Next Article in Journal
Application of Hierarchical Spatial Autoregressive Models to Develop Land Value Maps in Urbanized Areas
Next Article in Special Issue
A Robust Early Warning System for Preventing Flash Floods in Mountainous Area in Vietnam
Previous Article in Journal
Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction
Previous Article in Special Issue
Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessCreative

Dynamic Wildfire Navigation System

Geography and Spatial Sciences, University of Tasmania, Private Bag 76, Hobart, Tasmania 7001, Australia
Bureau of Meteorology, GPO Box 727, Hobart, Tasmania 7000, Australia
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 194;
Received: 25 March 2019 / Revised: 9 April 2019 / Accepted: 19 April 2019 / Published: 23 April 2019
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
PDF [1820 KB, uploaded 23 April 2019]
  |     |  


Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 km 2 in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance. View Full-Text
Keywords: GIS; FDI; wildfire; PostGIS; GeoDjango GIS; FDI; wildfire; PostGIS; GeoDjango

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Ozaki, M.; Aryal, J.; Fox-Hughes, P. Dynamic Wildfire Navigation System. ISPRS Int. J. Geo-Inf. 2019, 8, 194.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top