雅氏落叶松尺蠖不同危害程度下林木冠层颜色高光谱判别

Hyperspectral Discrimination of Different Canopy Colors in Erannis Jacobsoni Djak-Infested Larch

针叶害虫爆发将会削减林木针叶水分和叶绿素含量,导致林木冠层颜色发生变化,甚至使林木死亡。这严重威胁森林生态系统健康安全。通过遥感技术监测受害林木冠层颜色变化,可对虫害林生态系统安全进行快速评估。因此,虫害林木冠层不同颜色判别研究极为重要。基于此,选择蒙古国肯特省和杭爱省的3个雅氏落叶松尺蠖爆发林区(binder, Ikhtamir和Battsengel)为试验区,开展落叶松受害过程冠层颜色变化信息调查和光谱测量试验,并利用高光谱特征和机器学习算法判别了落叶松冠层不同颜色。首先通过虫害灾区林木调查对冠层颜色进行了分色,即绿色、黄色、红色和灰色。同时根据不同危害程度下林木冠层不同颜色,从试验区选取66棵样本树,并对其冠层进行了光谱测量。其次以样本树光谱反射率曲线为基础数据,计算平滑光谱反射率(SSR)、微分光谱反射率(DSR)和平滑光谱连续小波系数(SSR-CWC)等高光谱特征,并借助方差分析法揭示了这些高光谱特征对冠层不同颜色的敏感性。再次采用Findpeaks函数和连续投影算法结合模式(Findpeaks-SPA)快速提取了SSR, DSR和SSR-CWC等高光谱特征的敏感特征。最后通过随机森林分类(RF)和支持向量机分类(SVMC)算法构建雅氏落叶松尺蠖虫害林木冠层不同颜色判别模型,并与费歇尔判别(FD)模型进行比较,评价了判别模型精度。研究发现:(1)可见光的多个波段, SSR-CWC对冠层不同颜色表现出了极显著的敏感性。(2)基于Findpeaks-SPA模式能够有效提取敏感高光谱特征,该模式不仅大大降低高光谱特征数量,而且改善了多重共线性问题。(3)判别冠层不同颜色最有潜力的高光谱特征为SSR-CWC,其Daubechies系、 Biorthogonal系、 Coiflets系和Symlets系的最优小波基分别为db9, bior1.5, coif1和sym4,其中db9-RF(SVMC)达到了最高的判别总体精度(0.900 0)。这比SSR-RF(SVMC)和DSR-RF(SVMC)模型分别提高了0.250 0(0.450 0)和0.250 0(0.100 0)。(4)基于DSR和SSR-CWC的RF和SVMC模型判别精度优于FD模型,尤其db9-RF(SVMC)模型更为明显,其判别总体精度和Kappa系数比db9-FD模型分别提高了0.150 0和0.167 0。可见,在虫害林木冠层不同颜色判别中db9-RF(SVMC)有极大潜力。这为林业和生态安全相关部门对森林虫害严重程度进行遥感监测提供重要参考和实用价值。

The outbreak of conifer pests will reduce the water content and chlorophyll content of the conifer trees, cause the forest canopy color to change, and even cause the forest to die, which seriously threatens the health and safety of coniferous forest ecosystem. The remote sensing monitoring of forest canopy color change can be used to evaluate the security of forest ecosystem quickly, so the study of forest canopy color discrimination is very important. Therefore, this study selected three outbreak forest areas(Binder, Ikhtamir and Battsengel) of Erannis Jacobsoni Djak in Khentiy and Hangay province of Mongolia as the experimental areas. The investigation of canopy color change information and spectrum measurement experiment in the process of larch damage was carried out. The hyperspectral characteristics and machine learning algorithm were used to distinguish different colors of Larch canopy. Firstly, through the investigation of forest in the disaster area, the color of the canopy was divided into green, yellow, red and gray. At the same time, according to the different canopy colors of healthy and damaged trees, 66 sample trees were selected from the experimental area, and their spectral canopy were measured. Secondly, the hyperspectral characteristics such as smooth spectral reflectance(SSR), differential spectral reflectance(DSR) and smooth spectral continuous wavelet coefficient(SSR-CWC) were calculated based on the canopy spectral curve of the sample tree, and the sensitivity of these hyperspectral characteristics to different colors on the canopy is revealed by means of variance analysis. Thirdly, the sensitive features of SSR, DSR and SSR-CWC were extracted quickly by using Findpeaks function and continuous projection algorithm pattern(Findpeaks-SPA). At last, the models of different color discrimination of larch tree canopy were constructed by using the random forest classification(RF) and support vector machine classification(SVM) algorithm. And compared with the Fisher discriminant(FD) model, the accuracy of the discriminant models were evaluated. The results show that: ①In multiple wavelengths of visible light, SSR-CWC showed extremely significant sensitivity to different canopy colors. ②The sensitive hyperspectral features can be extracted effectively based on Findpeaks-SAP pattern, which reduces the number of hyperspectral features and improves multicollinearity of the model. ③SSR-CWC is the most potential hyperspectral feature to distinguish different colors on the canopy. The optimal wavelet bases of Daubechies, Biothogonal, Coiflets and Symlets are db9, bior1.5, coif1 and sym4, respectively. Among them, db9-RF(SVMC) reaches the highest overall discrimination accuracy(0.900 0). It is 0.250 0(0.450 0) and 0.250 0(0.100 0) higher than the SSR-RF(SVMC) and DSR-RF(SVMC) models. ④The discrimination accuracy of RF and SVMC models based on DSR and SSR-CWC is better than that of FD model, especially db9-RF(SVMC) model, which overall discrimination accuracy and kappa coefficient are 0.150 0 and 0.167 0 higher than db9-FD model, respectively. It can be seen that db9-RF(SVMC) has great potential in different color discrimination of forest canopy, which can provide important reference and practical value for remote sensing monitoring of forest pest severity in forestry and ecological security related departments.