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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak
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Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak

机译:评估高分辨率的无人机图像,以在模拟疾病暴发期间监测森林健康

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Research into remote sensing tools for monitoring physiological stress caused by biotic and abiotic factors is critical for maintaining healthy and highly-productive plantation forests. Significant research has focussed on assessing forest health using remotely sensed data from satellites and manned aircraft. Unmanned aerial vehicles (UAVs) may provide new tools for improved forest health monitoring by providing data with very high temporal and spatial resolutions. These platforms also pose unique challenges and methods for health assessments must be validated before use. In this research, we simulated a disease outbreak in mature Pinus radiata D. Don trees using targeted application of herbicide. The objective was to acquire a time-series simulated disease expression dataset to develop methods for monitoring physiological stress from a UAV platform. Time-series multi-spectral imagery was acquired using a UAV flown over a trial at regular intervals. Traditional field-based health assessments of crown health (density) and needle health (discolouration) were carried out simultaneously by experienced forest health experts. Our results showed that multi-spectral imagery collected from a UAV is useful for identifying physiological stress in mature plantation trees even during the early stages of tree stress. We found that physiological stress could be detected earliest in data from the red edge and near infra-red bands. In contrast to previous findings, red edge data did not offer earlier detection of physiological stress than the near infra-red data. A non-parametric approach was used to model physiological stress based on spectral indices and was found to provide good classification accuracy (weighted kappa = 0.694). This model can be used to map physiological stress based on high-resolution multi -spectral data. (C) 2017 Scion (New Zealand Forest Research Institute). Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
机译:对用于监测由生物和非生物因素引起的生理压力的遥感工具的研究对于维持健康和高产的人工林至关重要。大量的研究集中在利用卫星和有人驾驶飞机的遥感数据评估森林健康。无人机可以通过提供具有非常高的时间和空间分辨率的数据来提供新工具,以改善森林健康状况。这些平台也带来了独特的挑战,必须在使用前验证健康评估的方法。在这项研究中,我们使用除草剂的定向应用模拟了成熟的辐射松D. Don树的疾病暴发。目的是获得时间序列模拟的疾病表达数据集,以开发用于监测来自无人机平台的生理压力的方法。时间序列多光谱图像是使用定期在试验中飞行的无人机获取的。经验丰富的森林健康专家同时进行了传统的基于野外健康的树冠健康(密度)和针头健康(变色)健康评估。我们的结果表明,从无人机收集的多光谱图像即使在树种受力初期也可用于识别成熟的人工林中的生理压力。我们发现,从红色边缘和近红外波段的数据中最早可以检测到生理压力。与以前的发现相反,红边数据没有提供比近红外数据更早的生理压力检测。使用非参数方法基于光谱指数对生理压力进行建模,发现该方法可提供良好的分类准确性(加权kappa = 0.694)。该模型可用于基于高分辨率多光谱数据来映射生理压力。 (C)2017 Scion(新西兰森林研究所)。由Elsevier B.V.代表国际摄影测量与遥感学会(ISPRS)发布。

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