Abstract. Acacia longifolia, like any other invasive species, poses a threat to the natural ecosystems in South Africa and beyond. In South Africa, the species is reported to cause significant loss to vegetation biodiversity and is associated with high water use. It remains a challenge to understand the extent of their invasion in complex landscapes, where the use of ground-based surveys is difficult. Considering the heterogeneity and complexity of hydrological and ecological processes at catchment scale, the implementation of remote sensing to detect the distribution of these species becomes critical for continuous monitoring of their extent and associated impacts. We, therefore, aim at species-specific characterization of the spatial distribution of Acacia longifolia invasive wood species at catchment scale within the Cape Floristic region located in the Western Cape Province, using Sentinel 2 remotely sensed data. Overall, the distribution of A. Longifolia was found to be patchy and heterogeneous, across the catchment. However, the main hot spots were detected within the fynbos vegetation, low-lying areas, as well as along the southern coastal belt. Although the catchment is predominantly under cultivation, A. longifolia was found to occupy almost 10% of its area. Classification using combined variables, using both vegetation indices and bands produced the highest overall accuracy of 88.66%, than when bands (66.19%) and indices (76.05%) were used as independent dataset. For example, the producer’s classification accuracy of A. longifolia increased by almost 15%, whereas the user’s increased by almost 10% from spectral bands accuracies. The classification of A. longifolia was primarily attributed to near-infrared, short wave-infrared (centered at 2190 nm), red edge (705 and 740 nm), as well as, chlorophyll vegetation index, the leaf water content index, and normalized difference vegetation index, derived using the near-infrared and red-edge band (at 740 nm) variables. The findings of this study underscore the relevance of satellite-based remote sensing in monitoring invasions in natural ecosystems and can be used as baseline information for invasive-species clearing endeavors in the area.

中文翻译：

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南非西开普省 Cape Floristic 地区长叶金合欢入侵植物的遥感表征

摘要。与任何其他入侵物种一样，长叶金合欢对南非及其他地区的自然生态系统构成威胁。在南非，据报道该物种对植被生物多样性造成重大损失，并与高用水量有关。了解它们在复杂景观中的入侵程度仍然是一个挑战，在这些景观中难以使用地面调查。考虑到流域尺度水文和生态过程的异质性和复杂性，实施遥感来检测这些物种的分布对于持续监测其范围和相关影响变得至关重要。因此，我们 目的是使用 Sentinel 2 遥感数据，在位于西开普省开普省植物区的集水区尺度上对长叶金合欢入侵木材物种的空间分布进行物种特异性表征。总体而言，发现 A. Longifolia 的分布在整个集水区是不完整和异质的。然而，主要热点是在 fynbos 植被、低洼地区以及南部沿海地带发现的。尽管该流域主要是在种植中，但发现 A. longifolia 几乎占据了其面积的 10%。使用组合变量进行分类，同时使用植被指数和波段，其总体准确度最高，为 88.66%，高于使用波段 (66.19%) 和指数 (76.05%) 作为独立数据集的情况。例如，生产者对 A. longifolia 的分类精度提高了近 15%，而用户的光谱带精度提高了近 10%。A. longifolia 的分类主要归因于近红外、短波红外（以 2190 nm 为中心）、红边（705 和 740 nm）以及叶绿素植被指数、叶片含水量指数和归一化差异植被指数，使用近红外和红边带（740 nm）变量得出。这项研究的结果强调了卫星遥感在监测自然生态系统入侵方面的相关性，并可用作该地区入侵物种清除工作的基线信息。A. longifolia 的分类主要归因于近红外、短波红外（以 2190 nm 为中心）、红边（705 和 740 nm）以及叶绿素植被指数、叶片含水量指数和归一化差异植被指数，使用近红外和红边带（740 nm）变量得出。这项研究的结果强调了卫星遥感在监测自然生态系统入侵方面的相关性，并可用作该地区入侵物种清除工作的基线信息。A. longifolia 的分类主要归因于近红外、短波红外（以 2190 nm 为中心）、红边（705 和 740 nm）以及叶绿素植被指数、叶片含水量指数和归一化差异植被指数，使用近红外和红边带（740 nm）变量得出。这项研究的结果强调了卫星遥感在监测自然生态系统入侵方面的相关性，并可用作该地区入侵物种清除工作的基线信息。使用近红外和红边带（740 nm）变量得出。这项研究的结果强调了卫星遥感在监测自然生态系统入侵方面的相关性，并可用作该地区入侵物种清除工作的基线信息。使用近红外和红边带（740 nm）变量得出。这项研究的结果强调了卫星遥感在监测自然生态系统入侵方面的相关性，并可用作该地区入侵物种清除工作的基线信息。