Abstract:

Dead fine fuel moisture content (DFFMC) is a key factor affecting the spread of forest fires, which plays an important role in evaluation of forest fire risk. In order to achieve high-precision real-time measurement of DFFMC, this study established a long short-term memory (LSTM) network based on particle swarm optimization (PSO) algorithm as a measurement model. A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed. The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set. The surface fine dead fuel of Mongolian oak (Quercus mongolica Fisch. ex Ledeb.), white birch (Betula platyphylla Suk.), larch (Larix gmelinii (Rupr.) Kuzen.), and Manchurian walnut (Juglans mandshurica Maxim.) in the maoershan experimental forest farm of the Northeast Forestry University were investigated. We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy, meteorological, and spectral meteorological fusion methods. The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1% for Mongolian oak, 1.3% for white birch, 1.4% for larch, and 1.8% for Manchurian walnut, and these values were lower than those of the near-infrared method and the meteorological method. The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.

Key words: Near infrared spectroscopy, Meteorological factors, Data fusion, Long-term and short-term memory network, Particle swarm optimization algorithm