森林工程2025，Vol.41Issue(3)：439-450,12.DOI:10.7525/j.issn.1006-8023.2025.03.001
基于近红外光谱的林内枯叶跨林分间模型迁移的含水率检测方法
Moisture Content Detection Method in Forest Floor Litter Model Transfer Across Stands Using Near-Infrared Spectroscopy
摘要
Abstract
The moisture content of forest floor litter is a key factor in forest fire occurrences,and its accurate detection is crucial for fire prevention.Near-infrared spectroscopy(NIRS)can directly invert moisture content from spectral data,enabling rapid detection of litter moisture content.However,spectral characteristics differ between fuel types due to variations in light intensity data at different wavelengths,requiring separate detection models for litter from different tree species to match specific light intensity-moisture content inversion relationships.Collecting and labeling spectral data across different forest stands is time-consuming,limiting the practical application of the spectral method.To address this issue,this study proposes a moisture content detection method for forest floor litter based on Bi-LSTM(Bidirectional Long Short-Term Memory)transfer learning.By transferring the trained model parameters to new models,we avoid train-ing models from scratch,thereby improving model learning efficiency and reducing the data required for training.The study demonstrates that the Bi-LSTM method surpasses the traditional inversion approach using LSTM in terms of detec-tion accuracy.Specifically,the mean absolute error(MAE)for Quercus mongolica and Larix gmelinii is reduced by 0.62%and 0.87%,respectively,while the mean squared error(MSE)is reduced by 0.28%and 0.70%,respectively.Moreover,the Bi-LSTM-based transfer learning approach significantly lessens the reliance on labeled NIR spectral data.With a target domain sample size of 300 and a source domain sample size of 1 000,the detection model record an MAE of 3.27%,an MSE of 1.10%,and an R² of 0.918.When compared to models without source domain training,the MAE and MSE show reductions of 2.36%and 1.02%,respectively,and an increase in R² of 0.114.A comparative analysis before and after implementing transfer learning reveals that this methodology offers a novel strategy to diminish the time cost asso-ciated with modeling moisture content in spectral litter and to enhance the practical application of spectral detection.关键词
枯叶凋落物/含水率/迁移学习/深度学习/近红外光谱Key words
Litter fall/moisture content/transfer learning/deep learning/near-infrared spectrum分类
林学引用本文复制引用
张佳薇,姜天,杨春梅,刘强,韩哲,刘泽盛,李明宝..基于近红外光谱的林内枯叶跨林分间模型迁移的含水率检测方法[J].森林工程,2025,41(3):439-450,12.基金项目
中央财政林业科技推广示范项目资助(黑(2023)TG25号). （黑(2023）
