阿普利亚地区苛养木杆菌流行的多光谱传感器监测

Sara Dell’Anna, G. Mansueto, P. Boccardo, E. Arco
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引用次数: 0

摘要

2013年10月,意大利的普利亚地区意外地发现自己不得不面对一场瘟疫,在不到10年的时间里,这种瘟疫已经摧毁了数百万棵橄榄树,危及该地区40%的橄榄种植遗产。考虑到目前还没有发现确定的治疗办法,唯一的解决办法是在所谓的缓冲地带进行与不断监测有关的预防,并努力在非常局部的范围内界定这一现象。当时,遥感,特别是多光谱和高光谱传感器,似乎是从多维程度上描述问题并提出最适当方法来解决问题的关键。本研究利用安装在无人机系统上的多光谱传感器,对意大利布林迪西省4个橄榄园(Ogliarola品种)的不同症状状态进行了分析。在不同植被指数(IVs)中,GNDVI和BNDVI较好地识别了木杆菌感染的影响,实验室分析进一步证实了这一点。这一证实证实了所使用的方法的有效性,该方法旨在识别无症状树木的“异常”,并建立第一个早期预警系统,以便随后在实地进行更深入的调查。进一步的发展将研究一种分割算法的实现,根据这项工作中定义的IVs的阈值,并使用高光谱传感器,以识别树叶上的异常,归因于树木上潜在的木杆菌感染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-spectral sensors monitoring of the epidemic of Xylella Fastidiosa in the Apulia Region
In October 2013, the Apulia Region in Italy, unexpectedly, had to find itself to face the epidemic of Xylella Fastidiosa which in less than 10 years has decimated millions of olive trees, endangering the 40% of the regional olive-growing heritage. Taking into account the fact that there is not a definitive cure discovered yet, the only solution is the prevention linked to constant monitoring in the so-called buffer areas with diligent efforts to delimitate the phenomenon on a very local scale. Back then, remote sensing and, in particular, multispectral and hyperspectral sensors, seemed to be the key to depicting the problem to a multidimensional extent and coming up with the most adequate ways to tackle it. This research work focuses on the analysis of 4 olive groves (of the Ogliarola cultivar) in the province of Brindisi (Italy), with different symptomatic states, using a multispectral sensor mounted on a UAV system. Among a number of different vegetation indexes (IVs) calculated, the GNDVI and the BNDVI outperform to identify the effects of the Xylella Fastidiosa infection, which was further confirmed by the laboratory analyses. This confirmation has affirmed the validity of the approach used aiming at identifying “anomalies” on non-symptomatic trees and establishing a first early warning system for a subsequent more in-depth investigation in the field. Further developments will investigate the implementation of a segmentation algorithm, according to the threshold values of the IVs defined in this work, and the use of hyperspectral sensors, in order to identify anomalies on the foliage, attributable to a potential Xylella Fastidiosa infection on the trees.
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