ArcFuel methodology for mapping forest fuels in Europe

Toukiloglou, Pericles; Eftychidis, George; Gitas, Ioannis Z.; Tompoulidou, Maria

doi:10.1117/12.2028213

Cited by 8 publications

(11 citation statements)

References 34 publications

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“…In the EFFIS and FUELMAP projects under JRC [22,53,54], the correlation of vegetation types in Europe to Anderson fuel models was obtained. Under these projects, fuel of 42 types (as shown in Table 1) available in Europe were categorized into nine groups: Peat bogs (FT_1 and FT_2), Grasslands (FT_3 to FT_6), Shrublands (FT_7 to FT_12), Transitional Shrubland/Forest (FT_13 to FT_19), Coniferous Forest (FT_20 to FT_28), Broadleaved Forest (FT_29 to FT_34), Mixed Forest (FT_35 to FT_38), Aquatic Vegetation (FT_39 to FT_41), and Agroforestry areas (FT_42).…”

Section: Jrc-anderson Fuel Models Correlationmentioning

confidence: 99%

“…In our case, a fully constrained linear mixing model was used to assign mixed pixels to their appropriate category. In the framework of EFFIS (European Forest Fire Information System) and FUELMAP projects under the Joint Research Centre (JRC), a categorization of the fuel types available in Europe was carried out and correlated with Anderson fuel models (1982) [21,22].…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

2022

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Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input.

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Section: Jrc-anderson Fuel Models Correlationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

2022

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“…FUELMAP provides a map of 42 fuel classes at a 250 m spatial resolution for the EU. AecFuel is a more recent mapping of European fuels at a higher spatial resolution (50 m) [97]. These products were derived from a mixture of remotely sensed data from several sensors and pre-existing maps of relevant variables which were implemented into a hierarchical classification scheme for creating vegetation fuel maps.…”

Section: Orbital Multispectral Sensorsmentioning

confidence: 99%

A Review of the Applications of Remote Sensing in Fire Ecology

2019

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Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.

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“…As a recent example of such effort is the fuel type methodology developed within the LIFE 2010 ArcFUEL project. Under the scope of the specific project, a fuel type map for four Mediterranean regions was generated based on Landsat TM imagery and pixel-based approach (Toukiloglou et al 2013). Nevertheless, advanced classification methodologies such as object-oriented method, have been employed in several studies for local fuel type mapping in order to achieve more accurate results (Mallinis et al 2014), (Arroyo et al 2006), (Gitas et al 2006).…”

Section: Introductionmentioning

confidence: 99%

National fuel type mapping methodology using geographic object based image analysis and Landsat 8 Oli imagery

Tompoulidou¹,

Stefanidou²,

Grigoriadis³

et al. 2016

GEOBIA 2016: Solutions and Synergies

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A key issue in modern fire management planning is the accurate fuel type mapping, required at many different spatial and temporal scales. Fuel type classification is critical for improving fire prevention schemes, developing accurate fire dispersion models and designing effective measures for mitigating the impacts of a potential wildfire event on the ecosystem. Remote sensing offers the potential to provide spatially distributed information on fuel types and it has been widely used both at regional and local scale. The aim of this study is to develop and evaluate a fuel type mapping methodology on a national level, based on the geographic objectoriented classification approach and Landsat-8 OLI imagery. The proposed methodology was developed in the case study of Chalkidiki in the northern part of Greece and it was further tested for its transferability in the regional unit of Preveza and the whole administrative region of Attica. The classification scheme was determined taking under consideration the existing fuel models (Prometheus, FBP etc) and fuel type products (JRC FUELMAP, LIFE10 ArcFUEL) so as the final fuel type map could be easily adjusted and/or compared. Fifty-four Landsat-8 OLI images for summer and for winter season, covering the national territory, were acquired and several features (vegetation indices, textural and spectral features) were calculated for both seasons. The optimized feature selection for the discrimination of each fuel type category was empirically obtained. Results showcase the effectiveness of the employed object-oriented classification approach in obtaining highly accurate fuel type maps. Specifically, the generated fuel map of Chalkidiki exhibited an overall accuracy of 89.47%, with a Kappa Index of Agreement (KIA) equal to 0.844. The application of the model to the two other regions resulted in overall accuracies of 80.30% (KIA=0.706) for Attica and 91.74% (KIA=0.867) for Preveza. Summarizing, the results proved the good transferability properties of the proposed methodology enabling the implementation of the model across the country.

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ArcFuel methodology for mapping forest fuels in Europe

Cited by 8 publications

References 34 publications

An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

An Automatic Procedure for Forest Fire Fuel Mapping Using Hyperspectral (PRISMA) Imagery: A Semi-Supervised Classification Approach

A Review of the Applications of Remote Sensing in Fire Ecology

National fuel type mapping methodology using geographic object based image analysis and Landsat 8 Oli imagery

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