Abstract
Invasive species pose major global threats to ecosystems and agriculture. Serrated tussock (\textit{Nassella trichotoma}) is a highly competitive invasive grass species that disrupts native grasslands, reduces pasture productivity, and increases land management costs. In Victoria, Australia, it presents a major challenge due to its aggressive spread and ecological impact. While current ground surveys and subsequent management practices are effective at small scales, they are not feasible for landscape-scale monitoring. Although aerial imagery offers high spatial resolution suitable for detailed classification, its high cost limits scalability. Satellite-based remote sensing provides a more cost-effective and scalable alternative, though often with lower spatial resolution. This study evaluates whether multi-temporal Sentinel-2 imagery, despite its lower spatial resolution, can provide a comparable and cost-effective alternative for landscape-scale monitoring of serrated tussock by leveraging its higher spectral resolution and seasonal phenological information. A total of eleven models have been developed using various combinations of spectral bands, texture features, vegetation indices, and seasonal data. Using a random forest classifier, the best-performing Sentinel-2 model (M76*) has achieved an Overall Accuracy (OA) of 68\% and an Overall Kappa (OK) of 0.55, slightly outperforming the best-performing aerial imaging model's OA of 67\% and OK of 0.52 on the same dataset. These findings highlight the potential of multi-seasonal feature-enhanced satellite-based models for scalable invasive species classification.
Abstract (translated)
外来物种对生态系统和农业构成了重大全球威胁。锯齿草（*Nassella trichotoma*）是一种具有高度竞争力的入侵性草种，会破坏本地草地，降低牧场生产力，并增加土地管理成本。在澳大利亚维多利亚州，它由于其侵略性的传播速度及其生态影响而构成了一项重大挑战。尽管目前进行的地表调查及后续管理措施在小规模上有效，但它们不适用于大规模监测。虽然航空图像能够提供适合详细分类的高空间分辨率图像，但由于其高昂的成本限制了其实用性。基于卫星的遥感技术则提供了更具成本效益和可扩展性的替代方案，尽管其空间分辨率较低。本研究评估了多时间序列Sentinel-2影像是否可以通过利用更高的光谱分辨率和季节性物候信息，在锯齿草的大规模监测中提供一个相当且更经济的选择。 在该项研究中，共开发出了十一种模型，这些模型采用了多种光谱带、纹理特征、植被指数以及季节数据的不同组合。通过随机森林分类器，最佳表现的Sentinel-2模型（M76*）实现了总体准确性(OA)为68%，总体Kappa (OK)值为0.55，这稍微优于在同一数据集上进行的最佳性能航空成像模型的OA值67%和OK值0.52。这些发现突显了基于卫星增强多季节特征模型在大规模入侵物种分类中潜在的应用价值。
URL
https://arxiv.org/abs/2512.11267
