利用无人机和机器学习在地中海森林中鉴定亚历山达木物种

IF 4.4 2区 地球科学 Q1 REMOTE SENSING Drones Pub Date : 2023-11-09 DOI:10.3390/drones7110668
Antonio M. Cabrera-Ariza, Miguel Peralta-Aguilera, Paula V. Henríquez-Hernández, Rómulo Santelices-Moya
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引用次数: 0

摘要

本研究探讨了在智利地中海森林中使用无人机(uav)和机器学习算法来识别Nothofagus alessandrii (ruil)物种。这种物种的濒危性质,加上栖息地的丧失和环境的压力,需要有效的监测和保护工作。配备高分辨率传感器的无人机可以捕获正射影像,从而使用监督机器学习技术开发分类模型。三种分类算法-随机森林(RF),支持向量机(SVM)和最大似然(ML) -被评估,在像素和基于对象的水平,跨越三个研究领域。结果表明,RF始终表现出较强的分类性能,其次是SVM和ML。算法和训练方法的选择显著影响结果,突出了根据项目需求进行定制选择的重要性。这些发现有助于提高遥感应用中的物种识别精度,支持生物多样性保护和生态研究工作。
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Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests
This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for the identification of Nothofagus alessandrii (ruil) species in the Mediterranean forests of Chile. The endangered nature of this species, coupled with habitat loss and environmental stressors, necessitates efficient monitoring and conservation efforts. UAVs equipped with high-resolution sensors capture orthophotos, enabling the development of classification models using supervised machine learning techniques. Three classification algorithms—Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood (ML)—are evaluated, both at the Pixel- and Object-Based levels, across three study areas. The results reveal that RF consistently demonstrates strong classification performance, followed by SVM and ML. The choice of algorithm and training approach significantly impacts the outcomes, highlighting the importance of tailored selection based on project requirements. These findings contribute to enhancing species identification accuracy in remote sensing applications, supporting biodiversity conservation and ecological research efforts.
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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