Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Andrea Martínez-Movilla , Juan Luis Rodríguez-Somoza , Marta Román , Celia Olabarria , Joaquín Martínez-Sánchez
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引用次数: 0

Abstract

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.
利用大型无人飞行器快速诊断潮间带大型藻类的地理空间分布情况
大型藻类一直被用作沿海生态系统健康状况的指标,它们具有二氧化碳汇的功能,是初级生产的重要贡献者。随着人类活动的增加,评估这些活动对这些生态系统的影响至关重要。传统的勘测技术虽然精确,但耗时长,而且覆盖面积有限,因此需要新技术来监测潮间带大型藻类的覆盖范围和多样性。我们提出了一种使用 QGIS 免费资源半自动分类插件的方法,利用无人机和多光谱相机对潮间带大型藻类进行时空监测。我们还比较了六种分类器的性能:最小距离 (MD)、最大似然 (ML)、光谱角度映射 (SAM)、多层感知器 (MLP)、随机森林 (RF) 和支持向量机 (SVM),用于三种类型的大型藻类分类:一般、分类群组和物种。作为概念验证,对一个海洋保护区(西班牙西北部)的潮间带岩石海岸进行了为期四个月的研究。RF 和 SVM 取得了相似的结果,两者都被推荐用于总体(OASVM = 97.4±1.7 和 OARF = 98.3±1.7)和分类组(OASVM = 91.6±1.9 和 OARF = 89.2±4.5)。SVM 和 ML 更适合物种分类(OASVM = 77.4±11.4 和 OAML = 74.2±9.7)。由于大型藻类光谱特征的重叠,SAM 和 MLP 提供的物种分类器性能最差。该插件在调整 MLP 分类器的输入参数时显示出局限性,并且无法添加验证数据集。此外,我们还提出了一个开放式的 GIS 网络应用程序,即 Alganat 2000 GIS 网络,以促进沿海地区的监测和管理。我们的结论是,使用 SVM 或 ML 分类器的建议方法是评估潮间带大型藻类群落的有效工具。它的实施简单快捷,有利于不太熟悉编码和机器学习框架的研究人员,并减少了实地工作的时间和成本。作为未来的工作,我们建议将多光谱波段与地形和光谱指数相结合,并研究将深度学习模型应用于潮间带大型藻类的分类。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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