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Assessing The Use Of Multiseason Quickbird Imagery For Mapping Invasive Species In A Lake Erie Coastal Marsh

机译:评估多季节快鸟影像在伊利湖沿岸沼泽地入侵物种制图中的应用

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QuickBird multispectral satellite images taken in September 2002 (peak biomass) and April 2003 (pre-growing season) were used to map emergent wetland vegetation communities, particularly invasive Phragmites australis and Typha spp., within a diked wetland at the western end of Lake Erie. An unsupervised classification was performed on a nine-layer image stack consisting of all four spectral bands from both dates plus a September Normalized Difference Vegetation Index image. The resulting eight cover classes distinguished three monodominant genera {Phragmites australis, Typha spp., Nelumho luted), three multigenera plant communities (wet meadow, other non persistent emergents, woody vegetation), and two unvegetated cover types (water, bare soil). Field validation at 196 data points yielded an overall classification accuracy of 62%, with producer's accuracy for the eight individual classes ranging from 41 to 91% and user's accuracy from 17 to 90%. Three-fourths of areas designated as Phragmites were correctly mapped, but 14% were found to be cattail (Typha) during field validation. Lotus (Nelumho lutea) beds were accurately mapped on multiseason imagery (producer's accuracy = 91%); these beds had not yet emerged above water in April, but were fully developed in September. Other types of non persistent vegetation were confused with managed areas in which vegetation had been cut and burned to control invasive Phragmites. Multiseason QuickBird imagery is promising for distinguishing certain wetland plant species, but should be used with caution in highly managed areas where vegetation changes may reflect human alterations rather than phonological change.
机译:在2002年9月(生物量峰值)和2003年4月(生长前的季节)拍摄的QuickBird多光谱卫星图像用于绘制伊利湖西端堤防湿地内的新兴湿地植被群落,特别是入侵的芦苇和香蒲属。 。在由两个日期的所有四个光谱带以及九月归一化差异植被指数图像组成的九层图像堆栈上执行了无监督分类。由此产生的八种覆盖类别区分了三个单一优势属(芦苇,香蒲属,Nelumho luted),三个多属植物群落(湿草甸,其他非持久性出土,木本植被)和两种无植被的覆盖类型(水,裸土)。在196个数据点上进行的现场验证产生了62%的总体分类准确度,八个类别的生产者准确度从41%到91%不等,而用户准确度从17%到90%不等。正确绘制了被指定为芦苇的四分之三的区域,但在田间验证期间发现14%是香蒲(香蒲)。将莲花(Nelumho lutea)病床准确地映射到了多个季节的影像上(生产者的准确性= 91%);这些床在四月尚未浮出水面,但在九月已完全发育。其他类型的非持久性植被与被砍伐和焚烧以控制入侵芦苇的管理区域相混淆。 Multiseason QuickBird影像有望用于区分某些湿地植物种类,但在高度管理的区域中应谨慎使用,这些区域的植被变化可能反映出人类的变化而不是语音变化。

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