Modeling Forest Lightning Fire Occurrence in the Daxinganling Mountains of Northeastern China with MAXENT

Abstract

Forest lightning fire is a recurrent and serious problem in the Daxinganling Mountains of northeastern China. Information on the spatial distribution of fire danger is needed to improve local fire prevention actions. The Maxent (Maximun Entropy Models), which is prevalent in modeling habitat distribution, was used to predict the possibility of lightning fire occurrence in a 1 × 1 km grid based on history fire data and environment variables in Daxinganling Mountains during the period 2005-2010.We used a jack-knife test to assess the independent contributions of lightning characteristics, meteorological factors, topography and vegetation to the goodness-of-fit of models and evaluated the prediction accuracy with the kappa statistic and AUC (receiver operating characteristic curve) analysis. The results showed that rainfall, number of strikes and lightning current intensity were major factors, and vegetation and geographic variable were secondary, in affecting lightning fire occurrence. The predicted model performs well in terms of accuracy, with an average AUC and maximum kappa value of 0.866 and 0.782, respectively, for the validation sample. The prediction accuracy also increased with the sample size. Our study demonstrated that the Maxent model can be used to predict lightning fire occurrence in the Daxinganling Mountains. This model can provide guidance to forest managers in spatial assessment of daily fire danger. OPEN ACCESSForests 2015, 6 1423

Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

“…As shown in Figure 4, most of the lightning-caused fires (i.e., >98%) in all of the natural subregions happened during the growing season (i.e., from snow disappearance date in spring to the start of snowfall in autumn). The reason for such a trend might be related to the extensive availability of fuels, and the occurrence of the lightning storms as a fire ignition source in the region [7,39,40]. Even though we observed that a significant proportion of the fires took place during the growing season, we also observed a small amount of fires (i.e., <2%) before the growing season started, which could be related to dead branches, peat, or duff beneath the trees that remained from the previous year, and were initially ignited by lightning strikes in late winter [41][42][43].…”

“…However, validation showed a relatively weak relationship (R 2 = 0.44) between the modeled and observed values. In another study conducted over the Daxinganling Mountains of Northeastern China, Chen et al [7] predicted lightning-caused fire occurrences at 1-km spatial resolution during the 2005-2010 period. In this case, they used several datasets, including: (i) historical fire data, i.e., number of lightning strikes and lightning current intensity; (ii) meteorological data, i.e., rainfall, temperature, relative humidity, and wind speed; (iii) topographical elements, i.e., elevation, slope, and aspect; and (iv) fuel type, i.e., larch forest, Scots pine forests, mixed forest with larch, birch and oak trees, mixed forest with Scots pine, birch and oak trees, and grass.…”

Historical trends of forest fires and carbon emissions in China from 1988 to 2012

“…In China, research has focused on occurrences of lightning‐ignited forest fires [ Chen et al ., ], the relationships between fire occurrence and climate drivers [ Liu et al ., ; Ullanm et al ., ], and the impacts of fires on forest ecosystems [ Lü et al ., ; X. Li et al ., ]. These studies have improved understanding of fire characteristics and their effects on forest ecosystem functions at the stand or local scale.…”

“…In China, research has focused on occurrences of lightning-ignited forest fires [Chen et al, 2015], the relationships between fire occurrence and climate drivers [Liu et al, 2012;Ullanm et al, 2013], and the impacts of fires on forest ecosystems [Lü et al, 2006;X. Li et al, 2014].…”

Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

“…Meteorological features like average precipitation, temperature, humidity and wind speed have been used to understand forest re characteristics. In other studies lightening has been focused to predict forest re (Chen et al 2015).…”