Fire danger forecast constitutes one of the most important components of integrated fire management since it provides
crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this
work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The
methodology that is currently under development is based on an innovative approach that employs dry fuel spatial
connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically,
the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R
software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates
are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of
the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison
to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data.
Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires
Efficient forest fire management is a key element for alleviating the catastrophic impacts of wildfires. Overall, the effective response to fire events necessitates adequate planning and preparedness before the start of the fire season, as well as quantifying the environmental impacts in case of wildfires. Moreover, the estimation of fire danger provides crucial information required for the optimal allocation and distribution of the available resources. The Greek National Observatory of Forest Fires (NOFFi)—established by the Greek Forestry Service in collaboration with the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki and the International Balkan Center—aims to develop a series of modern products and services for supporting the efficient forest fire prevention management in Greece and the Balkan region, as well as to stimulate the development of transnational fire prevention and impacts mitigation policies. More specifically, NOFFi provides three main fire-related products and services: a) a remote sensing-based fuel type mapping methodology, b) a semi-automatic burned area mapping service, and c) a dynamically updatable fire danger index providing mid- to long-term predictions. The fuel type mapping methodology was developed and applied across the country, following an object-oriented approach and using Landsat 8 OLI satellite imagery. The results showcase the effectiveness of the generated methodology in obtaining highly accurate fuel type maps on a national level. The burned area mapping methodology was developed as a semi-automatic object-based classification process, carefully crafted to minimize user interaction and, hence, be easily applicable on a near real-time operational level as well as for mapping historical events. NOFFi’s products can be visualized through the interactive Fire Forest portal, which allows the involvement and awareness of the relevant stakeholders via the Public Participation GIS (PPGIS) tool.
Climate change and overall temperature increase results in changes in forest cover in high elevations. Due to the long life cycle of trees, these changes are very gradual and can be observed over long periods of time. In order to use remote sensing imagery for this purpose it needs to have very high spatial resolution and to have been acquired at least 50 years ago. At the moment, the only type of remote sensing imagery with these characteristics is historical black and white aerial photographs. This study used an aerial photograph from 1945 in order to map the forest cover at the Olympus National Park, at that date. An object-based classification (OBC) model was developed in order to classify forest and discriminate it from other types of vegetation. Due to the lack of near-infrared information, the model had to rely solely on the tone of the objects, as well as their geometric characteristics. The model functioned on three segmentation levels, using sub-/super-objects relationships and utilising vegetation density to discriminate forest and non-forest vegetation. The accuracy of the classification was assessed using 503 visually interpreted and randomly distributed points, resulting in a 92% overall accuracy. The model is using unbiased parameters that are important for differentiating between forest and non-forest vegetation and should be transferrable to other study areas of mountainous forests at high elevations.
The ArcFuel project aims to develop a generic methodology, which will enable the regular production of consistent forest vegetation fuel maps over Europe. Such maps can be used to simulate fire scenarios and support the design and implementation of effective prevention and mitigation measures against fires. ArcFuel uses the results of a recent effort of JRC Ispra, which aimed to create a standardized scheme of fuel types, representative of the vegetation occurring in the European forest regions. Based on this approach and using existing European spatial datasets and multi-temporal remotely sensed images ArcFuel defines a methodology for producing vegetation fuel maps compatible with the relevant scheme of JRC. The choice of input material was mainly driven by the need of keeping the production cost low and updating regularly the European vegetation fuel map. The proposed methodology can be applied in all EU regions and is currently tested and validated in pilot areas in Greece, Portugal, Spain and Italy.