Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Ntinopoulos, Nikolaos; Spiliotopoulos, Marios; Vasiliades, Lampros; Mylopoulos, Nikitas

doi:10.3390/cli10100143

Cited by 13 publications

(7 citation statements)

References 58 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the historical period, a north-south gradient is observed with lower values in northern parts (Thrace, Macedonia, Epirus and Thessaly) and higher values over Central Greece, the Aegean islands and Crete. These results are in accordance with previous related studies [14,29,30] that indicate that the drier and hotter southern and eastern regions of Greece seem to exhibit high values of the FWI. The spatial pattern of the future values of the FWI is similar to the historical one, following the north-south gradient, but overall large increases are observed under the two emission scenarios all over the country.…”

Section: Fire Weather Index Projectionssupporting

confidence: 94%

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

et al. 2023

View full text Add to dashboard Cite

Future fire weather conditions under climate change were investigated based on the Fire Weather Index (FWI), Initial Spread Index (ISI) and threshold-specific indicators in Greece. The indices were calculated from climate datasets derived from high-resolution validated simulations of 5 km. The dynamical downscaled simulations with the WRF model were driven by EC-Earth output for historical (1980–2004) and future periods, under two Representative Concentration Pathways (RCPs), RCP4.5 and 8.5. The analysis showed that the FWI is expected to increase substantially, particularly in the southern parts with extreme values found above 100. In addition, the number of days with an FWI above the 90th percentile is projected to increase considerably (above 30 days), under both scenarios. Over the eastern and northern mainland, the increase is estimated with more than 70 days under RCP4.5, in the near future (2025–2049). Moreover, central and north-eastern parts of the country will be affected with 30 or more extreme consecutive days of prolonged fire weather, under RCP4.5, in the near future and under RCP8.5 in the far future (2075–2099). Finally, the expected rate of fire spread is more spatially extended all over the country and particularly from southern to northern parts compared to the historical state.

show abstract

Section: Fire Weather Index Projectionssupporting

confidence: 94%

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As well as the effects on forest ecosystems, forest res can have signi cant economic and social consequences for local communities, and can be harmful or even fatal for humans living in the regions close to the burning areas (Kirsanov et al, 2020;Ntinopoulos et al, 2022). Although it is not comprehensively investigated, the effects of forest res in Namibia have severely affected economic, social, and environmental spheres in recent years.…”

Section: Summary Of the Resultsmentioning

confidence: 99%

Unveiling the Impact of Climate Variability on Forest Fire Occurrence in Namibia: A Modelling Study

Nikodemus

Trubin

Bernal

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Forest fire is the primary disturbance affecting the structure and composition of many forest ecosystems worldwide. Forest fires caused severe destruction to forest ecosystems in Namibia (South West Africa) during the period of 2011–2021. The aim of this paper is to assess the impacts of climate variability on forest fire occurrence in Namibia. Results A total of 38.184 Mha of forests was burned during the study period. Forest fire occurrence fluctuated in response to changes in climate variability (temperature, precipitation, and wind speed). Using linear regression models, ranked according to the Akaike information criterion (AIC), we found a combination of average wind speed in the 3-month period of July, August, September, and average monthly wind speed in January, May July, and August, to be the most important drivers of forest fires. Conclusions Forest fire occurrences are highly concentrated in the north-eastern and north-central regions. Forest fire occurrence is not limited to climate variability. There is little scientific evidence about other factors influencing forest fire occurrence in Namibia, in addition to climate variability.

show abstract

“…Due to the increased complexity of the fire weather index calculations [57], the index was not directly computed in GEE; instead, meteorological variables were obtained for a bounding box of the study region. What followed was the implementation of a Poisson model following the methodology of Ntinopoulos et al [58] in order to acquire an exponential relationship between the FWI values and its components that would be used in GEE to calculate the FWI.…”

Section: Fire Weather Index (Fwi)mentioning

confidence: 99%

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

et al. 2023

Self Cite

View full text Add to dashboard Cite

Wildfires are a natural phenomenon, which nowadays, due to the synergistic effect of increased human intervention and the escalation of climate change, are displaying an ever-increasing intensity and frequency. The underlying mechanisms present increased complexity, with the phenomenon itself being characterized by a significant degree of stochasticity. For the above reasons, machine learning models and neural networks are being implemented. In the current study, two types of neural networks are implemented, namely, Artificial Neural Networks (ANN) and Radial Basis Function Networks (RBF). These neural networks utilize information from the Fire Weather Index (FWI), Fosberg Fire Weather Index (FFWI), Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), aiming to predict ignitions in a region of Greece. All indices have been developed through the Google Earth Engine platform (GEE). In addition, a new index is proposed named “Vegetation-Enhanced FWI” (FWIveg) in order to enhance the FWI with vegetation information from the NDVI. To increase the robustness of the methodology, a genetic algorithm-based approach was used in order to obtain algorithms for the calculation of the new index. Finally, an artificial neural network was implemented in order to predict the Mati wildfire in Attica, Greece (23 July 2018) by applying the new index FWIveg, aiming to assess both the effectiveness of the new index as well as the ability to predict ignition events using neural networks. Results highlight the effectiveness of the two indices in providing joint information for fire prediction through artificial intelligence-based approaches.

show abstract

Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Cited by 13 publications

References 58 publications

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

Investigation of Fire Weather Danger under a Changing Climate at High Resolution in Greece

Unveiling the Impact of Climate Variability on Forest Fire Occurrence in Namibia: A Modelling Study

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

Contact Info

Product

Resources

About