Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China
Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China
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DOI：
10.3390/rs12244010
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Xiang Liu;Huiyu Liu;Pawan Datta;Julian Frey;B. Koch
中科院分区：
文献类型：
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作者：
Xiang Liu;Huiyu Liu;Pawan Datta;Julian Frey;B. Koch
互花米草（S. alterniflora）是中国滨海湿地最严重的入侵植物之一。准确和可重复的互花草入侵图谱对于制定具有成本效益的保护本地生物多样性的管理策略至关重要。传统的遥感成图方法需要大量的野外采样工作。此外，由于互花蓟马与其共同优势的本地植物之间的光谱可分离性较差，我们检测该入侵物种的能力仍然有限。为此，我们提出了一种结合物候归一化植被指数（NDVI）时间序列分析（TSA）的集成一类分类器（EOCC）检测互花葡萄球菌的新方案。在盐城国家自然保护区（YNNR）核心区对EOCC算法进行了单场景分析（SSA）和NDVI-TSA两种场景的性能评价。同时，在两种场景下测试了全监督分类器支持向量机（SVM）进行比较。在此基础上，探讨了互花草物候特征的关键阶段和物候特征NDVI-TSA在互花草识别中的优势。结果表明，仅使用正训练数据的EOCC与在YNNR中使用完整训练数据训练的SVM表现相似。此外，EOCC算法在无需二次训练的情况下转移到第二站点时，具有更强的鲁棒可转移性，分类精度明显高于SVM。与物候NDVI-TSA联合使用时，EOCC算法的敏感性-特异性结果更为平衡，可转移性略好于物候最佳期（即11月衰老期）。结果表明，该检测方案具有较高的跨区域互花草定位潜力，EOCC算法可以替代传统的监督分类方法，成为入侵植物检测的一种可行方案。其中，YNNR的总体准确率（OA）、Kappa和真技能统计量（TSS）分别为92.92%、0.843和0.834，而向非训练点转移的OA、Kappa和TSS分别为90.94%、0.815和0.825。
Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.