农业工程学报2018，Vol.34Issue(9)：167-174,8.DOI:10.11975/j.issn.1002-6819.2018.09.020
基于遥感温度植被干旱指数的小蠹虫害预警
Prediction of bark beetles pests based on temperature vegetation dryness index
摘要
Abstract
Bark beetles(Scolytidae)are widely distributed in the world.In Yunnan Province,the southwest of China,Yunnan pine (Pinus yunnanensis) has been seriously damaged by bark beetles. Because Yunnan pine is one of the most important afforestation tree species in Yunnan Province, the government has implemented a series of measures to protect Yunnan pine from being damaged by bark beetles. However, it is very difficult to identify the bark beetle pest in the early stage due to the hysteresis symptoms. Based on the hypothesis of delay effect between drought and insect pests, one of the remote sensing drought indices TVDI (temperature vegetation dryness index) was proposed to predict the damage of bark beetles. TVDI was estimated by NDVI (normalized difference vegetation index) and brightness temperature in Shilin County where large area was damaged by drought and bark beetles. NDVI and brightness temperature were derived from long-term Landsat images from 2009 to 2014. The damage rating of Yunnan pine forests attacked by bark beetles was divided into 4 categories, including the healthy forests, lightly damaged forests, moderately damaged forests and severely damaged forests. TVDI was related to different damage ratings, and the relationship between TVDI and the difference of NDVI (dNDVI) before and after the Yunnan pine forest attacked by bark beetles was analyzed to effectively predict the possible occurrence of pests in the future. The dNDVI was used to stand for the real damage degree of Yunnan pine forests. To evaluate the relationship, lightly damaged forest polygons were selected in this study. However, only the pixels with dNDVI greater than 0 were extracted to eliminate those areas without being affected by insects in each polygon, and the larger polygon was split into many small polygons. In order to eliminate the difference of dNDVI range between different years, the maximum and minimum normalization method was used to normalize dNDVI. Results showed that the area and the number of attacked forest patches by beetles were declining from 2010 to 2015. TVDI in the healthy forest patches (0.657±0.114) was higher than that in the pest infected forest patches. Whereas, the value of TVDI (0.530±0.112) of the lightly damaged Yunnan pine forest patches was not significantly higher than that of the moderately damaged Yunnan pine forest patches (0.498±0.097) (P>0.05), but it was significantly higher than that of the severely damaged Yunnan pine forest patches (0.449±0.113) (P<0.05). And no significant difference was found between moderately and severely damaged Yunnan pine forests (P>0.05). Smaller TVDI generally corresponded to more serious degree of pest damages. The time series of TVDI spatial distribution also showed that TVDI was gradually increasing in recent years, while the pest infected area and the mean damage degree declined. Furthermore, TVDI was negatively correlated with dNDVI during the period of 2011, and a linear regression model could successfully express the relationship between them with the R2of 0.322. The linear model could predict the dNDVI in 2012 with the root mean square error (RMSE) of 0.237. However, the regression line deviated from 1:1 line, and other factors should be considered in the future, including site conditions, stand structure, and forest growth. Overall, the relatively humid areas were more likely to have bark beetle pests during the drought periods. According to the spatial distribution of TVDI, the area with lower TVDI would be prone to be infected by insect pests. However, the prediction of the spatial distribution of bark beetles pest still needs further study. Other than TVDI, the model should involve other factors. This study proposes constructive suggestion on the timely releasing pest monitoring information.关键词
干旱/遥感/虫害控制/温度植被干旱指数/小蠹Key words
drought/remote sensing/pest control/temperature vegetation dryness index/bark beetle分类
信息技术与安全科学引用本文复制引用
沈亲,邓槿,刘旭升,黄华国..基于遥感温度植被干旱指数的小蠹虫害预警[J].农业工程学报,2018,34(9):167-174,8.基金项目
国家自然科学基金(41571332) （41571332）
林业公益性行业科研专项"重大森林虫灾监测预警的关键技术研究"(201404401) （201404401）
