iForest - Biogeosciences and Forestry


Detecting tree water deficit by very low altitude remote sensing

Hilaire Martin (1)   , Sylvain Labbé (2), Patrick Baldet (1), Frédéric Archaux (1), Gwénaël Philippe (1)

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 215-219 (2017)
doi: https://doi.org/10.3832/ifor1690-009
Published: Feb 11, 2017 - Copyright © 2017 SISEF

Technical Reports

In a context of climate change and expected increasing drought frequency, it is important to select tree species adapted to water deficit. Experimentation in tree nurseries makes it possible to control for various factors such as water supply. We analyzed the spectral responses for two genetic varieties of Douglas fir sapling exposed to different levels of water deficit. Our results show that the mean NDVI derived from remote sensing at very low altitudes clearly differentiated stress levels while genetic varieties were partially distinguished.


Very Low Altitude Remote Sensing, Water Deficit, Variety, Douglas Fir

Authors’ address

Hilaire Martin
Patrick Baldet
Frédéric Archaux
Gwénaël Philippe
IRSTEA National Research Institute of Science and Technology for Environment and Agriculture, Domaine des Barres, F-45290 Nogent-sur-Vernisson (France)
Sylvain Labbé
IRSTEA, National Research Institute of Science and Technology for Environment and Agriculture, 361 rue J.F. Breton, BP 5095, F-34196 Montpellier Cedex 5 (France)

Corresponding author

Hilaire Martin


Martin H, Labbé S, Baldet P, Archaux F, Philippe G (2017). Detecting tree water deficit by very low altitude remote sensing. iForest 10: 215-219. - doi: 10.3832/ifor1690-009

Academic Editor

Giorgio Matteucci

Paper history

Received: Apr 27, 2015
Accepted: Nov 16, 2016

First online: Feb 11, 2017
Publication Date: Feb 28, 2017
Publication Time: 2.90 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 41389
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 35518
Abstract Page Views: 1887
PDF Downloads: 2812
Citation/Reference Downloads: 37
XML Downloads: 1135

Web Metrics
Days since publication: 2691
Overall contacts: 41389
Avg. contacts per week: 107.66

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

(No citations were found up to date. Please come back later)


Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

Becker M (1974)
Étude expérimentale de la transpiration et du développement de jeunes Douglas en fonction de l’alimentation en eau [Experimental study of sweating and development of young Douglas fir controled by water supply]. Annals of Forest Science 31: 997-109. [in French]
Online | Gscholar
Bréda N, Granier A, Aussenac G (2000)
Evolutions possibles des contraintes climatiques et conséquences pour la croissance des arbres [Possible changes in climatic constraints and consequences for tree growth]. Revue forestière Française (numéro spécial) 52: 73-90. [in French]
CrossRef | Gscholar
Bréda N, Huc R, Granier A, Dreyer E (2006)
Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Annals of Forest Science 63: 625-644.
CrossRef | Gscholar
Camarero JJ, Albuixech J, López-Lozano R, Casterad MC, Montserrat-Martí G (2010)
An increase in canopy cover leads to masting in Quercus ilex. Trees 24: 909-918.
CrossRef | Gscholar
Carleer A, Wolff E (2004)
Exploitation of very hign resolution satellite data for tree species identification. American Society for Photogrammetry and Remote Sensing 70 (1): 135-140.
CrossRef | Gscholar
Gitas IZ, San-Miguel-Ayanz J, Chuvieco E, Camia A (2014)
Advances in remote sensing and GIS applications in support of forest fire management. International Journal of Wildland Fire 23: 603-605.
CrossRef | Gscholar
Jensen T, Apan A, Young F, Zeller L (2007)
Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Computers and Electronics in Agriculture 59: 66-77.
CrossRef | Gscholar
Lebourgeois V, Bégué A, Labbé S, Mallavan B, Prévot L, Roux B (2008)
Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors 8: 7300-7322.
CrossRef | Gscholar
Lisein J, Bonnet S, Lejeune P, Pierrot-Deseiligny M (2014)
Modélisation de la canopée forestière par photogrammétrie depuis les images acquises par drone [Modeling forest canopy by photogrammetry from images acquired by drone]. Revue Française de Photogrammétrie et de Télédétection 206: 45-54. [in French]
Online | Gscholar
Meyer GE, Camargo Neto J (2008)
Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 63: 282-293.
CrossRef | Gscholar
Peng J, Dong W, Yuan W, Zhang Y (2012)
Responses of grassland and forest to temperature and precipitation changes in northeast China. Advances in Atmospheric sciences 29 (5): 1063-1077.
CrossRef | Gscholar
Pettorelli N, Laurance WF, O’Brien TG, Wegmann M, Nagendra H, Turner W (2014)
Satellite remote sensing for applied ecologist: opportunities and challenges. Journal of Applied Ecology 51: 839-848.
CrossRef | Gscholar
R Development Core Team (2014)
R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
Online | Gscholar
Rabatel G, Gorreta N, Labbé S (2014)
Getting simultaneous red and near infrared bands from a single digital camera for plant monitoring applications: Theoretical and practical study. Biosystems Engineering 117 (1): 2-14.
CrossRef | Gscholar
Rouse J, Haas RH, Schell JA, Deering DW (1973)
Monitoring vegetation systems in the great plains with ERTS. In: Proceeding of the “3rd ETRS Symposium”, NASA SP-351 1, US Government Printing Office, Washington, DC, USA, pp. 309-317.
Rozenberg P, Sergent A-S, Dalla-Salda G, Martinez-Meier A, Marin S, Ruiz-Diaz M, Bastien J-C, Sanchez L, Bréda N (2012)
Analyse rétrospective de l’adaptation à la sécheresse chez le douglas [Retrospective analysis of drought adaptation in Douglas fir]. Schweiz Z Forstwes 163 (3): 88-95. [in French]
CrossRef | Gscholar
Volcani A, Karnieli A, Svoray T (2005)
The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of semi-arid forest with respect to drought years. Forest Ecology and Management 215: 239-250.
CrossRef | Gscholar
Wang Q, Tenhunen J, Granier A (2005)
On the relationship of NDVI with leaf area index in a deciduous forest site. Remote sensing of Environment 94 (2): 244-255.
CrossRef | Gscholar

This website uses cookies to ensure you get the best experience on our website. More info