CAJ | 학술논문

为快速测定森林土壤的有机碳含量，从取自小兴安岭带岭林业局东方红林场的120个土壤样品中采集350～2500 nm的土壤近红外光谱数据，对光谱做一定的预处理后，运用主成分分析法压缩提取前8个主成分，结合BP神经网络非线性方法建立土壤有机碳含量的预测模型并进行验证。结果表明，验证集的相关系数为0.78002，均方根误差为0.5002，预测集的相关系数为0.84941，均方根误差为0.4538。应用近红外光谱技术及BP神经网络非线性方法建模可以有效地预测土壤的有机碳含量，为野外大面积快速测定森林土壤碳含量提供了技术依据。
위쾌속측정삼림토양적유궤탄함량，종취자소흥안령대령임업국동방홍림장적120개토양양품중채집350～2500 nm적토양근홍외광보수거，대광보주일정적예처리후，운용주성분분석법압축제취전8개주성분，결합BP신경망락비선성방법건립토양유궤탄함량적예측모형병진행험증。결과표명，험증집적상관계수위0.78002，균방근오차위0.5002，예측집적상관계수위0.84941，균방근오차위0.4538。응용근홍외광보기술급BP신경망락비선성방법건모가이유효지예측토양적유궤탄함량，위야외대면적쾌속측정삼림토양탄함량제공료기술의거。
To rapidly determine forest SOC content , the spectra of 120 soils samples from Dongfanghong forest farm of Dailing Forestry Bureau located in the northeast Lesser Khingan Mountains were scanned with a vis -NIR spectrometer in the 350 to 2 500 nm after some pretreats , and the first eight principal components were compressed and gained by principal component analysis ( PCA ) . With combination of BP neural network nonlinear method , the prediction model of SOC content were established and validated .The result showed that the correlation coeffi-cient (R) and root mean square error (RMSE) of validation and test set were 0.780 02, 0.849 41 and 0.500 2, 0.453 8 respectively .In this sense , NIR technology and BP neural network nonlinear method could be a good tool in the prediction of SOC content , and could provide a feasibility to determine forest SOC content in the field widely and quickly .