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Integrating geospatial information into fire risk assessment

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TLDR
In this paper, a comprehensive fire risk assessment system is proposed, which makes extensive use of geographic information technology to evaluate risk conditions, including human factors, lightning probability and fuel moisture content of both dead and live fuels.
Abstract
Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper,whichmakesextensiveuseofgeographicinformationtechnologiestoofferaspatiallyexplicitevaluationoffirerisk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approachestomergethosevariablesintosyntheticriskindicesandfinallythevalidationoftheoutputs.Themodelhasbeen applied at a national level for the whole Spanish Iberian territory at 1-km 2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P,0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

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Spatial, temporal, and content analysis of Twitter for wildfire hazards

TL;DR: In this article, the authors analyzed wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness.
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An insight into machine-learning algorithms to model human-caused wildfire occurrence

TL;DR: This paper proposes the use of ML within the context of fire risk prediction, and more specifically, in the evaluation of human-induced wildfires in Spain, and suggests that any of these ML algorithms leads to an improvement in the accuracy of the model when compared to traditional methods.
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Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

TL;DR: In this article, the authors presented a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250m) among the existing global BA datasets.
References
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Book

Inverse Problem Theory and Methods for Model Parameter Estimation

TL;DR: This chapter discusses Monte Carol methods, the least-absolute values criterion and the minimax criterion, and their applications to functional inverse problems.
BookDOI

Assessing the accuracy of remotely sensed data : principles and practices

TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Book

Geographically Weighted Regression: The Analysis of Spatially Varying Relationships

TL;DR: In this paper, the basic GWR model is extended to include local statistics and local models for spatial data, and a software for Geographically Weighting Regression is described. But this software is not suitable for the analysis of large scale data.
Journal ArticleDOI

Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009)

TL;DR: In this paper, the authors used a revised version of the Carnegie-Ames-Stanford-Approach (CASA) biogeochemical model and improved satellite-derived estimates of area burned, fire activity, and plant productivity to calculate fire emissions for the 1997-2009 period on a 0.5° spatial resolution with a monthly time step.
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