, even on a small-scale technical application level in forest road planning, thus helping managers and owners during the decision-making process in forestry operations.

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The Application of Two Approaches Using GIS Technology Implementation in Forest Road Network Planning in an Italian Mountain Setting

Identifying causes, dynamics and consequences of work accidents in forest operations in an alpine context

Determining exhaust fumes exposure in chainsaw operations.

. Modeling and assessing the factors that drive forest fire ignitions
is critical for fire prevention and sustainable
ecosystem management. In southern Europe, the anthropogenic component of wildland fire ignitions is especially
relevant. In the Alps, however, the role of fire as a component of disturbance regimes in forest and grassland ecosystems
is poorly known. The aim of this work is to model the probability of fire ignition for an Alpine region in Italy using
a regional wildfire archive (1995–2009) and MaxEnt modeling. We analyzed separately (i) winter forest fires, (ii) winter
fires on grasslands and fallow land, and (iii) summer fires. Predictors were related to morphology, climate, and land use;
distance from infrastructures, number of farms, and number of grazing animals were used as proxies for the anthropogenic
component. Collinearity among predictors was reduced by a principal component analysis. Regarding ignitions, 30 % occurred in
agricultural areas and 24 % in forests. Ignitions peaked in the late winter–early spring. Negligence from
agrosilvicultural activities was the main cause of ignition (64 %); lightning accounted for 9 % of causes across the
study time frame, but increased from 6 to 10 % between the first and second period of analysis. Models for all groups of
fire had a high goodness of fit (AUC 0.90–0.95). Temperature was proportional to the probability of ignition, and
precipitation was inversely proportional. Proximity from infrastructures had an effect only on winter fires, while the density
of grazing animals had a remarkably different effect on summer (positive correlation) and winter (negative)
fires. Implications are discussed regarding climate change, fire regime changes, and silvicultural prevention. Such
a spatially explicit approach allows us to carry out spatially targeted fire management strategies and may assist in
developing better fire management plans.

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Cristiano Foderi

Papers

Impact of wheeled and tracked tractors on soil physical properties in a mixed conifer stand

The Application of Two Approaches Using GIS Technology Implementation in Forest Road Network Planning in an Italian Mountain Setting

Identifying causes, dynamics and consequences of work accidents in forest operations in an alpine context

Determining exhaust fumes exposure in chainsaw operations.

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley