Unsupervised machine learning based on vegetation indices and reflectance spectra for tree classification
https://doi.org/10.21266/2079-4304.2025.255.89-102
Abstract
The article is devoted to the use of the k-means machine learning method and vegetation indices for the classification of tree species based on UAV survey materials. Vegetation indices analyze the spectral characteristics of plants, which is especially useful for distinguishing similar tree species, as they exhibit different spectral signatures. The study was conducted on two forest plots with tree distributions of pine-birch and pine-spruce. Data were collected from five spectral sensors: Blue (470 nm), Green (560 nm), Red (665 nm), Far Red (720 nm), and Infrared (840 nm). Tree crown detection was performed using RGB images captured by spectral sensors and the DeepForest model. Within the boundaries of the detected crowns, spectral sensor data were processed to calculate the vegetation indices for each tree. Pixels for each tree from the matrix view were decomposed into a vector, sorted in ascending order. The following parameters were calculated from each vector: minimum, maximum, mean, median values, as well as the 25th and 75th percentiles and Shannon entropy. Dimensionality reduction via PCA and k-means clustering were then applied to differentiate tree types. The results showed that for pine and spruce, classification accuracy exceeded 80%, whereas for pine and birch, the metrics were less successful, possibly due to seasonal changes in the indices. In both cases, DVI proved to be the most effective indicator, demonstrating high efficiency for certain forest covers.
About the Authors
I. S. Steshin
Volga State Technological University
Russian Federation
Russian Federation
Steshin Ilya S. – junior researcher
124000, Lenin Sq. 3. Yoshkar-Ola. Mari El
I. V. Petukhov
Volga State Technological University
Russian Federation
Russian Federation
Petukhov Igor V. – DSc (Technical), Professor of the Department of Design and Production of Electronic Computing Tools
124000, Lenin Sq. 3. Yoshkar-Ola. Mari El
References
1. Barandas M., Folgado D., Fernandes L., Santos S., Abreu M., Bota P., Liu Hui, Schultz T., Gamboa H. TSFEL: Time Series Feature Extraction Library. SoftwareX, 2020, vol. 11, art. no. 100456.
2. Chehreh B., Moutinho A., Viegas C. Latest Trends on Tree Classification and Segmentation Using UAV Data–A Review of Agroforestry Applications. Remote Sens (Basel), 2023, vol. 15, iss. 9, art. no. 2263.
3. Crutchfield J.P., Feldman D.P. Regularities Unseen, Randomness Observed: Levels of Entropy Convergence. 2001. URL: https:.arxiv.org/abs/cond-mat/0102181 (accessed: 14.09.2024)
4. Fassnacht F.E., Latifi H., Stereńczak K., Modzelevska A., Lefsky M., Waser L.T., Straub C., Ghosh A. Review of studies on tree species classification from remotely sensed data. Remote Sens Environ., 2016, vol. 186, pp. 64–87.
5. Geoscan Gemini Multispectral. URL: https://www.geoscan.ru/ru/products/gemini-m (accessed May 05, 2024).
6. Hamunyela E., Verbesselt J., Herold M. Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sens Environ., 2016, vol. 172, pp. 126–138.
7. Harris C.R., Millman J., van der Walt S.J., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith N.J., Kern R., Picus M., Hoyer S., van Kerkwijk M.H., Brett M., Haldane A., del Río J.F., Wiebe M., Peterson P., Gérard-Marchant P., Sheppard K., Reddy T., Weckesser W., Abbasi H., Gohlke H., Oliphant T.E. Array programming with NumPy. Nature, 2020, vol. 585, iss. 7825, pp. 357–362.
8. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ., 2002, vol. 83, iss. 1–2, pp. 195–213.
9. Jollife I. T., Cadima J. Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, vol. 374, art. no. 2065.
10. Macqueen J. Some methods for classification and analysis of multivariate observations. Berkeley Symp. on Math. Statist. and Prob., 1967, pp. 281–297.
11. Mikhaylichenko A.A. Analytical review of methods for assessing the quality of classification algorithms. ASU Bulletin, 2022, iss. 4(311), pp. 52–59. (In Russ.)
12. Nevalainen O., Honkavaara E., Tuominen S., Viljanen N., Hakala T., Yu Xiaowei, Hyyppä J., Saari H., Pölönen I., Imai N.N., Tommaselli A.M.G. Individual tree detection and classification with UAV-Based photogrammetric point clouds and hyperspectral imaging. Remote Sens (Basel), 2017, vol. 9, iss. 3, art. no. 185.
13. Onishi M., Ise T. Automatic classification of trees using a UAV onboard camera and deep learning. 2018. URL: https:.arxiv.org/abs/1804.10390 (accessed: 14.09.2024)
14. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 2011, vol. 12, pp. 2825–2830.
15. Pettorelli N., Vik J.O., Mysterud A., Gaillard J.-M., Tucker S.J., Stenseth N.C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol., 2005, vol. 20, iss. 9, pp. 503–510.
16. Piekarski P., Zwoliński Z. Temporal variation in vegetation indexes for pine and beech stands during the vegetation season, Szczecin Lowland, Poland. Quaestiones Geographicae, 2014, vol. 33, iss. 3, pp. 131–143.
17. Rautiainen M., Nilson T., Lükk T. Seasonal reflectance trends of hemiboreal birch forests. Remote Sens Environ., 2009, vol. 113, iss. 4, pp. 805–815.
18. Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van der Walt S.J., Brett M., Wilson J., Millman K.J., Mayorov N., Nelson A.R.J., Jones E., Kern R., Larson E., Carey C.J., Polat I., Feng Yu, Moore E.W., VanderPlas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E.A., Harris C.R., Archibald A.M., Ribeiro A.H., Pedregosa F., van Mulbregt P., SciPy 1.0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods, 2020, vol. 17, iss. 3, pp. 261–272.
19. Wäldchen J., Mäder P. Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review. Archives of Computational Methods in Engineering, 2018, vol. 25, iss. 2, pp. 507–543.
20. Weinstein B.G., Marconi S., Bohlman S., Zare A., White E. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. Remote Sens (Basel), 2019, vol. 11, iss. 11, art. no. 1309.
21. Weinstein B.G., Marconi S., Bohlman S., Zare A., White E. Cross-site learning in deep learning RGB tree crown detection. Ecol. Inform., 2020, vol. 56, art. no. 101061.
22. Weinstein B.G., Graves S.J., Marconi S., Singh A., Zare A., Stewart D., Bohlman S., White E. A benchmark dataset for canopy crown detection and delineation in coregistered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network. PLoS Comput. Biol., 2021, vol. 17, iss. 7, art. no. e1009180.
23. Zhong Lihui, Dai Zhengquan, Fang Panfei, Cao Yong, Wang Leiguang. A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods. Forests, 2024, vol. 15, iss. 5, art. no. 852.
Review
For citations:
Steshin I.S., Petukhov I.V. Unsupervised machine learning based on vegetation indices and reflectance spectra for tree classification. Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii. 2025;(255):89-102. (In Russ.) https://doi.org/10.21266/2079-4304.2025.255.89-102
Views:
38
Email this article 