Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

IF 4.8 2区 环境科学与生态学 Q1 OCEANOGRAPHY
Elena Cini , Flavio Marzialetti , Marco Paterni , Andrea Berton , Alicia Teresa Rosario Acosta , Daniela Ciccarelli
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引用次数: 0

Abstract

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.

UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).

The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

通过基于地理对象的图像分析(GEOBIA)整合无人机图像和机器学习,以加强对地中海沿海沙丘上的虞美人(Yucca gloriosa)的监测
有效监测和早期发现外来入侵植物物种(IAPs)对于减少其蔓延和保护本地栖息地至关重要。无人驾驶飞行器(UAV)提供了一种具有成本效益的解决方案,可提供高分辨率图像。在这项研究中,我们旨在开发一种半自动化方法,利用机器学习算法、空间度量和聚类技术对无人机图像进行监测、绘制地图并提出管理措施建议,以抵御在意大利中部沿海固定沙丘上定植的入侵植物 Yucca gloriosa。此外,还利用了衍生植被指数。为绘制矢车菊分布图,对正射影像进行了基于地理对象的图像分析(GEOBIA),然后在训练阶段采用随机森林算法,考虑了三种变量组合(DSM + 植被指数、DSM + 光谱波段、DSM + 混合变量)。结果表明,最准确的预测图是基于 DSM + 混合变量数据集的,这表明结合使用光谱带和植被指数具有重要作用。在所有三种情况下,DSM 都是区分 Y. gloriosa 和周围环境的关键变量。此外,我们的研究结果还证明了结合植被指数将目标外来入侵植物(IAP)与周围环境区分开来的优势,特别是考虑到其独特的光合作用过程和生物量生产。从管理角度来看,我们的试点研究表明,基于无人机的绘图方法是实地工作与成本之间的最佳平衡。这种方法可以在不影响方法准确性的前提下,精确确定光叶女贞的控制区和清除区。生成的预测图还对沿海沙丘生态系统的保护具有潜在意义,通过提出对 Y. gloriosa 的管理措施,为有效管理入侵物种和保护生物多样性提供了一个很有前途的工具。
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来源期刊
Ocean & Coastal Management
Ocean & Coastal Management 环境科学-海洋学
CiteScore
8.50
自引率
15.20%
发文量
321
审稿时长
60 days
期刊介绍: Ocean & Coastal Management is the leading international journal dedicated to the study of all aspects of ocean and coastal management from the global to local levels. We publish rigorously peer-reviewed manuscripts from all disciplines, and inter-/trans-disciplinary and co-designed research, but all submissions must make clear the relevance to management and/or governance issues relevant to the sustainable development and conservation of oceans and coasts. Comparative studies (from sub-national to trans-national cases, and other management / policy arenas) are encouraged, as are studies that critically assess current management practices and governance approaches. Submissions involving robust analysis, development of theory, and improvement of management practice are especially welcome.
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