首页> 外文会议>International Geoscience and Remote Sensing Symposium >FIELD SPECTROMETRY OF PAPYRUS VEGETATION (CYPERUS PAPYRUS L.) IN SWAMP WETLANDS OF ST LUCIA, SOUTH AFRICA
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FIELD SPECTROMETRY OF PAPYRUS VEGETATION (CYPERUS PAPYRUS L.) IN SWAMP WETLANDS OF ST LUCIA, SOUTH AFRICA

机译:南非圣卢西亚沼泽湿地(Cypetus papyrus L.)的场光谱法

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Techniques for discriminating species in swamp wetlands are critical for rapid wetlands assessment and proactive management. In this study, we tested whether field spectrometry could discriminate between papyrus swamp and its co-existence species (Phragmites australis, Echinochloa pyramidalis, and Thelypteris interrupta). Canopy spectral measurements were taken from the species using Analytical Spectral Devices but later resampled to Hyperspectral Mapper (HYMAP) resolution. The random forest algorithm and a forward variable selection technique were used to identify key wavelengths for discriminating the species. This method yielded ten wavelengths (1409 nm, 710 nm, 437 nm, 464 nm, 452 nm, 1424 nm, 725 nm, 480 nm, 587 nm, and 603 nm) located in the visible and SWIR portions of the electromagnetic spectrum with lowest out-of-bag estimate error rate of 9.5%. The use of random forest as a classification algorithm resulted in overall accuracy of 90.5% and a KHAT value of 0.87 for all class pairs (n = 6), with individual class accuracies ranging from 93.73% to 100%. The study also demonstrated the possibility to scale up the method to airborne sensors such as HYMAP for discriminating swamp wetland species.
机译:沼泽湿地中鉴别物种的技术对于快速湿地评估和主动管理至关重要。在这项研究中,我们测试了场光谱法是否可以区分纸莎草沼泽及其共存物种(芦苇澳大利亚,echinochloa Pyramidalis和ThelyPteris中断)。使用分析光谱装置从物种中取出冠层谱测量,但后来重新采样到高光谱映射器(Hymap)分辨率。随机森林算法和正向变量选择技术用于识别用于区分物种的关键波长。该方法产生十个波长(1409nm,710nm,437nm,464nm,452nm,1424nm,725nm,480nm,587nm和603nm),位于电磁光谱的可见和Swir部分,最低禁止的估计错误率为9.5%。随机森林的使用作为分类算法导致总体精度为90.5%和所有类对(n = 6)的khat值为0.87,各种阶级精度范围为93.73%至100%。该研究还证明了将该方法扩大到空中传感器(如Hymap)的方法,以辨别沼泽湿地物种。

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