文章基本信息

标题：SPECTRAL DIFFERENTIATION OF SIX AQUATIC WEEDS IN LAKE CHIVERO, ZIMBABWE
本地全文：下载
作者：Munyaradzi D Shekede ; Karin Schmidt ; Samuel Kusangaya 等
期刊名称：The Journal of Sustainable Development in Africa (JSDA)
电子版ISSN：1520-5509
出版年度：2013
卷号：15
期号：3
出版社：Institute of Sustainable Development in Africa
摘要：Altho ugh aquatic plants are fundamental to the functioning of aquatic ecosystem, their proliferation requires a certain level of control and management. Spectral analysis o f aquatic weeds offers an opportunity for understanding the distributio n and extent of specific aquatic weeds including evolution of weed invasion, propagation and colonization of affected areas. The objective of this study was to measure and differentiate aquatic weeds (Hydrocotyle ranunculoides (Spaghetti Weed), Eichhornia crassipes (Water hyacinth), Pistia stratiotes (Nile cabbage or Water lettuce), Typha capensis (Common bullrush) and Phragmites australis (Common reed) in Lake Chivero, Zimbabwe based on their spectral characteristics. An LI1800 portable spectroradiometer was used for collecting aquatic weed spectral data at 10 -nm wavelength interval. The null hypothesis, that there was no significant difference in the mean radiance of the six aquatic weed species, was tested using ANOV A and Tukey's Ho nest S ignificant Difference test at 0.05 significance level. The study showed that mo st of the aquatic weeds analysed in the study possess unique spectral characteristics which provided a basis for significant (p<0.05) spectral separation of these macrophytes. The research further revealed that increased spectral separability of aquatic weeds is better in lo ng wavelength region than in short wavelength region of the electromagnetic spectrum. There is need for more research on spectral separabilty of aquatic weeds, not only in Lake Chivero, but in all water bodies which are at risk of aquatic weed invasions, especially using airborne hyper spectral techniques in order to cover larger representative areas
关键词：aquatic weeds; hyperspectral remote sensing; sep arability