Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

被引：44

作者：

Avola, Giovanni ^{[1
]}

Di Gennaro, Salvatore Filippo ^{[2
]}

Cantini, Claudio ^{[1
]}

Riggi, Ezio ^{[1
]}

Muratore, Francesco ^{[1
]}

Tornambe, Calogero ^{[1
]}

Matese, Alessandro ^{[2
]}

机构：

[1] Natl Res Council CNR, Trees & Timber Inst IVALSA, Via P Gaifami 18, I-95126 Catania, Italy

[2] Natl Res Council CNR, Inst Biometeorol IBIMET, Via Caproni 8, I-50145 Florence, Italy

来源：

REMOTE SENSING | 2019年 / 11卷 / 10期

关键词：

Olea europaea L; canopy; precision agriculture; unmanned aerial vehicle (UAV); vegetation indices (VIs); cultivar recognition; LEAF-AREA INDEX; UAV; REFLECTANCE; IMAGERY; WHEAT;

D O I：

10.3390/rs11101242

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green(520-600), red(630-690) and near-infrared(760-900) bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis-PCA and linear discriminant analysis-LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases.

引用

页数：9

共 41 条

[11] Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes [J].

Gutierrez, Mario ;

Reynolds, Matthew P. ;

Klatt, Arthur R. .

JOURNAL OF EXPERIMENTAL BOTANY, 2010, 61 (12) :3291-3303

[12] Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture [J].

Haboudane, D ;

Miller, JR ;

Pattey, E ;

Zarco-Tejada, PJ ;

Strachan, IB .

REMOTE SENSING OF ENVIRONMENT, 2004, 90 (03) :337-352

[13] Development of a two-band enhanced vegetation index without a blue band [J].

Jiang, Zhangyan ;

Huete, Alfredo R. ;

Didan, Karnel ;

Miura, Tomoaki .

REMOTE SENSING OF ENVIRONMENT, 2008, 112 (10) :3833-3845

[14] Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling [J].

Jimenez-Brenes, F. M. ;

Lopez-Granados, F. ;

de Castro, A. I. ;

Torres-Sanchez, J. ;

Serrano, N. ;

Pena, J. M. .

PLANT METHODS, 2017, 13

[15]

Johnson R. M., 2008, Journal - American Society of Sugar Cane Technologists, V28, P63

[16] DERIVATION OF LEAF-AREA INDEX FROM QUALITY OF LIGHT ON FOREST FLOOR [J].

JORDAN, CF .

ECOLOGY, 1969, 50 (04) :663-&

[17] Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing [J].

Kelcey, Joshua ;

Lucieer, Arko .

REMOTE SENSING, 2012, 4 (05) :1462-1493

[18] Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors [J].

Kuska, M. T. ;

Mahlein, A. -K. .

EUROPEAN JOURNAL OF PLANT PATHOLOGY, 2018, 152 (04) :987-992

[19] Assessment of Vegetation Indices Derived by UAV Imagery for Durum Wheat Phenotyping under a Water Limited and Heat Stressed Mediterranean Environment [J].

Kyratzis, Angelos C. ;

Skarlatos, Dimitrios P. ;

Menexes, George C. ;

Vamvakousis, Vasileios F. ;

Katsiotis, Andreas .

FRONTIERS IN PLANT SCIENCE, 2017, 8

[20] Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy [J].

Li, Xiaoli ;

He, Yong ;

Fang, Hui .

JOURNAL OF FOOD ENGINEERING, 2007, 81 (02) :357-363

← 1 2 3 4 5 →