A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the 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. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network.

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

This paper provides insight into the use of Machine Learning (ML) models for the assessment of human-caused wildfire occurrence. It proposes the use of ML within the context of fire risk prediction, and more specifically, in the evaluation of human-induced wildfires in Spain. In this context, three ML algorithms—Random Forest (RF), Boosting Regression Trees (BRT), and Support Vector Machines (SVM)—are implemented and compared with traditional methods like Logistic Regression (LR). Results suggest that the use of any of these ML algorithms leads to an improvement in the accuracy—in terms of the AUC (area under the curve)—of the model when compared to LR outputs. According to the AUC values, RF and BRT seem to be the most adequate methods, reaching AUC values of 0.746 and 0.730 respectively. On the other hand, despite the fact that the SVM yields an AUC value higher than that from LR, the authors consider it inadequate for classifying wildfire occurrences because its calibration is extremely time-consuming.

An insight into machine-learning algorithms to model human-caused wildfire occurrence

. This paper presents 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. 250 m) among
the existing global BA datasets. The product includes the full times series
(2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based
on monthly composites of daily images, using temporal and spatial distance
to active fires. The algorithm has two steps, the first one aiming to reduce
commission errors by selecting the most clearly burned pixels (seeds), and
the second one targeting to reduce omission errors by applying contextual
analysis around the seed pixels. This product was developed within the
European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the
Fire Disturbance project (Fire_cci). The final output
includes two types of BA files: monthly full-resolution continental tiles
and biweekly global grid files at a degraded resolution of 0.25 ∘ .
Each set of products includes several auxiliary variables that were defined
by the climate users to facilitate the ingestion of the product into global
dynamic vegetation and atmospheric emission models. Average annual burned
area from this product was 3.81 Mkm 2 , with maximum burning in 2011 (4.1 Mkm 2 )
and minimum in 2013 (3.24 Mkm 2 ). The validation was based on
a stratified random sample of 1200 pairs of Landsat images, covering the
whole globe from 2003 to 2014. The validation indicates an overall accuracy
of 0.9972, with much higher errors for the burned than the unburned category
(global omission error of BA was estimated as 0.7090 and global commission
as 0.5123). These error values are similar to other global BA products, but
slightly higher than the NASA BA product (named MCD64A1, which is produced
at 500 m resolution). However, commission and omission errors are better
compensated in our product, with a tendency towards BA underestimation
(relative bias −0.4033 ), as most existing global BA products. To understand
the value of this product in detecting small fire patches (  ha),
an additional validation sample of 52 Sentinel-2 scenes was generated
specifically over Africa. Analysis of these results indicates a better
detection accuracy of this product for small fire patches (  ha)
than the equivalent 500 m MCD64A1 product, although both have high errors for
these small fires. Examples of potential applications of this dataset to
fire modelling based on burned patches analysis are included in this paper.
The datasets are freely downloadable from the Fire_cci
website ( https://www.esa-fire-cci.org/ , last access: 10 November 2018) and their repositories (pixel at
full resolution: https://doi.org/cpk7 , and grid: https://doi.org/gcx9gf ).

/pdf/generation-and-analysis-of-a-new-global-burned-area-product-2xy26m45et.pdf

Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression

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.

E. Zapico

Papers

Integrating geospatial information into fire risk assessment