scholarly journals Integrating Remote Sensing Methods and Fire Simulation Models to Estimate Fire Hazard in a South-East Mediterranean Protected Area

Fire ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 31
Author(s):  
Panteleimon Xofis ◽  
Pavlos Konstantinidis ◽  
Iakovos Papadopoulos ◽  
Georgios Tsiourlis
Keyword(s):  
Remote Sensing ◽  
Protected Area ◽  
Management Strategy ◽  
Fire Suppression ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Simulation Models ◽  
Wildfire Management ◽  
Behavior Simulation

Unlike low intensity fire which promotes landscape heterogeneity and important ecosystem services, large high-intensity wildfires constitute a significant destructive factor despite the increased amount of resources allocated to fire suppression and the improvement of firefighting tactics and levels of organization. Wildfires also affect properties, while an increasing number of fatalities are also associated with wildfires. It is now widely accepted that an effective wildfire management strategy can no longer rely on fire suppression alone. Scientific advances on fire behavior simulation and the increasing availability of remote sensing data, along with advanced systems of fire detection can significantly reduce fire hazards. In the current study remote sensing data and methods, and fire behavior simulation models are integrated to assess the fire hazard in a protected area of the southeast Mediterranean region and its surroundings. A spatially explicit fire hazard index was generated by combining fire intensity estimations and proxies of fire ignition probability. The results suggest that more than 50% of the study area, and the great majority of the protected area, is facing an extremely high hazard for a high-intensity fire. Pine forest formations, characterized by high flammability, low canopy base height and a dense shrub understory are facing the most critical hazard. The results are discussed in relation to the need for adopting an alternative wildfire management strategy.

Supporting decision makers through rapid assessments of remote sensing data: the case of Tefé National Forest, Brazil

2020 ◽  
Author(s):  
Ana Carolina Moreira Pessôa ◽  
Liana O. Anderson ◽  
Rafael Suertegaray Rossato ◽  
Victor Marchezini ◽  
Bruna Maria Pechini Bento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Protected Area ◽  
Rapid Assessment ◽  
Remote Sensing Data ◽  
Pilot Project ◽  
Management Plan ◽  
Extreme Drought ◽  
National Forest ◽  
Sensing Data

<p>Providing scientific subsidies for public policies is a compromise that is beyond the boundaries created by the academic universe, requiring scientists to respond to the challenges posed by increasingly complex societies, both socially and environmentally. Considering this, the objective of this work was to build a pilot project for rapid assessment of Tefé National Forest (TNF) land use zoning and evaluate its relevance as a tool to support actions and influence discussions in protected area management councils.</p><p>The assessment considered remote sensing data on deforestation and fire from 2005 to 2015. Deforestation maps (PRODES-INPE) and active fire (MODIS) information were overlapped with TNF land use zoning. Although National Forest, in general, has its land use rules provided by law, each protected area defines on its Management Plan their own land use zoning, with specific rules.</p><p>The study showed that in 2015, 97% of TNF was covered by forest, and although no deforestation was recorded in the same year, the number of active fires was 1.8 times higher than the average from 2005 to 2014. This demonstrates the vulnerability of this area to the extreme drought which affected the region this year. The Population Zone, where 44% of the TNF population lives, recorded the highest rates of deforestation and fire. The Preservation Zone, on the other hand, showed to be fulfilling its function, presenting no active fires and only one deforestation event during the whole analyzed period.</p><p>These results were presented at the 20th TNF Council Meeting, in 2017. The TNF manager pointed out the great importance of spatial and temporal diagnoses, which can exert in prioritize actions to tackle specific problems in most threatened zones. Community leaders participating in the meeting contributed to the completion of the results with in situ day-to-day reports, offering hypotheses for some phenomena observed on the assessment, such as the deforestation observed in 2010. After that, it became clear that actions directly focused on the Population Zone, and mainly related to the use of fire in years of extreme drought, can improve the conservation outcome for this protected area. Integrated socio-environmental diagnosis, such as this pilot project, can be an important tool, allowing a broader version of the monitoring strategies.</p>

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

Sea of possibilities: Old and new uses of remote sensing data for the enforcement of the Ascension Island marine protected area

Marine Policy ◽  
2018 ◽  
Author(s):  
Thomas Appleby ◽  
Matthew Studley ◽  
Brendon Moorhouse ◽  
Judith Brown ◽  
Chad Staddon ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Area ◽  
Marine Protected Area ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Ascension Island

scholarly journals Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning

2020 ◽  
Vol 12 (24) ◽  
pp. 4124
Author(s):  
Goran Krsnik ◽  
Eduard Busquets Olivé ◽  
Míriam Piqué Nicolau ◽  
Asier Larrañaga ◽  
Adrián Cardil ◽  
...  
Keyword(s):  
Forest Fires ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Regional Level ◽  
Hazard Evaluation ◽  
Level Data ◽  
Risk Assessment And Management ◽  
Geospatial Interaction

Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia.

Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling

2001 ◽  
Vol 10 (4) ◽  
pp. 301 ◽  
Author(s):  
Robert E. Keane ◽  
Robert Burgan ◽  
Jan van Wagtendonk
Keyword(s):  
Remote Sensing ◽  
Geographical Information Systems ◽  
Fire Management ◽  
Multiple Scales ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Mapping Method ◽  
Geographical Information ◽  
Biophysical Modeling ◽  
Fuel Mapping

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

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