Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis

被引:76
作者
Poblete, T. [1 ]
Camino, C. [2 ]
Beck, P. S. A. [2 ]
Hornero, A. [3 ]
Kattenborn, T. [4 ]
Saponari, M. [5 ]
Boscia, D. [5 ]
Navas-Cortes, J. A. [6 ]
Zarco-Tejada, P. J. [1 ,6 ,7 ]
机构
[1] Univ Melbourne, FVAS, Sch Agr & Food, Melbourne, Vic, Australia
[2] EC, Directorate D Sustainable Resources, JRC, Via E Fermi 2749 TP 261,26a-043, I-21027 Ispra, VA, Italy
[3] Swansea Univ, Dept Geog, Swansea SA2 8PP, W Glam, Wales
[4] KIT, Inst Geog & Geoecol IFGG, Karlsruhe, Germany
[5] CNR, IPSP, Via Amendola 122-D, I-70126 Bari, Italy
[6] CSIC, IAS, Ave Menendez Pidal S-N, Cordoba 14004, Spain
[7] Univ Melbourne, MSE, Dept Infrastruct Engn, Melbourne, Vic, Australia
关键词
Hyperspectral; Multispectral; Thermal; Radiative transfer; Xylella fastidiosa; Airborne; Machine learning; WATER-STRESS; CHLOROPHYLL CONTENT; VERTICILLIUM WILT; MODEL; FLUORESCENCE; REFLECTANCE; TEMPERATURE; INVERSION; INDEXES; RETRIEVAL;
D O I
10.1016/j.isprsjprs.2020.02.010
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with largescale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar-induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from similar to 80% (kappa, kappa = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to similar to 74% (kappa = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing x to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive.
引用
收藏
页码:27 / 40
页数:14
相关论文
共 55 条
[1]   Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows [J].
Aasen, Helge ;
Honkavaara, Eija ;
Lucieer, Arko ;
Zarco-Tejada, Pablo J. .
REMOTE SENSING, 2018, 10 (07)
[2]   Can Apulia's olive trees be saved? [J].
Almeida, Rodrigo P. P. .
SCIENCE, 2016, 353 (6297) :346-348
[3]  
[Anonymous], 2021, REPORT FSEC PF 270 9
[4]   A REAPPRAISAL OF THE USE OF DMSO FOR THE EXTRACTION AND DETERMINATION OF CHLOROPHYLLS-A AND CHLOROPHYLLS-B IN LICHENS AND HIGHER-PLANTS [J].
BARNES, JD ;
BALAGUER, L ;
MANRIQUE, E ;
ELVIRA, S ;
DAVISON, AW .
ENVIRONMENTAL AND EXPERIMENTAL BOTANY, 1992, 32 (02) :85-100
[5]   Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle [J].
Berni, Jose A. J. ;
Zarco-Tejada, Pablo J. ;
Suarez, Lola ;
Fereres, Elias .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (03) :722-738
[6]   Hyperspectral remote sensing of plant pigments [J].
Blackburn, George Alan .
JOURNAL OF EXPERIMENTAL BOTANY, 2007, 58 (04) :855-867
[7]  
BRANCATO A, 2018, EFSA J, V16
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   Simplifying decision trees: A survey [J].
Breslow, LA ;
Aha, DW .
KNOWLEDGE ENGINEERING REVIEW, 1997, 12 (01) :1-40
[10]   High-resolution airborne hyperspectral and thermal imagery for early, detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices [J].
Calderon, R. ;
Navas-Cortes, J. A. ;
Lucena, C. ;
Zarco-Tejada, P. J. .
REMOTE SENSING OF ENVIRONMENT, 2013, 139 :231-245