Next Article in Journal
Statistical Observation of Thunderstorm-Induced Ionospheric Gravity Waves above Low-Latitude Areas in the Northern Hemisphere
Next Article in Special Issue
Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification
Previous Article in Journal
Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations
Previous Article in Special Issue
Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces
Article

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

1
Environmental Pollutions, Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Malayer 65719-95863, Iran
2
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain
3
Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University & Water Science Engineering Department, Bu-Ali Sina University, Hamedan 65178, Iran
4
Faculty of Natural Resource and Earth Science, Shahrekord University, Shahrekord 8815648456, Iran
5
Department of Geography, Ghent University, 9000 Ghent, Belgium
6
Research Group Climate Change and Security, Institute of Geography, University of Hamburg, 20146 Hamburg, Germany
7
ISUMADECIP, Faculty of Environmental Science and Engineering, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2731; https://doi.org/10.3390/rs11232731
Received: 22 October 2019 / Revised: 13 November 2019 / Accepted: 16 November 2019 / Published: 20 November 2019
(This article belongs to the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation)
Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage. View Full-Text
Keywords: field spectroscopy; hyperspectral; heavy metals; grapevine; PLS; SVM; MLR field spectroscopy; hyperspectral; heavy metals; grapevine; PLS; SVM; MLR
Show Figures

Graphical abstract

MDPI and ACS Style

Mirzaei, M.; Verrelst, J.; Marofi, S.; Abbasi, M.; Azadi, H. Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis. Remote Sens. 2019, 11, 2731. https://doi.org/10.3390/rs11232731

AMA Style

Mirzaei M, Verrelst J, Marofi S, Abbasi M, Azadi H. Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis. Remote Sensing. 2019; 11(23):2731. https://doi.org/10.3390/rs11232731

Chicago/Turabian Style

Mirzaei, Mohsen, Jochem Verrelst, Safar Marofi, Mozhgan Abbasi, and Hossein Azadi. 2019. "Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis" Remote Sensing 11, no. 23: 2731. https://doi.org/10.3390/rs11232731

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop