*
 

iForest - Biogeosciences and Forestry

*

Spectral reflectance properties of healthy and stressed coniferous trees

G Masaitis (1)   , G Mozgeris (1), A Augustaitis (2)

iForest - Biogeosciences and Forestry, Volume 6, Issue 1, Pages 30-36 (2013)
doi: https://doi.org/10.3832/ifor0709-006
Published: Jan 14, 2013 - Copyright © 2013 SISEF

Research Articles

Collection/Special Issue: IUFRO 7.01.00 - COST Action FP0903, Kaunas (Lithuania - 2012)
Biological Reactions of Forest to Climate Change and Air Pollution
Guest Editors: Elena Paoletti, Andrzej Bytnerowicz, Algirdas Augustaitis


This study investigates the properties of hyperspectral reflectance of healthy and stressed coniferous trees. Two coniferous tree species which naturally grow in Lithuania, Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.), as well as an introduced species, Siberian pine (Pinus sibirica Du Tour), were selected for the study. Hyperspectral reflectance data were collected under laboratory conditions by scanning the needles of healthy (no foliar loss) and stressed Norway spruce (foliar loss 66-70%), Scots pine (foliar loss 71-75%) and Siberian pine (foliar loss 86-90%) trees using a Themis Vision Systems VNIR 400H hyperspectral imaging camera. The spectrometer of the camera covers the spectral range of 400-1000 nm with the sampling interval of 0.6 nm. Simultaneously, the chlorophyll a and b content in the needles was determined by spectrophotometrically measuring the needles’ absorbance of ethanol extracts. The statistical analyses included principal component analysis, analysis of variance and partial least squares regression techniques. Relatively large spectral differences between healthy and stressed trees were detected for Norway spruce needles: 884 out of 955 wavebands indicated a statistically different reflectance (p<0.05). The reflectance associated with the stress level was statistically different (p<0.05) in 767 and 698 out of 955 wavebands for Scots pine and Siberian pine, respectively. The most informative wavelengths for spectral separation between the needles taken from healthy and stressed trees were found in the following spectral ranges: 701.0-715.7 nm for Norway spruce, 706.1-718.2 nm for Scots pine, and 862.3-893.1 nm for Siberian pine. The relationship between the spectral reflectance properties of the needles and their chlorophyll content was also determined for each species. Waveband ranges (as well as single bands) most sensitive to changes in chlorophyll content were: 709.9-722.1 nm (715.6 nm) for Norway spruce; 709.3-721.4 nm (715.0 nm) for Scots pine; 710.6-722.7 nm (720.1 nm) for Siberian pine. In general, the study revealed that narrow-band based hyperspectral imaging has the potential for accurately detecting stress in coniferous trees.

  Keywords


Conifers, Imaging Spectrometry, Hyperspectral Reflectance, Tree Stress, Waveband Selection

Authors’ address

(1)
G Masaitis
G Mozgeris
Institute of Forest Management and Wood Science, Aleksandras Stulginskis University, Studentu str. 11, LT-53361 Akademija, Kaunas distr. (Lithuania)
(2)
A Augustaitis
Forest Monitoring Laboratory, Institute of Forest Management and Wood Science, Aleksandras Stulginskis University, Studentu str. 11, LT-53361 Akademija, Kaunas distr. (Lithuania)

Corresponding author

Citation

Masaitis G, Mozgeris G, Augustaitis A (2013). Spectral reflectance properties of healthy and stressed coniferous trees. iForest 6: 30-36. - doi: 10.3832/ifor0709-006

Academic Editor

Elena Paoletti

Paper history

Received: Jul 30, 2012
Accepted: Dec 19, 2012

First online: Jan 14, 2013
Publication Date: Feb 05, 2013
Publication Time: 0.87 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 49222
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 41223
Abstract Page Views: 2022
PDF Downloads: 4676
Citation/Reference Downloads: 38
XML Downloads: 1263

Web Metrics
Days since publication: 4092
Overall contacts: 49222
Avg. contacts per week: 84.20

Article Citations

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

Total number of cites (since 2013): 29
Average cites per year: 2.64

 

Publication Metrics

by Dimensions ©

Articles citing this article

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

 
(1)
Atzberger C, Werner W (1998)
Needle reflectance of healthy and diseased Spruce stands. In: “Material of 1 EARSeL workshop on imaging spectroscopy”. Remote Sensing Laboratories, University of Zurich, Switzerland, pp. 271-283.
Gscholar
(2)
Augustaitis A, Mozgeris G, Eigirdas M, Sajonas M (2009)
Color infrared aerial images to evaluate tree crown defoliation. In: Proceedings of the “4 International Scientific Conference on Rural Development”. Akademija, Kaunas distr. (Lithuania), 15-17 October, 2009. Lithuanian University of Agriculture, vol. 4, book 2. pp. 213-216.
Gscholar
(3)
Bikuviene I, Mozgeris G (2010)
Testing the simultaneous use of laser scanning and aerial image data for estimation of tree crown density. In: Proceedings of the “16 Annual International Conference Research for Rural Development”. Jelgava (Latvia), 19-21 May, 2010. Latvia University of Agriculture, vol. 1, pp. 201-207.
Gscholar
(4)
Carrascal LM, Galvan I, Gordo O (2009)
Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118: 681-690.
CrossRef | Gscholar
(5)
Carter GA, Knapp AK (2001)
Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88: 677-684.
CrossRef | Gscholar
(6)
Carter GA (1993)
Responses of leaf spectral reflectance to plant stress. American Journal of Botany 80: 239-43.
CrossRef | Gscholar
(7)
Carter GA, Dell TR, Cibula WG (1996)
Spectral reflectance characteristics and digital imagery of a pine needle blight in the southeastern United States. Canadian Journal of Forest Research 26: 402-407.
CrossRef | Gscholar
(8)
Cho MA, Debba P, Mutanga O, Dudeni-Tlhone N, Magadla T, Khuluse SA (2012)
Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health. International Journal of Applied Earth Observation and Geoinformation 16: 85-93.
CrossRef | Gscholar
(9)
Ciesla WM (2000)
Remote sensing in forest health protection. FHTET Report No. 00-03, Forest Health Technology Enterprise Team, Remote Sensing Applications Center, USDA Forest Service, Salt Lake City, Utah, USA.
Gscholar
(10)
Datt B (1998)
Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in leaves. Remote Sensing of Environment 66: 111-121.
CrossRef | Gscholar
(11)
Eichhorn J, Roskams P, Ferretti M, Mues V, Szepesi A, Durrant D (2010)
Visual assessment of crown condition and damaging agents. Manual Part IV. In: “Manual on Methods and Criteria for Harmonized Sampling, Assessment, Monitoring and Analysis of the Effects of Air Pollution on Forests”. UNECE ICP Forests Programme Co-ordinating Centre, Hamburg, Germany, pp. 49.
Gscholar
(12)
Eitel JUH, Keefe R F, Long DS, Davis A S, Vierling LA (2010)
Active ground optical remote sensing for improved monitoring of seedling stress in nurseries. Sensors 10: 2843-2850.
CrossRef | Gscholar
(13)
Entcheva-Campbell PK, Rock BN, Martin ME, Neefus CD, Irons JR, Middletin EM, Albrechtova J (2004)
Detection of initial damage in Norway spruce canopies using hyperspectral airborne data. International Journal of Remote Sensing 24: 5557-5583.
CrossRef | Gscholar
(14)
Fischer R, Lorenz M (2011)
Forest condition in Europe. Technical Report, ICP Forests and FutMon. Work Report of the Institute for World Forestry 2011/1. ICP Forests, Hamburg, Germany.
Gscholar
(15)
Gitelson AA, Merzlyak MN (1996)
Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148: 494-500.
CrossRef | Gscholar
(16)
Gitelson AA, Gritz Y, Merzlyak MN (2003)
Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160: 271-282.
CrossRef | Gscholar
(17)
Horler DNH, Dockray M, Barber J (1983)
The red edge of plant leaf reflectance. International Journal of Remote Sensing 4: 273-88.
CrossRef | Gscholar
(18)
Huber S, Kneubuehler M, Psomas A, Itten K, Zimmermann N (2008)
Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. Forest ecology and management 256: 491-501.
CrossRef | Gscholar
(19)
Luther JE, Carroll AL (1999)
Development of an index of balsam fir vigor by foliar spectral reflectance. Remote Sensing of Environment 69: 241-252.
CrossRef | Gscholar
(20)
Malenovský Z, Ufer C, Lhotáková Z, Clevers J G PW, Schaepman ME, Albrechtová J, Cudlín P (2006)
A new hyperspectral index for chlorophyll estimation of a forest canopy: area under curve normalised to maximal band depth between 650-725 nm. EARSeL eProceedings 5: 161-172.
Gscholar
(21)
Manakos I, Manevski K, Petropoulos GP, Elhag M, Kalaitzidis C (2010)
Development of a spectral library for mediterranean land cover types. In: Proceedings of the “30 EARSeL Symposium: Remote Sensing for Science, Education and Natural and Cultural Heritage” (Reuter R ed). Paris (France), 31 May - 3 June 2010, pp. 663-668.
Gscholar
(22)
Martin ME, Aber JD (1997)
High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecological Applications 7: 431-443.
CrossRef | Gscholar
(23)
Moorthy I, Miller J, Noland TL (2008)
Estimating chlorophyll concentration in conifer needles with hyperspectral data: an assessment at the needle and canopy level. Remote Sensing of Environment 112: 2824-2838.
CrossRef | Gscholar
(24)
Mozgeris G, Augustaitis A, Gečionis A (2011)
Small format aerial images to estimate the pine crown defoliation. In: Proceedings the “Fifth International Scientific Conference on Rural Development”. Akademija, Kaunas distr. (Lithuania), 24-25 November 2011. Aleksandras Stulginskis University, vol. 5, book 2, pp. 452-458.
Gscholar
(25)
Nidamanuri RR, Zbell B (2011)
Transferring spectral libraries of canopy reflectance for crop classification using hyperspectral remote sensing data. Biosystems engineering 110: 231 - 246.
CrossRef | Gscholar
(26)
Nobuya M, Dobbertin M (2004)
Within country accuracy of tree crown transparency estimates using the image analysis system CROCO: a case study from Switzerland. Environmental Modelling and Software 19: 1089-1095.
CrossRef | Gscholar
(27)
Ozolinčius R, Stakenas V (1996)
Forest health monitoring in Lithuania: 1988-1995. Lietuvos Mišku Institutas, Kaunas, Lithuania. [in Lithuanian]
Gscholar
(28)
Repšys J (1992)
The optical properties of coniferous trees damaged by air pollution. Musu Girios 1: 5-6.
Gscholar
(29)
Rock BN, Vogelmann JE, Williams DL, Vogelmann AF, Hoshizaki T (1986)
Remote detection of forest damage. BioScience 36: 439-445.
CrossRef | Gscholar
(30)
Shaw G, Manolakis D (2002)
Signal processing for hyperspectral image exploitation. IEEE Signal Processing Magazine 19: 12-16.
CrossRef | Gscholar
(31)
Smith KL, Steven MD, Colls JJ (2004)
Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment 92: 207-217.
CrossRef | Gscholar
(32)
Solberg S, Næsset E, Lange H, Bollandsås OM (2004)
Remote sensing of forest health. International Archieves of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 36 - 8/W2, pp. 161-166.
Gscholar
(33)
Somers B, Verbesselt J, Ampea E M, Sims N, Verstraetena W W, Coppina P (2010)
Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat 5-TM and EO-1 Hyperion data. International Journal of Applied Earth Observation and Geoinformation 12: 270-277.
CrossRef | Gscholar
(34)
State Forest Service (2011)
Lithuanian statistical yearbook of forestry. Lutute, Kaunas, Lithuania.
Gscholar
(35)
Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer V (2004)
Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment 91: 354-376.
CrossRef | Gscholar
(36)
Treitz PM, Howarth PJ (1999)
Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography 23: 359-390.
Gscholar
(37)
Varshney P, Arora M (2004)
Advanced image processing techniques for remotely sensed hyperspectral data. Springer Press, Berlin, Germany.
Gscholar
(38)
Vogelmann JE, Rock BN (1988)
Assessing forest damage in high-elevation coniferous forests in Vermont and New Hampshire using thematic mapper data. Remote Sensing of Environment 24: 227-246.
CrossRef | Gscholar
(39)
Wang Q, Li P (2012)
Hyperspectral indices for estimating leaf biochemical properties in temperate deciduous forests: Comparison of simulated and measured reflectance data sets. Ecological Indicators 14: 56-65.
CrossRef | Gscholar
(40)
Wold H (1966)
Estimation of principal components and related models by iterative least squares. In: “Multivariate Analysis” (Krishnaiah PR ed). Academic Press, New York, USA, pp. 391-420.
Gscholar
(41)
Wold S, Esbensen K, Geladi P (1987)
Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2: 37-52.
CrossRef | Gscholar
(42)
Wold S, Sjostrom M, Eriksson L (2001)
PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58: 109-130.
CrossRef | Gscholar
(43)
Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006)
Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. Forest Ecology and Management 221 (1-3): 27-41.
CrossRef | Gscholar
(44)
Zarco-Tejada PJ, Miller JR, Harron J, Hu B, Noland TL, Goel N, Mohammed GH, Sampson P (2004)
Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sensing of Environment 89: 189-199.
CrossRef | Gscholar
(45)
Zomer RJ, Trabucco A, Ustin SL (2009)
Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. Journal of Environmental Management 90: 2170-2177.
CrossRef | Gscholar
 

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