Special Issue "Advances in Forest Fire Behaviour Modelling Using Remote Sensing"
Deadline for manuscript submissions: 31 December 2020.
Interests: Lidar for forest structure analysis; 3D fire behaviour models; Object-based feature extraction and classification; Land use/land cover change analysis
Special Issues and Collections in MDPI journals
Interests: Landscape, vegetation, and fire ecology; remote sensing of vegetation patterns and processes; forest and rangeland ecology and management; empirical modeling of spatially explicit ecological data.
Accurate information about three-dimensional canopy structure and heterogeneous wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Recently, physically-based fire behaviour models have been developed to represent fuels and fire behaviour processes, showing promise for examination of fuel/fire/atmosphere interactions. However, these models require very high spatial detail, such as locations and dimensions of individual trees, species composition, spatial distributions of understory fuels, 3D distribution of fuel mass and bulk density at voxel level, fuel surface area and moisture content. Remote sensing tools and methods are starting to play an important role in the acquisition of a variety of data and in the estimation of such parameters at finer spatial scales, so they can be used as input in fire behavior models, where bulk density of canopy, understory and surface fuels must be estimated and quantified at voxel level, and fuel moisture content, from leaves, pine needles and fine roundwood at tree or patch level. This multiscale concept can only be achieved by using different types of acquisition devices and techniques capable to produce models at distinct levels of detail. The wide range of platforms (satellites, aerial, UAS and field-based) and sensors (multi and hyper-spectral, RADAR, LiDAR) nowadays available for data acquisition offer excellent prospects for addressing this multiscale problem.
In this special issue, submissions describing new advances in data acquisition and methods for fire behaviour modelling, including integration of platforms and sensors, estimation of fuel parameters, analyses of factors affecting fire behaviour, and other topics involving the use of remote sensing data, are encouraged and welcome.Prof. Dr. Luis A. Ruiz
Dr. Andrew T. Hudak
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Fire behavior models
- Fire ecology
- Forest structure
- Canopy fuels
- Canopy bulk density
- Fuel moisture content
- Understory vegetation
- Surface fuels
- Point clouds
- ALS, TLS, UAV