Abstract
Invasion of alien plant species entails great ecological, economic, and social consequences. One of the effective ways to manage invasive species may be to account for and destroy them using unmanned aerial vehicles. However, this requires learning to identify invasive species using real-time remote sensing. Recently, great hopes for solving this problem have been placed on hyperspectral cameras. In this regard, there is a need to fundamentally answer the question of the possibility of identifying plant species from spectral data, regardless of the time of data acquisition. The aim of the study was to identify four species of woody plants by the time series of spectral characteristics of their leaves, obtained using a hyperspectral camera. The study was conducted in laboratory conditions, in which the number of unaccounted for factors is much less than in the field. The objects of study were one native species Quercus robur L. and three species invasive for Europe – Fraxinus pennsylvanica Marsh., Ailanthus altissima (Mill.) Swingle, Parthenocissus inserta (A. Kern.) Fritsch. The collection of leaves for Hyperspectral Imaging (HSI) was carried out during the growing season of the plants at intervals of 7–10 days. Random Forest (RF) was chosen as the object classification method. The RF pixel-based test was carried out both for specific calendar dates (time slice) and for the time series when the RF model was trained on the data of one calendar date and tested on other calendar dates. None of the RF testing methods was able to classify all four species simultaneously with sufficient probability (more than 90%). Therefore, RF testing of combinations of two samples was used – "species" & "other three species" for all calendar dates. This approach made it possible to classify species by spectral characteristics of leaves with 100% reliability. It has been established that the spectral bands informative for RF pixel-based classification lie in the visible range of the spectrum in the range from 462 to 478, from 510 to 534, and from 566 to 646 nm.
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Data Availability
The data presented in this study are available on request from the corresponding author.
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P.D. Project administration, Methodology, Writing—Review & Editing, Investigation, Formal analysis, Funding acquisition. B.K. Writing—Original Draft, Investigation, Formal analysis. A.D. Investigation, Data Curation, Formal analysis, Visualization. T.V. Resources. Our article has not been published previously, nor is it currently under consideration for publication elsewhere. All authors have contributed, read, and approved the final manuscript, its publication is approved by all.
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CRediT authorship contribution statement Pavel Dmitriev: Project administration, Methodology, Writing—Review & Editing, Investigation, Formal analysis, Funding acquisition. Boris Kozlovsky: Writing—Original Draft, Investigation, Formal analysis. Anastasiya Dmitrieva: Investigation, Data Curation, Formal analysis, Visualization. Tatiana Varduni: Resources. Our article has not been published previously, nor is it currently under consideration for publication elsewhere. All authors have contributed, read, and approved the final manuscript, its publication is approved by all.
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Dmitriev, P.A., Kozlovsky, B.L., Dmitrieva, A.A. et al. Classification of invasive tree species based on the seasonal dynamics of the spectral characteristics of their leaves. Earth Sci Inform 16, 3729–3743 (2023). https://doi.org/10.1007/s12145-023-01118-0
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DOI: https://doi.org/10.1007/s12145-023-01118-0