微分光谱连续小波系数估测雅氏落叶松尺蠖危害下的落叶松失叶率

害虫引起的林木失叶会严重威胁森林健康。森林虫害遥感监测与评价中快速、准确获取失叶信息十分重要。基于此,针对雅氏落叶松尺蠖引起的落叶松失叶灾象,在蒙古国开展受害林木光谱测量和失叶率估测试验。首先通过光谱实测数据的处理,得到微分光谱反射率（DSR,对光谱反射率求一阶导数）和微分光谱连续小波系数（DSR-CWC,利用Biorthogonal,Coiflets,Daubechies和Symlets等4种小波系的36个母小波基函数对DSR进行连续小波变换）,分析DSR和DSR-CWC对失叶率的敏感性,进而借助MATLAB的Findpeaks（Fp）函数自动寻找DSR和DSR-CWC的敏感波段并确定其对应的敏感特征,然后利用连续投影算法（SPA）对敏感特征进行降维处理,最后利用敏感特征建立偏最小二乘回归（PLSR）和支持向量机回归（SVMR）失叶率估测模型,并与逐步多元线性回归（SMLR）模型进行比较。研究结果表明：①DSR-CWC与DSR相比,对失叶率变化的敏感性更显著且敏感波段亦较多,其敏感波段主要分布于三个吸收谷（440～515,630～760和1 420～1 470 nm）和三个反射峰（516～620,761～1 000和1 548～1 610 nm）范围内。说明DSR-CWC能够增强光谱反射和吸收特征。②Fp与SPA结合模式（Fp-SPA）不仅能够快速、客观选择敏感特征,而且对特征有效降维,是一种光谱敏感特征选择的有效方法。③4种小波系的最优母小波基分别为bior2.4,coif2,db1和sym6,其中db1的失叶率估测性能最稳定,精度最高。④对DSR进行连续小波变换能够提高失叶率估测精度,在DSR-CWC中db1-PLSR模型（R2_M=0.934 0,RMSE_M=0.089 0）提高的最为显著,比DSR-PLSR的R2_M提高了0.047 5并且比DSR-PLSR的RMSE_M降低了0.024 9。⑤利用DSR-CWC建立的PLSR和SVMR模型估测精度类似,其精度优于SMLR模型。可见,DSR-CWC比DSR失叶率估测更有潜力,可为森林虫害遥感监测中提供重要参考。     

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.