Suppr超能文献

评估高分辨率商业卫星图像用于纽约州葡萄霜霉病检测和监测的能力。

Assessing the Capacity of High-Resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York State.

作者信息

Kanaley Kathleen, Combs David B, Paul Angela, Jiang Yu, Bates Terry, Gold Kaitlin M

机构信息

Cornell University, Cornell AgriTech, Geneva, NY 14456, U.S.A.

Cornell Lake Erie Research and Extension Laboratory, Portland, NY 14769, U.S.A.

出版信息

Phytopathology. 2024 Dec;114(12):2536-2545. doi: 10.1094/PHYTO-11-23-0432-R. Epub 2024 Dec 19.

Abstract

Grapevine downy mildew (GDM), caused by the oomycete , can cause 100% yield loss and vine death under conducive conditions. High-resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases such as GDM over large, heterogeneous fields. Here, we investigated the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, New York, from June to August 2020, 2021, and 2022. Satellite imagery acquired within 72 h of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July to early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps toward incorporating satellite remote sensing in grapevine disease surveillance systems. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

摘要

葡萄霜霉病(GDM)由卵菌引起,在适宜条件下可导致100%的产量损失和葡萄藤死亡。高分辨率多光谱卫星平台为在大面积、异质田地中追踪如GDM等快速传播的病害提供了机会。在此,我们研究了PlanetScope(3米)和SkySat(50厘米)影像在整个生长季检测和监测GDM的能力。2020年、2021年和2022年6月至8月,一组训练有素的巡查员对纽约州日内瓦一个研究葡萄园的GDM严重程度和发病率进行了评级。对巡查后72小时内获取的卫星影像进行处理,以提取单波段反射率和植被指数(VIs)。基于两个影像数据集的光谱波段和VIs训练的随机森林模型能够对GDM发病率和严重程度高的区域与低的区域进行分类,最大准确率分别为0.85(SkySat)和0.92(PlanetScope)。然而,直到7月下旬至8月初,我们才观察到高损害等级和低损害等级的植被指数之间存在显著差异。我们确定云层覆盖、影像配准和低光谱分辨率是基于卫星的GDM监测业务化的关键挑战。这项工作确立了星载多光谱传感器检测晚期GDM的能力,并概述了将卫星遥感纳入葡萄病害监测系统的步骤。[公式:见正文] 版权所有© 2024作者。本文是一篇根据知识共享署名 - 非商业性使用 - 禁止演绎4.0国际许可协议分发的开放获取文章。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验