Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem
Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem
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DOI：
10.3390/rs8040334
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发表时间：
2016-04-01
期刊：
影响因子：
5
通讯作者：
Thiele, Jan
中科院分区：
文献类型：
--
作者：
Grosse-Stoltenberg, Andre;Hellmann, Christine;Thiele, Jan
高光谱遥感是区分植物物种的有效工具，为生态评估提供了追踪植物入侵的巨大潜力。然而，对于许多高影响入侵者来说，使用遥感数据所需的基线信息缺失。此外，识别合适的分类算法和光谱区域以成功地对物种进行分类仍然是一个开放的研究领域。在这里，我们测试了入侵树长叶相思与邻近的外来和本地植被在Natura 2000受保护的地中海沙丘生态系统中的可分离性。我们利用连续的可见光、近红外和短波红外(VNIR-SWIR)数据以及树叶和冠层的植被指数进行分类，比较了5种不同的分类算法。我们能够根据植被指数成功地将长叶沙枣与周围的植被区分开来。在叶水平上，径向基函数核支持向量机和随机森林均获得了较高的灵敏度(支持向量机：0.83，随机森林：0.78)和较高的正预测值(0.86，0.83)。在冠层水平上，RF是敏感度(0.75)和PPV(0.75)的最佳平衡点。最相关的植被指数与叶绿素、水分、氮和纤维素等生化参数以及植被盖度相关，这与长叶木通的生物化学和生态生理特性是一致的。我们的结果突出了利用遥感作为工具来早期检测地中海沿海生态系统中的长叶木通的潜力。
Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms and spectral regions for successfully classifying species remains an open field of research. Here, we tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected Mediterranean dune ecosystem. We used continuous visible, near-infrared and short wave infrared (VNIR-SWIR) data as well as vegetation indices at the leaf and canopy level for classification, comparing five different classification algorithms. We were able to successfully distinguish A. longifolia from surrounding vegetation based on vegetation indices. At the leaf level, radial-basis function kernel Support Vector Machine (SVM) and Random Forest (RF) achieved both a high Sensitivity (SVM: 0.83, RF: 0.78) and a high Positive Predicted Value (PPV) (0.86, 0.83). At the canopy level, RF was the classifier with an optimal balance of Sensitivity (0.75) and PPV (0.75). The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia. Our results highlight the potential to use remote sensing as a tool for an early detection of A. longifolia in Mediterranean coastal ecosystems.