首页> 外文期刊>International Journal of Forestry Research >Identifying Subalpine Fir (Abies lasiocarpa) Attacked by the Balsam Woolly Adelgid (Adelges piceae) Using Spectral Measurements of the Foliage
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Identifying Subalpine Fir (Abies lasiocarpa) Attacked by the Balsam Woolly Adelgid (Adelges piceae) Using Spectral Measurements of the Foliage

机译:使用叶片的光谱测量来识别遭受苦瓜(Balsam Woolly Adelgid)侵袭的亚高山冷杉(Abies lasiocarpa)

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Balsam woolly adelgid is an invasive pest of firs in the United States. Aerial surveys are conducted for detection of adelgid infestations but other remotely sensed data may also be useful. Our objective was to determine if high spectral resolution, branch-level data can be used to distinguish infested from noninfested trees. Stepwise discriminant analysis yielded a three-variable model (the red-green index and two narrow-bands (one at 670 nm and the other at 1912 nm)) that classified infested versus non-infested trees with 94% accuracy compared with the 83% accuracy obtained with a single-variable model. The response of trees in narrow spectral bands was integrated across wavebands to simulate measurements from the multispectral SPOT5-HRVIR sensor. Stepwise discriminant analysis again yielded a three-variable model (simple ratio, the SPOT5-HRVIR band in the SWIR region and NDVI) with similar accuracy (93%) at discriminating infested from non-infested trees compared with the 83% accuracy obtained with a single-variable model.
机译:苦瓜羊毛adelgid是美国冷杉的一种入侵性害虫。进行了航空勘测,以检测adelgid感染,但其他遥感数据也可能有用。我们的目标是确定是否可以使用高光谱分辨率,分支级别的数据来区分受感染树和未受感染树。逐步判别分析得出了一个三变量模型(红绿色指数和两个窄带(一个在670 nm,另一个在1912 nm),将受侵染与未受侵染的树木分类的准确度为94%,而准确度为83%使用单变量模型获得的精度。跨波段集成了窄光谱带中树木的响应,​​以模拟多光谱SPOT5-HRVIR传感器的测量结果。逐步判别分析再一次产生了一个三变量模型(简单比率,SWIR区域中的SPOT5-HRVIR波段和NDVI),在区分未受侵染的树木时,具有相似的准确度(93%),而使用A的准确度则为83%单变量模型。

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