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
Genetic diversity is an important indicator of forest sustainability requiring particular attention and new methods to obtain fast and cheap estimates of genetic diversity. We assessed the differences in visible (VIS) and near infrared (NIR) spectral reflectance properties of detached shoots of several distant Scots pine provenances aiming to identify the most informative spectral wavebands and the seasonal time for the genetic diversity scoring. Shoots of five trees per provenance were sampled at two week intervals during the active growth and fall. The samples were scanned using a hyperspectral camera, equipped with a highly sensitive spectrometer capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. The ANOVAs revealed significant provenance effects on the spectral reflectance at variable spectral intervals depending on the sampling occasion. During the active growth, PCA identified the most informative wavebands over whole spectral range investigated. During the shoot/needle hardiness development, NIR was the most informative. Provenance ranking in spectral reflectance returned geographically interpretable pattern. We conclude that there are significant provenance attributable and interpretable differences in spectral reflectance of Scots pine needles providing a good opportunity for detecting this spectral variation with the hyperspectral imaging technique.
This paper discusses the potential of visible and near-infrared hyperspectral imaging to describe properties of conventionally and organically grown carrots. 140 samples of four Lithuanian carrot cultivars were scanned using a VNIR400H hyperspectral camera, capable of covering the spectral range of 400-1000 nm with a sampling interval of 0.6 nm. Half of the samples were grown under organic farming conditions and the remainder under conventional conditions. Chemical and electro-chemical properties, i.e. nitrate content, acidity, reduction potential and electrical conductivity, were determined for the carrot root samples using conventional methods of chemical investigations. The ability to separate organically and conventionally grown samples on the basis of spectral data was examined by applying estimations of Jeffries-Matusita distances and linear discriminant analysis. Opportunities to predict the chemical and electro-chemical properties of samples applying the partial least squares regression and the spectral data as predictors were also investigated. The overall classification accuracy of samples of organically and conventionally grown carrot cultivars when applying linear discriminant analysis was in the range of 94.4-100% and the Jeffries-Matusita distances were in the range of 1.98-2.00. There was good prediction potential using the partial least squares regression for electrical conductivity (R 2 = 0.88) and reduction potential (R 2 = 0.81), better than moderate for nitrate content (R 2 = 0.77) and moderate for acidity (R 2 = 0.68) using hyperspectral reflectance data of carrot captured under laboratory conditions. Both the separation ability and prediction potential were higher if taking into account the cultivar.
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