Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests  

作　　者：Juan Guerra-Hernandez Ramon A.Diaz-Varela Juan Gabriel Avarez-Gonzalez Patricia Maria Rodriguez-Gonzalez 

机构地区：[1]edata,Centro de iniciativas empresariais,Fundacion CEL.O Palomar s/n,27004 Lugo,Spain [2]Forest Research Centre,School of Agriculture,University of Lisbon,Instituto Superior de Agronomia(ISA),Tapada da Ajuda,1349-017 Lisbon,Portugal [3]Biodiversity and Applied Botany Unit(GI BIOAPLIC 1809),Department of Botany,Escuela Politecnica Superior,R/Benigno Ledo,University of Santiago de Compostela,Campus Universitario,27002 Lugo,Spain [4]Sustainable Forest Management Unit(UXFS),Department of Agroforestry Engineering,Escuela Politecnica Superior,R/Benigno Ledo,University of Santiago de Compostela,Campus Universitario,E-27002 Lugo,Spain

出　　处：《Forest Ecosystems》2021年第4期810-830,共21页森林生态系统（英文版）

基　　金：co-funded by the European Commission LIFE program-Project LIFE FLUVIAL,LIFE16 NAT/ES/000771;supported by the Portuguese Foundation for Science and Technology(FCT)through FCT the Investigador FCT Programme(IF/00059/2015);through the CEEC Individual Programme(2020.03356.CEECIND);CEF was supported through the FCT UIDB/00239/2020;supported by the‘National Programme for the Promotion of Talent and Its Employability’of the Ministry of Economy,Industry,and Competitiveness(Torres-Quevedo program)through a postdoctoral grant(PTQ2018-010043).

摘　　要：Background:Black alder(Alnus glutinosa)forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex(class Oomycetes),“alder Phytopththora”.Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity.Data obtained by unmanned aerial vehicles(UAVs)may be particularly useful for such tasks due to the high resolution,flexibility of acquisition and cost efficiency of this type of data.In this study,A.glutinosa decline was assessed by considering four categories of tree health status in the field:asymptomatic,dead and defoliation above and below a 50% threshold.A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles-red green blue(RGB)data were analysed using classical random forest(RF)and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony.A total of 34 remote sensing variables were considered,including a set of vegetation indices,texture features from the normalized difference vegetation index(NDVI)and a digital surface model(DSM),topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level.Results:The four categories identified by the RF yielded an overall accuracy of 67%,while aggregation of the legend to three classes(asymptomatic,defoliated,dead)and to two classes(alive,dead)improved the overall accuracy to 72% and 91% respectively.On the other hand,the confusion matrix,computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%,80% and 94% for four-,three-and two-level classifications,respectively.Discussion:The study findings provide forest managers with an alternative robust classification method for the rapid,effective assessment of areas affected and non-affected by the disease,t

关 键 词：ALDER RPAS MULTI-SPECTRAL DEFOLIATION Texture variables 3D point cloud Tree health monitoring 

分 类 号：S718.5[农业科学—林学]