Detecting bugweed (Solanum mauritianum) abundance in plantation forestry using multisource remote sensing

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• 作者：Kabir Peerbhay ; ^{Peerbhaykabir@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Onisimo Mutanga ; Romano Lottering ; Victor Bangamwabo ; Riyad Ismail
• 关键词：Remote sensing ; LiDAR ; Weed detection ; Sparse partial least squares discriminant analysis (SPLS-DA)
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：121
• 期：Complete
• 页码：167-176
• 全文大小：2999 K

文摘

The invasive weed Solanum mauritianum (bugweed) has infested large areas of plantation forests in KwaZulu-Natal, South Africa. Bugweed often forms dense infestations and rapidly capitalises on available natural resources hindering the production of forest resources. Precise assessment of bugweed canopy cover, especially at low abundance cover, is essential to an effective weed management strategy. In this study, the utility of AISA Eagle airborne hyperspectral data (393–994 nm) with the new generation Worldview-2 multispectral sensor (427–908 nm) was compared to detect the abundance of bugweed cover within the Hodgsons Sappi forest plantation. Using sparse partial least squares discriminant analysis (SPLS-DA), the best detection results were obtained when performing discrimination using the remotely sensing images combined with LiDAR. Overall classification accuracies subsequently improved by 10% and 11.67% for AISA and Worldview-2 respectively, with improved detection accuracies for bugweed cover densities as low as 5%. The incorporation of LiDAR worked well within the SPLS-DA framework for detecting the abundance of bugweed cover using remotely sensed data. In addition, the algorithm performed simultaneous dimension reduction and variable selection successfully whereby wavelengths in the visible (393–670 nm) and red-edge regions (725–734 nm) of the spectrum were the most effective.