Monitoring model of dendrolimus tabulaeformis disaster using hyperspectral remote sensing technology.
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摘要: 油松毛虫对人工油松林造成了严重的危害。据统计,辽宁省油松毛虫的发生面积为12万hm2/a,年均直接经济损失340万元。利用遥感技术特别是高光谱遥感大面积及时监测病虫害是今后林业病虫害监测的发展方向之一。高光谱遥感技术可以为植物叶绿素和含水率等生物化学参数的定量化诊断提供简便、有效以及非破坏性的数据采集和处理方法。本研究采用野外便携式光谱仪测定不同失叶率油松的高光谱反射率数据,使用分光光度计室内测定相应叶片的叶绿素含量,采用烘干法测定叶片含水率。通过计算归一化光谱指数(NDSI)、比值光谱指数(RSI)、差值光谱指数(DSI)与叶绿素a含量、叶绿素b含量、含水率的相关系数,选择相关系数最高的光谱指数作为核心光谱指数。以核心光谱指数为自变量,失叶率为因变量建立回归模型,采用逐步回归法进行变量筛选,筛选出包含DSI(808,816)、RSI(482,494)、NDSI(881,920)、NDSI(907,919)的光谱指数集作为最佳回归模型的自变量,应用R语言的函数lm()获得最佳回归模型y=1.781 8－3.172 4×NDSI(808,816)－0.960 6×RSI(482,494)－2.196 7×NDSI(881,920)－1.241 9×NDSI(907,919),R2为0.786。模型检验结果显示,最佳回归模型的均方根误差(RMSE)为0.089,相对误差(RE)为11.7%,预测效果良好,表明该模型可用于估算油松失叶程度,有助于对油松的受害情况做出综合分析,提高油松毛虫灾害监测的精度,克服了使用单一叶绿素指标或含水率指标的片面性和局限性。Abstract: Dendrolimus tabulaeformis has caused serious damage to Pinus tabuliformis. According to the statistics, there are 0.12 million hectares of destroyed area in Liaoning Province of northeastern China every year, and the direct economic loss is 3.4 million CNY per year. The application of remote sensing technology, especially hyperspectral remote sensing to pest monitoring is one of the future directions of forestry pest monitoring. Hyperspectral remote sensing technology can provide a simple,effective and non-destructive data acquisition,which can offer processing method for quantifying diagnosis plant chlorophyll content and moisture content as well. This study uses the field portable spectrometer to measure the reflectance spectrum of trees with different leaf loss rates, uses the spectrophotometer to measure leaf chlorophyll content and measures the leaf moisture content with drying method. To calculate the linear relationship between normalization spectrum index (NDSI), ratio spectrum index (RSI), differential spectrum index (DSI) and chlorophyll a content, chlorophyll b content, moisture content, the spectrum index with maximum value of linear relationship was the key spectrum index. This study constructs regression model between the key spectrum indexes and the leaf loss rate, selects the spectrum index jet including DSI(808,816), RSI(482,494), NDSI(881,920), NDSI(907,919) with the stepwise regression algorithm, and builds the regression equation y=1.781 8－3.172 4×NDSI(808,816)－0.960 6×RSI(482,494)－2.196 7×NDSI(881,920)－1.241 9×NDSI(907,919) , whose R2 was 0.786. The root mean square error (RMSE) was 0.089 and relative error (RE) was 11.7%, which shows that this model is good and can be used to estimate the degree of leaf loss rate of P. tabuliformis. This model will help to make comprehensive analysis of the victimization degree of P. tabuliformis and improve the accuracy of pest monitoring degree, and overcome the sidedness and limitation of using single indicator of chlorophyll or moisture content.
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