Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

The 2013 Rim Fire: Implications for Predicting Extreme Fire Spread, Pyroconvection, and Smoke Emissions

Abstract The 2013 Rim Fire, which burned over 104,000 ha, was one of the most severe fire events in California's history, in terms of its rapid growth, intensity, overall size, and persistent smoke plume. At least two large pyrocumulonimbus (pyroCb) events were observed, allowing smoke particles to extend through the upper troposphere over a large portion of the Pacific Northwest. However, the most extreme fire spread was observed on days without pyroCb activity or significant regional convection. A diverse archive of ground, airborne, and satellite data collected during the Rim Fire provides a unique opportunity to examine the conditions required for both extreme spread events and pyroCb development. Results highlight the importance of upper-level and nocturnal meteorology, as well as the limitations of traditional fire weather indices. The Rim Fire dataset also allows for a detailed examination of conflicting hypotheses surrounding the primary source of moisture during pyroCb development. All pyroCbs were associated with conditions very similar to those that produce dry thunderstorms. The current suite of automated forecasting applications predict only general trends in fire behavior, and specifically do not predict 1) extreme fire spread events and 2) injection of smoke to high altitudes. While these two exceptions are related, analysis of the Rim Fire shows that they are not predicted by the same set of conditions and variables. The combination of numerical weather prediction data and satellite observations exhibits great potential for improving automated regional-scale forecasts of fire behavior and smoke emissions

Earth resources applications of the Synchronous Earth Observatory Satellite (SEOS)

The results are presented of a four month study to define earth resource applications which are uniquely suited to data collection by a geosynchronous satellite. While such a satellite could also perform many of the functions of ERTS, or its low orbiting successors, those applications were considered in those situations where requirements for timely observation limit the capability of ERTS or EOS. Thus, the application presented could be used to justify a SEOS

High resolution fire behavior monitoring and plume simulation in the context of experimental fire

Coupled fire-atmosphere systems are currently developed to respond to the need of operational system in air quality and fire attack management. This work participates to this effort by proposing a simulation strategy where the plume is simulated using fire observation. Such approach can provide reference test case for more complex coupled fire- atmosphere simulation. Using the Forefire-MesoNH system, we simulate the plume evolution of a landscape scale burn where the fire is not simulated as a spreading front but rather prescribed from multiple fix burners controlled with observation data. The simulation of the plume formed from a 7-hectare savannah fire conducted in Kruger National Park in 2014 is demonstrated using helicopter-borne observations georeferenced at 1-m resolution and post-processed to extract information of heat fluxes at pixel level.Postprint (published version

Use of remote sensing to quantify forest response following a wildfire in northeast Minnesota

Wildfire is a natural disturbance common to many forested ecosystems of North America. In the fall of 2011, a lightning-ignited wildfire, the Pagami Creek Fire (PCF), burned over 38,000 hectares of forest in northeast Minnesota, most of which occurred in the protected Boundary Waters Canoe Area Wilderness (BWCAW). Satellite remote sensing data has been used within the upper Midwest to detect and quantify forest vegetation extent, composition, and associated structure. However, very few studies have quantified the composition and structure of early seral forest regeneration. We were interested in two objectives: 1) Use a combination of remote sensing imagery (multi-temporal Landsat data and National Agriculture Imagery Program (NAIP) color-infrared aerial imagery) and derived variables to develop models estimating quaking aspen (Populus tremuloides Michx.) regeneration abundance and structure as well as vegetation species richness derived from field plots. These empirical models were used to spatially extrapolate field data to produce continuous spatially explicit biophysical estimates for the entire PCF burned area. 2) Examine hypothesized relationships between pre-fire aspen basal area (BA) and resulting fire severity, and to evaluate whether burn severity and pre-burn aspen BA may explain observed variability in aspen regeneration (size, abundance, and distribution) and vegetation species richness within the PCF burned area. Remote sensing-based models for vegetation species richness (RMSE=2.47 species, Adj. R2=0.60) was the strongest observed, followed by average aspen stem diameter (RMSE=2.21 mm, Adj. R2=0.53). We also generated statistically significant (p \u3c 0.01) empirical models for average aspen basal area, height, density, and percent cover. We used simple linear regression to examine empirical relationships between field measurements of regenerating aspen abundance and vegetation species richness measurements as functions of satellite derived fire severity and pre-fire aspen BA estimates. Aside from one of our aspen measurements (percent foliar cover), there was a significant relationship (p \u3c 0.01) and meaningful coefficient of determination with pre-fire aspen BA and post-fire aspen measurements (Adj. R2 range = 0.33 - 0.12). There was not a significant relationship (p = 0.46) between pre-fire BA and vegetation species richness (Adj. R2 = -0.01). There was not a significant relationship (p-value range = 0.14 - 0.57) between fire severity and all other vegetation recovery response variables. We expect both forest managers and ecologists to benefit from the ability to detect, model, and quantify early-seral stage forest regeneration over large, often remote, areas. Such remote sensing methods may further facilitate understanding of landscape scale processes (pattern, distribution, and abundance) with respect to fire and other stand-replacing forest disturbances in this and similar forested environments

Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

[EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications—many times in combination—whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.SIPart of this work was funded by the Spanish Ministry of Science, Innovation and University through the project AGL2016-76769-C2-1-R “Influence of natural disturbance regimes and management on forests dynamics, structure and carbon balance (FORESTCHANGE)”

Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

[EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis, and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications-many times in combination-whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.Part of this work was funded by the Spanish Ministry of Science, innovation and University through the project AGL2016-76769-C2-1-R "Influence of natural disturbance regimes and management on forests dynamics. structure and carbon balance (FORESTCHANGE)".Gómez, C.; Alejandro, P.; Hermosilla, T.; Montes, F.; Pascual, C.; Ruiz Fernández, LÁ.; Álvarez-Taboada, F.... (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems. 28(1):1-33. https://doi.org/10.5424/fs/2019281-14221S133281Ungar S, Pearlman J, Mendenhall J, Reuter D, 2003. Overview of the Earth Observing-1 (EO-1) mission. IEEE T Geosci Remote 41: 1149−1159.Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA, 2011. Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. 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Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0