The classification of plant associations and their mapping play a key role in defining habitat biodiversity management, monitoring, and conservation strategies. In this work we present a methodological framework to map Mediterranean forest plant associations and habitats that relies on the application of the Functional Principal Component Analysis (FPCA) to the remotely sensed Normalized Difference Vegetation Index (NDVI) time series. FPCA, considering the chronological order of the data, reduced the NDVI time series data complexity and provided (as FPCA scores) the main seasonal NDVI phenological variations of the forests. We performed a supervised classification of the FPCA scores combined with topographic and lithological features of the study area to map the forest plant associations. The supervised mapping achieved an overall accuracy of 87.5%. The FPCA scores contributed to the global accuracy of the map much more than the topographic and lithological features. The results showed that (i) the main seasonal phenological variations (FPCA scores) are effective spatial predictors to obtain accurate plant associations and habitat maps; (ii) the FPCA is a suitable solution to simultaneously express the relationships between remotely sensed and ecological field data, since it allows us to integrate these two different perspectives about plant associations in a single graph. The proposed approach based on the FPCA is useful for forest habitat monitoring, as it can contribute to produce periodically detailed vegetation-based habitat maps that reflect the “current” status of vegetation and habitats, also supporting the study of plant associations.

中文翻译：

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使用Landsat 8 NDVI时间序列通过功能主成分分析来映射地中海森林植物协会和栖息地

植物协会的分类及其制图在定义栖息地生物多样性管理，监测和保护策略中起着关键作用。在这项工作中，我们提出了一种方法框架，用于绘制地中海森林植物协会和栖息地的地图，该框架依赖于将功能主成分分析（FPCA）应用于遥感归一化植被指数（NDVI）时间序列。考虑到数据的时间顺序，FPCA减少了NDVI时间序列数据的复杂性，并提供了（作为FPCA分数）森林的主要季节性NDVI物候变化。我们对FPCA评分进行了监督分类，并结合了研究区域的地形和岩性特征，以绘制森林植物协会的地图。监督映射的整体精度为87。5％。FPCA分数对地图的整体准确性的贡献远超过地形和岩性特征。结果表明：（i）主要的季节性物候变化（FPCA分数）是有效的空间预测指标，可获取准确的植物关联和生境图；（ii）FPCA是同时表达遥感数据和生态田间数据之间关系的一种合适解决方案，因为它使我们能够将关于植物关联的这两种不同观点整合到一张图中。基于FPCA的拟议方法可用于森林生境监测，因为它有助于定期生成详细的基于植被的生境图，以反映植被和生境的“当前”状态，也支持植物协会的研究。FPCA分数对地图的整体准确性的贡献远超过地形和岩性特征。结果表明：（i）主要的季节性物候变化（FPCA分数）是有效的空间预测指标，可获取准确的植物关联和生境图；（ii）FPCA是同时表达遥感数据和生态田间数据之间关系的一种合适解决方案，因为它使我们能够将关于植物关联的这两种不同观点整合到一张图中。基于FPCA的拟议方法可用于森林生境监测，因为它有助于定期生成详细的基于植被的生境图，以反映植被和生境的“当前”状态，也支持植物协会的研究。FPCA分数对地图的整体准确性的贡献远超过地形和岩性特征。结果表明：（i）主要的季节性物候变化（FPCA分数）是有效的空间预测指标，可获取准确的植物关联和生境图；（ii）FPCA是同时表达遥感数据和生态田间数据之间关系的一种合适解决方案，因为它使我们能够将关于植物关联的这两种不同观点整合到一张图中。基于FPCA的拟议方法可用于森林生境监测，因为它有助于定期生成详细的基于植被的生境图，以反映植被和生境的“当前”状态，也支持植物协会的研究。