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Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Masaitis, Gediminas;Mozgeris, Gintautas;Augustaitis, Algirdas
Title: Estimating Crown Defoliation and the Chemical Constituents in Needles of Scots Pine (Pinus sylvestris L.) Trees by Laboratory Acquired Hyperspectral Data
Is part of: Baltic Forestry. Girionys : Lithuanian Forest Research Institute et all, 2014, Vol. 20, N 2
Extent: p. 314-325
Date: 2014
Keywords: Hyperspectral reflectance;Hyperspectral imaging;Scots pine;Defoliation;Chemical constituents
Abstract: The studies, which involve the potential of imaging spectrometry, are among the most promising ones in forest health assessment. This study estimated crown defoliation and the concentrations of some chemical constituents in the needles of Scots pine (Pinus sylvestris L.) trees using laboratory acquired hyperspectral data. Needle samples from 67 Scots pine trees, which showed crown defoliation within the range from 0% to 80% (using 5% gradation), were collected in two mature stands located in the eastern Lithuania. The concentrations of ten chemical elements in the needles were also measured. The hyperspectral reflectance data of the needle samples was recorded under laboratory conditions using a VNIR 400H portable hyperspectral imaging camera operating in the 400–1,000 nm range. Principal component analysis and linear discriminant analysis were used to classify the needle samples into defoliation classes and partial least squares regression was used to predict the concentration of chemical constituents by means of hyperspectral reflectance data. Spectral reflectance data was found to poorly discriminate the needle samples into defoliation classes assessed using 5% steps (kappa statistic was 0.29 and 0.26 for the previous and current year needles, respectively). However, combining the samples into four damage classes, according to the UNECE/FAO definition (none: under 10%; slight: > 10–25%; moderate: > 25–60% and severe: over 60%) improved the spectral reflectance data discrimination ability significantly for the previous year (kappa statistic was 0.50), but not significantly for the current year (kappa statistic was 0.35) needles. Classification into three damage classes (under 30%; > 30–50% and over 50%) was perfect (kappa statistic 1.0)
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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