Frequent and intense anthropogenic fires present meaningful challenges to forest management in the boreal forest of China. Understanding the underlying drivers of human-caused fire occurrence is crucial for making effective and scientifically-based forest fire management plans. In this study, we applied logistic regression (LR) and Random Forests (RF) to identify important biophysical and anthropogenic factors that help to explain the likelihood of anthropogenic fires in the Chinese boreal forest. Results showed that the anthropogenic fires were more likely to occur at areas close to railways and were significantly influenced by forest types. In addition, distance to settlement and distance to road were identified as important predictors for anthropogenic fire occurrence. The model comparison indicated that RF had greater ability than LR to predict forest fires caused by human activity in the Chinese boreal forest. High fire risk zones in the study area were identified based on RF, where we recommend increasing allocation of fire management resources.

Fuel moisture affects fuel ignition potential and fire behaviour. To accurately model fire behaviour, predict fuel ignition potential and plan fuel reduction, fuel moisture content must be assessed regularly and often. To establish models for Daxinganling Region, which has the most severe forest fires in China, hourly measurements were taken of moisture content in litter beds of larch stands sampled under different shading and slope conditions. Models were established using three vapour-exchange methods. The Nelson and Simard methods employed a direct timelag method using a timelag concept and the Nelson and Simard equilibrium moisture content (EMC) functions and estimating model parameters directly from fuel moisture and weather observation data in the field. The direct regression method used equations directly derived from linear regression of fuel moisture and field weather variation. The mean absolute error and mean relative error were determined for the Nelson (0.78%, 4.98%), Simard (1.04%, 5.57%) and direct regression (1.48%, 9.01%) methods. Only the models established using the direct timelag methods met the 1% accuracy requirement using either the Nelson or Simard EMC function, confirming the suitability and robustness of the direct timelag methods. The Simard and Nelson methods had similar accuracy, but Simard was more robust and only needed estimation of one parameter and hence is recommended for predicting litter moisture in this region.

Modelling the drying process of fuel moisture with initial moisture content above the fibre saturation point can be used to determine when fuel will become sufficiently dry (after precipitation) to burn and provide a more accurate prediction of fire potential. Based on analysis of the mechanism by which the drying process occurs, we propose a model comprising two phases distinguished by a moisture threshold of 0.35 g g–1, the fibre saturation point; one phase is controlled by evaporation and the other by diffusion. Each phase has a distinct equation with a different timelag. We compared our two-phase model with a one-phase model (one-timelag model) and another two-phase model by estimating drying of 15 Scots pine (Pinus sylvestris var. mongolica) needle fuelbeds. The results indicate that the two-timelag model improves moisture modelling, thereby reducing mean absolute error by more than 30%, i.e. from 0.0047 g g–1 (one-phase model) to 0.0030 g g–1. The model yields consistent results, further suggesting its potential for improving fuel moisture prediction of fire danger rating systems. The first timelag of the model is affected by fuelbed properties. Equations based on variables that represent fuelbed properties were established, thus saving time when estimating parameters for stand-specific fuel moisture models.

A high spatial resolution QuickBird satellite image and a low spatial but high spectral resolution Landsat Thermatic Mapper image were used to linearly regress fuel loads of 70 plots with size 30 × 30 m over the Daxinganling region of north-east China. The results were compared with loads from field surveys and from regression estimations by surveyed stand characteristics. The results show that fuel loads were related to stand characteristics, such as stand mean diameter at breast height and stand height. As the QuickBird image using the shadow fraction method represented the stand characteristics well, fuel loads were well estimated from the QuickBird image. QuickBird estimations outperformed those from the lower spatial resolution Thermatic Mapper image. For many fuel classes, the QuickBird estimations were as good as those regressed from surveyed stand characteristics, and thus similar to the surveyed fine and total dead fuel loads. However, coarse fuel loads were not estimated as well using both satellite images owing to their intrinsic low association with stand characteristics. Despite this limitation in estimating coarse fuels, very-high-resolution images such as QuickBird are still valuable in estimating fine fuels, which are critically important in the practice of fire management.

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