Abstract Eucalyptus spp. and Acacia mearnsii are common exotic tree species in eastern Africa that have shown (strong) invasive behavior in some regions. Acacia mearnsii is considered a highly invasive species that is replacing native species and Eucalyptus spp. are known to consume high amounts of groundwater with suspected effects on native flora. Mapping the occurrence of these species in the Taita Hills, Kenya (part of the Eastern Arc Mountains Biodiversity Hotspot) is important as there is lack of knowledge on their occurrence and ecological impact in the area. Mapping methods that require a lot of fieldwork are impractical in areas like the Taita Hills, where the terrain is rugged and the infrastructure is poor. Our aim was hence to map the occurrence of these tree species in a 100 km2 area using airborne imaging spectroscopy and laser scanning. We used a one class biased support vector machine (BSVM) classifier as it needs labeled training data only for the positive classes (A. mearnsii and Eucalyptus spp.), which potentially reduces the amount of required fieldwork. We also introduce a new approach for parameterizing and setting the threshold level simultaneously for the BSVM classifier. The second aim was to link the occurrence of these species to selected environmental variables. The results showed that the BSVM classifier is suitable for mapping Acacia mearnsii and Eucalyptus spp., holding the potential to improve the efficiency of field data collection. The introduced parametrization/threshold selection method performed better than other commonly used approaches. The crown level F1-score was 0.76 for Eucalyptus spp. and 0.78 for A. mearnsii. We show that Eucalyptus spp. and A. mearnsii trees cover 0.8% and 1.6% of the study area, respectively. Both species are particularly located on steeper slopes and higher altitudes. Both species have significant occurrences in areas close to the biggest remaining native forest patch (Ngangao) in the study area. Nonetheless, follow-up studies are needed to evaluate their impact on the native flora and fauna, as well as their impact on the water resources. The maps created in this study in combination with such follow-up studies could serve as base data to generate guidelines that authorities can use to take action in handling the problems these species are causing.

中文翻译：

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使用一类分类的东弧山生物多样性热点地区入侵树种检测

摘要桉树属。Acacia mearnsii 和 Acacia mearnsii 是东非常见的外来树种，在某些地区表现出（强烈的）入侵行为。Acacia mearnsii 被认为是一种高度入侵物种，正在取代本地物种和桉树。已知消耗大量地下水，怀疑对本地植物群有影响。绘制这些物种在肯尼亚泰塔山（东部弧山生物多样性热点的一部分）的分布图很重要，因为缺乏关于它们在该地区的发生和生态影响的知识。需要大量实地工作的制图方法在像泰塔山这样地形崎岖且基础设施薄弱的地区是不切实际的。因此，我们的目标是使用机载成像光谱和激光扫描绘制 100 平方公里区域内这些树种的分布图。我们使用了一类偏向支持向量机 (BSVM) 分类器，因为它只需要正类（A. mearnsii 和 Eucalyptus spp.）的标记训练数据，这可能会减少所需的实地工作量。我们还介绍了一种新方法，用于同时为 BSVM 分类器参数化和设置阈值级别。第二个目标是将这些物种的出现与选定的环境变量联系起来。结果表明，BSVM 分类器适用于金合欢和桉树的映射，具有提高现场数据收集效率的潜力。引入的参数化/阈值选择方法比其他常用方法表现更好。桉树属的冠级 F1 分数为 0.76。A. mearnsii 为 0.78。我们表明桉树属。和 A. mearnsii 树分别占研究区域的 0.8% 和 1.6%。这两个物种都特别位于更陡峭的斜坡和更高的海拔。这两个物种在靠近研究区最大的剩余原生森林斑块 (Ngangao) 的地区都有大量出现。尽管如此，还需要进行后续研究来评估它们对本地动植物的影响，以及它们对水资源的影响。