基于近紅外光譜的林內(nèi)枯葉跨林分間模型遷移的含水率檢測(cè)方法

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中圖分類號(hào)：S762.2 文獻(xiàn)標(biāo)識(shí)碼：A DOI：10.7525/j.issn.1006-8023.2025.03.001

Abstract：Themoisturecontentofforestfloorliterisakeyfactorinforestfireoccurrences，nditsaccuratedetectionis crucial for fire prevention.Near-infrared spectroscopy（NIRS）can directly invert moisturecontent from spectral data， enabling rapid detectionof liter moisture content.However，spectral characteristics difer between fuel types due to variations inlightintensitydataatdiffrent wavelengths，requiringseparatedetectionmodelsforliterfrom dierenttree species to match specific light intensity-moisture content inversion relationships.Collcting and labeling spectral data across differentforeststands is time-consuming，limitingthepracticalapplicationofthespectralmethod.Toaddresthis issue，this study proposesa moisturecontent detection method for forest floor liter basedon Bi-LSTM（Bidirectional Long Short-Term Memory）transferlearning.By transfering the trained modelparameters to new models，we avoid training models from scratch，thereby improving model learning eficiencyand reducing the data required fortraining.The studydemonstrates thatthe Bi-LSTMmethodsurpases the traditional inversion approach using LSTMin termsof detection accuracy.Specifically，the mean absolute errr （MAE）for Quercus mongolicaand Larix gmelinii is reduced by

0 . 6 2 % and 0 . 8 7 % ，respectively，while the mean squared error（MSE）is reduced by 0 . 2 8 % and 0 . 7 0 % ，respectively. Moreover，the Bi-LSTM-based transfer learning approach significantly lessens the reliance on labeled NIR spectraldata. With a target domain sample size of 3OO and a source domain sample size of 1 0 0 0 ，the detection model record an MAE of 3 . 2 7 % ，an MSE of 1. 10 % ，and an of 0.918.When compared to models without source domain training，the MAE and MSE show reductions of 2. 3 6 % and 1 . 0 2 % ，respectively，and an increase in of 0.114.A comparative analysis before andafterimplementingtransferlearningreveals thathismethodologyoffersanovelstrategytodiminishthetimecostasociated withmodeling moisturecontentin spectralitterand to enhancethepracticalapplicationof spectraldetection. Keywords：Liter fall；moisture content;transfer learning；deep learning;near-infrared spectrum

0 引言

林內(nèi)可燃物作為森林火災(zāi)產(chǎn)生的必要條件之二[1]，其中森林地表枯葉含水率（dead fuel forest mois-turecontent，DFFMC）作為一級(jí)引火要素是引起森林火災(zāi)的重要因素[2]。（剩余16370字）