气候变化领域集成服务门户(CCPortal): Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

CCPortal

DOI	10.1016/j.rse.2021.112420
	Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits
	Camino C.; Calderón R.; Parnell S.; Dierkes H.; Chemin Y.; Román-Écija M.; Montes-Borrego M.; Landa B.B.; Navas-Cortes J.A.; Zarco-Tejada P.J.; Beck P.S.A.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution. © 2021 The Authors
英文关键词	Epidemic spread model; Hyperspectral; Machine learning; Nitrogen; Radiative transfer model; SWIR domain; Thermal; Xylella fastidiosa
语种	英语
scopus关键词	Bacteria; Chlorophyll; Epidemiology; Forestry; Infrared devices; Infrared radiation; Nitrogen; Polymerase chain reaction; Probability distributions; Proteins; Remote sensing; Support vector machines; Viruses; Epidemic spread model; HyperSpectral; Leaf proteins; Machine-learning; Plant traits; Radiative transfer modelling; Remote-sensing; SWIR domain; Thermal; Xylella fastidiosa; Radiative transfer; Indicator indicator; Prunus dulcis; Xylella fastidiosa
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178827
作者单位	European Commission (EC), Joint Research Centre (JRC), Ispra, Italy; School of Environment and Life Sciences, University of Salford, Manchester, United Kingdom; Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain; School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences (FVAS), Department of Infrastructure Engineering, Faculty of Engineering and Information Technology (FEIT), University of Melbourne, Melbourne, Victoria, Australia
推荐引用方式 GB/T 7714	Camino C.,Calderón R.,Parnell S.,et al. Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits[J],2021,260.
APA	Camino C..,Calderón R..,Parnell S..,Dierkes H..,Chemin Y..,...&Beck P.S.A..(2021).Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits.Remote Sensing of Environment,260.
MLA	Camino C.,et al."Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits".Remote Sensing of Environment 260(2021).