Classification of invasive tree species based on the seasonal dynamics of the spectral characteristics of their leaves

Dmitriev, Pavel A.; Kozlovsky, Boris L.; Dmitrieva, Anastasiya A.; Varduni, Tatiana V.

doi:10.1007/s12145-023-01118-0

Cited by 1 publication

(1 citation statement)

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“…During the preprocessing stage, noise was removed from the spectra by using a Savitsky-Golay filter with a length of 15 nm. To select the region of interest (ROI), the threshold of the vegetation index (VI) Carter5 was used with a value of more than 1.4 [52] (Figure 2b,c).…”

Section: Hyperspectral Imagery Data Preprocessingmentioning

confidence: 99%

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Dmitriev,

Kozlovsky,

Dmitrieva

2024

Horticulturae

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Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

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Section: Hyperspectral Imagery Data Preprocessingmentioning

confidence: 99%