Characterizing the distribution, mechanism, and behaviour of invasive species is crucial to implementing an effective plan to protect and manage native grassland ecosystems. Hyperspectral remote sensing has been used to map and monitor invasive species at various spatial and temporal scales. However, most studies focus either on invasive tree species mapping or on the landscape level using coarse-spatial resolution imagery. These coarse-resolution images are not fine enough to distinguish individual invasive grasses, especially in a heterogeneous environment where invasive species are small, fragmented, and co-existent with native plants with similar color and texture. To capture the small yet highly dynamic invasive plants at different stages of the growing season and under various topography and hydrological conditions, we use airborne high-resolution narrow-band hyperspectral imagery (HrHSI) to map invasive species in a heterogeneous grassland ecosystem in southern Ontario, Canada. The results show that there is high spectral and textural separability between two invasive species and between invasive and native plants, leading to an overall species classification accuracy of up to 89.6%. The combination of resultant species-level maps and the digital elevation model (DEM) showed that seasonality is the dominant factor that drives the distribution of invasive species at the landscape level, while small-scale topographic variations partially explain local patches of invasive species. This study provides insights into the feasibility of using HrHSI in mapping invasive species in a heterogeneous ecosystem and offers the means to understand the mechanism and behaviour of invasive species for a more effective grassland management strategy.

表征入侵物种的分布、机制和行为对于实施保护和管理本地草原生态系统的有效计划至关重要。高光谱遥感已被用于绘制和监测各种时空尺度的入侵物种。然而，大多数研究要么侧重于入侵树种制图，要么侧重于使用粗空间分辨率图像的景观水平。这些粗分辨率图像不足以区分单个入侵草，尤其是在入侵物种较小、零散且与具有相似颜色和质地的本地植物共存的异质环境中。捕捉生长季节不同阶段、不同地形和水文条件下的小型但高度动态的入侵植物，我们使用机载高分辨率窄带高光谱图像 (HrHSI) 来绘制加拿大安大略省南部异质草原生态系统中入侵物种的地图。结果表明，两种入侵物种之间以及入侵植物与本地植物之间具有较高的光谱和纹理分离性，总体物种分类准确率高达89.6%。生成的物种级地图和数字高程模型 (DEM) 的结合表明，季节性是驱动景观级入侵物种分布的主要因素，而小尺度地形变化部分解释了局部入侵物种斑块。