Identification of the Leaves of Ulmus pumila L., Tilia cordata Mill. and Acer campestre L. Using Vegetation Indices

Yıl 2024, , 54 - 66, 27.06.2024

Pavel Dmitriev Boris Kozlovsky Anastasiya Dmitrieva Tatiana Varduni

https://doi.org/10.33904/ejfe.1430606

Öz

The aim of the research was to evaluate a group of vegetation indices (VIs) for identifying the leaves of some species including Ulmus pumila L., Tilia cordata Mill. and Acer campestre L. Hyperspectral imaging (HSI) was carried out under artificial lighting in laboratory conditions using a Cubert UHD-185 hyperspectral camera. A technique was developed for the automated selection of pure spectral profiles from hyperspectral images by setting a double barrier specified by intervals of PSSR and NDVI VIs. A total of 80 VIs was calculated. A statistical analysis of the data was carried out to determine their representativeness. The VIs that were most dependent on the species characteristics of the trees were determined using analysis of variance (ANOVA) and principal component analysis (PCA) methods. Research has shown that the PCA method is effective and sufficient to identify the group of VIs characterized by the highest dispersion related to tree species. The PCA carried out for pairs of tree species made it possible to identify a group of vegetation indices, the value of which to the greatest extent depends on species characteristics. These VIs are Carter2, CI2, CRI4, GMI2, mSR2, NDVI2, OSAVI2, SR1, Carter4, Datt2, SR6, Datt, DD, Maccioni, MTC.

Anahtar Kelimeler

Hyperspectral imaging, Principal component analysis, Region of interest, Species classification, Woody plants

Etik Beyan

All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

Destekleyen Kurum

Southern Federal University

Proje Numarası

Ministry of Science and Higher Education of the Russian Federation (no. FENW-2023-0008)

Kaynakça

• Aasen, H., Burkart, A., Bolten, A., Bareth, G. 2015. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. JPRS, 108:245-259. https://doi.org/ 10.1016/ j.isprsjprs.2015.08.002
• Aneta, M., Fassnacht, F.E., Stereńczak, K. 2020. Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 84: 101960, doi.org/10.1016/j.jag.2019.101960
• Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., Michels, R., Soukkamäki, J. 2015. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements. Photogramm. Fernerkundung, Geoinf. 69-79. https://doi.org/10.1127/pfg/2015/0256.
• Cao, J., Leng, W., Liu, K., Liu, L., He, Z., Zhu, Y. 2018. Object-Based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10:89. https://doi.org/10.3390/rs10010089.
• Chen, C., Jing, L., Li, H., Tang, Y., Chen, F. 2023. Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features. Remote Sens. 15: 2301
• Dainelli, R., Toscano, P., Di Gennaro, S.F., Matese, A. 2021. Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing - a Systematic Review. Part II: Research Applications. Forests, 12(4):397. https://doi.org/10.3390/f12040397.
• Dmitriev, P.A., Kozlovsky, B.L., Kupriushkin, D.P., Lysenko, V.S., Rajput, V.D. et al. 2022a. Identification of species of the genus Acer L. using vegetation indices calculated from the hyperspectral images of leaves. Remote Sensing Applications: Society and Environment, 100679. https://doi.org/10.1016/j.rsase.2021.100679.
• Dmitriev, P.A., Kozlovsky, B.L., Kupriushkin, D.P., Dmitrieva, A.A., Rajput, V.D. et al. 2022b. Assessment of Invasive and Weed Species by Hyperspectral Imagery in Agrocenoses Ecosystem. Remote Sens., 14:2442. https://doi.org/10.3390/ rs14102442.
• Egli, S., Höpke, M. 2020. CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations. Remote Sens., 12:2-17. https://doi.org/10.3390/rs12233892.
• Fassnacht, F.R., White, J.C., Wulder, M.A., Næsset, E. 2024. Remote sensing in forestry: current challenges, considerations and directions, Forestry: An International Journal of Forest Research, 97(1): 11–37. https://doi.org/10.1093/forestry/ cpad024
• Grabska, E., Frantz, D. and Ostapowicz, K. 2020 Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the polish Carpathians. Remote Sens. Environ. 251, 112103. https://doi.org/10.1016/ j.rse.2020.112103.
• Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A. 2022 Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sens. Environ. 282, 113276. https://doi.org/10.1016/j.rse.2022.113276.
• Heupel, K., Spengler, D., Itzerott, S.A. 2018. Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information. PFG, 86:53-69. https://doi.org/10.1007/s41064-018-0050-7.
• Zhang, J., Zhang, Y., Zhou, T., Sun, Y., Yang, Z., Zheng, S. 2023. Research on the identification of land types and tree species in the Engebei ecological demonstration area based on GF-1 remote sensing, Ecological Informatics, 77(2023): 102242, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2023. 102242
• Onishi, M., Ise, T. 2021. Explainable identification and mapping of trees using UAV RGB image and deep learning. Scientific Reports. 11(1):1-15. https://doi.org/10.1038/s41598-020-79653-9.
• Panov, V.D., Lurie, P.M., Larionov, Y.A. 2006. The Climate of the Rostov Region: Yesterday, Today, Tomorrow. Donskoy Publishing House: Rostov-on-Don, Russia, p. 488.
• R Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (date of circulation: December 01, 2023).
• Saeed, S., Latif, M.A., Rajput, M.A. 2021. Fuzzy-Based Multi-Crop Classification Using High Resolution UAV Imagery. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah, 19(1):1-8. https://doi.org/10.52584/ QRJ.1901.01.
• Sothe, C., De Almeida, C.M., Schimalski, M.B., La Rosa, L.E.C., Castro et al. 2020. Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data. GIScience Remote Sens., 57:369-394. https://doi.org/10.1080/ 15481603.2020.1712102.
• van der Werff, H., Ettema, J., Sampatirao, A., Hewson, R. 2022. How Weather Affects over Time the Repeatability of Spectral Indices Used for Geological Remote Sensing. Remote Sens. 14: 6303. https:// doi.org/10.3390/rs14246303
• Vannoppen, A., Gobin, A., Kotova, L., Buntemeyer, L., Remedio, A.R. et al. 2020. Wheat yield estimation from ndvi and regional climate models in Latvia. Remote Sensing., 12(14):2206. https://doi.org/ 10.3390/rs12142206.
• Zhong, H., Lin, W., Liu, H., Ma, N., Liu, K., Cao, R., Wang, T., Ren, Z. 2022. Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China. Front. Plant Sci. 13:964769. doi: 10.3389/fpls.2022.964769.