基于无人机的树皮甲虫侵扰云杉的早期检测在地方性种群和流行性种群中有所不同

Aurora Bozzini, Stefano Brugnaro, G. Morgante, Giacomo Santoiemma, Luca Deganutti, V. Finozzi, Andrea Battisti, Massimo Faccoli
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引用次数: 0

摘要

由于气候变化引起的压力因素,欧洲森林面临着越来越大的威胁,这为树皮甲虫的爆发创造了绝佳的条件。欧洲最重要的云杉林害虫是欧洲云杉树皮甲虫(Ips typographus L.)。要想有效控制 I. typographus 的爆发,就必须及时发现最近被侵袭的云杉树,而要发现被侵袭树冠上的症状非常困难。树皮甲虫的种群密度是影响侵染率和症状发展的众多因素之一。这项研究利用高分辨率无人飞行器(UAV)多光谱图像,比较了地方性和流行性树皮甲虫种群早期症状的出现情况。2022 年春季,在南阿尔卑斯山 10 个地点(5 个流行性地点和 5 个地方性地点)生长的云杉树群中,树皮甲虫诱导宿主定殖。从 2022 年 5 月到 8 月,无人机上的多光谱传感器每两周拍摄一次图像。通过对一系列植被指数的分析,可以观察到每个地点实际受侵染树木的反射特征和症状外观,并将其与未受侵染树木进行比较。在疫情发生地,至少在人眼观察到症状(红色阶段)前 1 个月就能发现受侵染的树木,而在地方性疫情发生地则无法做到这一点。主要的植被指数包括 NDVI(归一化差异植被指数)、SAVI(土壤调整植被指数,校正因子为 0.44)和 NDRE(归一化差异红边指数)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drone-based early detection of bark beetle infested spruce trees differs in endemic and epidemic populations
European forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is the European Spruce Bark Beetle (Ips typographus L.). Effective management of I. typographus outbreaks necessitates the timely detection of recently attacked spruce trees, which is challenging given the difficulty in spotting symptoms on infested tree crowns. Bark beetle population density is one of many factors that can affect infestation rate and symptoms development. This study compares the appearance of early symptoms in endemic and epidemic bark beetle populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery.In spring of 2022, host colonization by bark beetles was induced on groups of spruce trees growing in 10 sites in the Southern Alps, characterized by different population density (5 epidemic and 5 endemic). A multispectral sensor mounted on a drone captured images once every 2 weeks, from May to August 2022. The analyses of a set of vegetational indices allowed the actual infested trees’ reflectance features and symptoms appearance to be observed at each site, comparing them with those of unattacked trees.Results show that high bark beetles population density triggers a more rapid and intense response regarding the emergence of symptoms. Infested trees were detected at least 1 month before symptoms became evident to the human eye (red phase) in epidemic sites, while this was not possible in endemic sites. Key performing vegetation indices included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Vegetation Index, with a correction factor of 0.44), and NDRE (Normalized Difference Red Edge index).This early-detection approach could allow automatic diagnosis of bark beetles’ infestations and provide useful guidance for the management of areas suffering pest outbreaks.
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