T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image Interpretation, p.756, 2008.

T. Key, T. A. Warner, J. B. Mcgraw, and M. A. Fajvan, A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest, Remote Sens. Environ, vol.75, pp.100-112, 2001.

, Green Infrastructure (GI)-Enhancing Europe's Natural Capital, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, 2013.

S. Melles, S. Glenn, and K. Martin, Urban bird diversity and landscape complexity: Species-environment associations along a multiscale habitat gradient, Conserv. Ecol, vol.7, issue.5, 2003.

, Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis, 2005.

A. A. Alvey, Promoting and preserving biodiversity in the urban forest. Urban For. Urban Green, vol.5, pp.195-201, 2006.

D. J. Nowak, E. J. Greenfield, R. E. Hoehn, and E. Lapoint, Carbon storage and sequestration by trees in urban and community areas of the United States, Environ. Pollut, vol.178, pp.229-236, 2013.

M. Pasher, M. Mcgovern, M. Khoury, and J. Duff, Assessing carbon storage and sequestration by Canada's urban forests using high resolution Earth observation data. Urban For. Urban Green, vol.13, pp.484-494, 2014.

I. Kowarik, P. Py?ek, K. Prach, M. Rejmanek, and M. Wade, On the role of alien species in urban flora and vegetation. In Plant Invasions-General Aspects and Special Problems, pp.85-103, 1995.

, Remote Sens, vol.10, 2018.

I. J. Song, S. K. Hong, H. O. Kim, B. Byun, and Y. Gin, The pattern of landscapes patches and invasion of naturalized plants in developed areas of urban Seoul, Landsc. Urban Plan, vol.70, pp.205-219, 2005.

P. J. Hardin and R. R. Jensen, The effect of urban leaf area on summertime urban surface kinetic temperatures: A Terre Haute case study. Urban For. Urban Green, vol.6, pp.63-72, 2007.

A. Roloff, S. Korn, and S. Gillner, The climate-species-matrix to select tree species for urban habitats considering climate change. Urban For. Urban Green, vol.8, pp.295-308, 2009.

A. Kontogianni, T. Tsitsoni, and G. Goudelis, An index based on silvicultural knowledge for tree stability assessment and improved ecological function in urban ecosystems, Ecol. Eng, vol.37, pp.914-991, 2011.

V. Marozas, G. Cekstere, M. Laivins, and L. Straigyte, Comparison of neophyte communities of Robinia pseudoacacia L. and Acer negundo L. in the eastern Baltic Sea region cities of Riga and Kaunas. Urban For. Urban Green, vol.14, pp.826-834, 2015.

L. Straigyt-?-e, G. Cekstere, M. Laivins, and V. Marozas, The spread, intensity and invasiveness of the Acer negundo in Riga and Kaunas, Dendrobiology, vol.74, pp.155-166, 2015.

M. J. Garcia-garcia, A. Sánchez-medina, E. Alfonso-corzo, and C. G. Garcia, An index to identify suitable species in urban green areas. Urban For. Urban Green, vol.16, pp.43-49, 2016.

L. Straigyt-?-e and T. Vaidelys, Inventory of green spaces and woody plants in the urban landscape in Ariogala. South-East Eur, vol.3, pp.115-121, 2012.

A. E. Hostetler, J. Rogan, D. Martin, V. ;. Delauer, and J. O'neil-dunne, Characterizing tree canopy loss using multi-source GIS data in Central Massachusetts, USA. Remote Sens. Lett, vol.4, pp.1137-1146, 2013.

R. Pu and S. Landry, A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens, vol.124, pp.516-533, 2012.

J. A. Mcgee, S. D. Day, R. H. Wynne, and M. B. White, Using geospatial tools to assess the urban tree canopy: Decision support for local governments, J. For, vol.110, pp.275-286, 2012.

S. W. Macfaden, J. P. ;-o'neil-dunne, A. R. Royar, J. W. Lu, and A. G. Rundle, High resolution tree canopy mapping for New York City using LIDAR and object-based image analysis, J. Appl

X. Li and G. Shao, Object-based urban vegetation mapping with high-resolution aerial photography as a single data source, Int. J. Remote Sens, vol.34, pp.771-789, 2013.

J. Tigges, T. Lakes, and P. Hostert, Urban vegetation classification: Benefits of multitemporal RapidEye satellite data, Remote Sens. Environ, vol.136, pp.66-75, 2013.

K. Merry, P. Bettinger, J. Siry, and J. M. Bowker, Estimating urban forest carbon sequestration potential in the Southern United States using current remote sensing imagery sources, Geogr. Tech, vol.10, pp.78-89, 2015.

. State-forest-service, Mi?kotvarkos Darbu ? Vykdymo Instrukcija (Specifications of Forest Management Planning Projects). In Lithuanian, p.25, 2010.

J. Im and J. R. Jensen, Hyperspectral remote sensing of vegetation, Geogr. Compass, vol.2, pp.1943-1961, 2008.

K. L. Castro-esau, G. A. Sanchez-azofeifa, and T. Caelli, Discrimination of lianas and trees with leaf-level hyperspectral data, Remote Sens. Environ, vol.90, pp.353-372, 2004.

C. Vaiphasa, S. Ongsomwang, T. Vaiphasa, and A. K. Skidmore, Tropical mangrove species discrimination using hyperspectral data: A laboratory study, Estuar. Coast. Shelf Sci, vol.65, pp.371-379, 2005.

J. A. Van-aardt and M. Norris-rogers, Spectral-age interactions in managed, even-aged Eucalyptus plantations: Application of discriminant analysis and classification and regression trees approaches to hyperspectral data, Int. J. Remote Sens, vol.29, pp.1841-1845, 2008.

K. Manevski, I. Manakos, G. P. Petropoulos, and C. Kalaitzidis, Discrimination of common Mediterranean plant species using field spectroradiometry, Int. J. Appl. Earth Obs. Geoinf, vol.13, pp.922-933, 2011.

G. Masaitis and G. Mozgeris, Some peculiarities of laboratory measured hyperspectral reflectance characteristics of Scots pine and Norway spruce needles, Proceedings of the 18th Annual International Conference Research for Rural Development, pp.25-32, 2012.

G. Masaitis, G. Mozgeris, and A. Augustaitis, Estimating crown defoliation and the chemical constituents in needles of Scots pine (Pinus sylvestris L.) trees by laboratory acquired hyperspectral data, Balt. For, vol.20, pp.314-325, 2014.

, Remote Sens, vol.10, 2018.

D. Danusevicius, G. Masaitis, and G. Mozgeris, Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances, Silvae Genet, vol.63, pp.169-180, 2014.

Q. Xiao, S. L. Ustin, and E. G. Mcpherson, Using AVIRIS data and multiple-masking techniques to map urban forest tree species, Int. J. Remote Sens, vol.25, pp.5637-5654, 2004.

M. Alonzo, K. Roth, and D. Roberts, Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis, Remote Sens. Lett, vol.4, pp.513-521, 2013.

M. Alonzo, B. Bookhagen, and D. A. Roberts, Urban tree species mapping using hyperspectral and lidar data fusion, Remote Sens. Environ, vol.148, pp.70-83, 2014.

A. Carleer and E. Wolff, Exploitation of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens, vol.70, pp.135-140, 2004.

M. A. Cho, P. Debba, R. Mathieu, L. Naidoo, J. Van-aardt et al., Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis, IEEE Trans. Geosci. Remote Sens, vol.48, pp.4133-4142, 2010.

E. Raczko and B. Zagajewski, Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images, Eur. J. Remote Sens, vol.50, pp.144-154, 2017.

W. Ouerghemmi, S. Gadal, G. Mozgeris, D. Jonikavi?ius, and C. Weber, Urban Objects Classification by Spectral Library: Feasibility and Applications. Joint Urban Remote Sensing Event (JURSE), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01492070

F. A. Gougeon and D. G. Leckie, The individual tree crown approach applied to IKONOS images of a coniferous plantation area, Photogramm. Eng. Remote Sens, vol.72, pp.1287-1297, 2006.

S. Aksoy, A. Gökhan, and T. Wassenaar, Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery, IEEE Trans. Geosci. Remote Sens, vol.48, pp.511-522, 2010.

L. M. Moskal, D. M. Styers, and M. Halabisky, Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data. Remote Sens, vol.3, pp.2243-2262, 2011.

M. Immitzer, C. Atzberger, and T. Koukal, Tree species classification with RandomForest using very high spatial resolution 8-Band WorldView-2 satellite data. Remote Sens, vol.4, pp.2661-2693, 2012.

L. Ballanti, L. Blesius, E. Hines, and B. Kruse, Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers

O. Nevalainen, E. Honkavaara, S. Tuominen, N. Viljanen, T. Hakala et al., Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging

E. Ndikumana, D. Ho-tong-minh, N. Baghdadi, D. Courault, and L. Hossard, Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, vol.10, 1217.
DOI : 10.3390/rs10081217
URL : http://www.mdpi.com/2072-4292/10/8/1217/pdf

W. Li, H. Fu, L. Yu, P. Gong, D. Feng et al., Stacked Autoencoder-Based Deep Learning for Remote-Sensing Image Classification: A Case Study of African Land-Cover Mapping, Int. J. Remote Sens, vol.37, pp.5632-5646, 2016.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification, IEEE Trans. Geosci. Remote Sens, vol.55, pp.645-657, 2017.
DOI : 10.1109/tgrs.2016.2612821
URL : https://hal.archives-ouvertes.fr/hal-01369906

G. Forzieri, L. Tanterib, G. Moser, and F. Catani, Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies, Int. J. Appl. Earth Obs. Geoinf, vol.23, pp.313-323, 2013.
DOI : 10.1016/j.jag.2012.10.004

K. Anderson and K. J. Gaston, Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Front. Ecol. Environ, vol.11, pp.138-146, 2013.
DOI : 10.1890/120150
URL : https://ore.exeter.ac.uk/repository/bitstream/10871/16852/1/Anderson_Gaston_Frontiers_ecology_envt_2013.pdf

I. Colomina and P. Molina, Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm. Remote Sens, vol.92, pp.79-97, 2014.
DOI : 10.1016/j.isprsjprs.2014.02.013
URL : https://doi.org/10.1016/j.isprsjprs.2014.02.013

Q. Feng, J. Liu, and J. Gong, UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens, vol.7, pp.1074-1094, 2015.
DOI : 10.3390/rs70101074
URL : http://www.mdpi.com/2072-4292/7/1/1074/pdf

Y. Lin, M. Jiang, Y. Yao, L. Zhang, and J. Lin, Use of UAV oblique imaging for the detection of individual trees inresidential environments. Urban For. Urban Green, vol.14, pp.404-412, 2015.

, Remote Sens, vol.10, 2018.

E. Honkavaara, H. Saari, J. Kaivosoja, I. Pölönen, T. Hakala et al., Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture, Remote Sens, vol.5, pp.5006-5039, 2013.

G. Yang, C. Li, Y. Wang, H. Yuan, H. Feng et al., The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

G. Mozgeris and A. Augustaitis, Estimating crown defoliation of Scots pine (Pinus sylvestris L.) trees using small format digital aerial images, vol.6, pp.15-22, 2013.

G. Mozgeris, D. Jonikavi?ius, D. Jovarauskas, R. Zinkevi?ius, S. Petkevi?ius et al., Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat, Precis. Agric, 2018.

, EnsoMOSAIC Aerial Mapping System-Overview, p.27, 2018.

. Rikola-product-family, , p.27, 2018.

C. Noaa-solar, , 2018.

U. Atlas, , 2018.

, EnsoMOSAIC Aerial Mapping System-Components, p.27, 2018.

M. W. Matthew, S. M. Adler-golden, A. Berk, S. C. Richtsmeier, R. Y. Levine et al., Status of atmospheric correction using a MODTRAN4-based algorithm, SPIE Proc, vol.4049, pp.199-207, 2000.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Trans. Geosci. Remote Sens, vol.26, pp.65-74, 1988.

?. Kauno-miesto-?eldynu-?-?em and . Elapis, Map of Kaunas City Green Areas, 2018.

, Data Mining Software in Java, vol.3, 2018.

G. H. John and P. Langley, Estimating continuous distributions in Bayesian classifiers, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995.

D. W. Aha, D. Kibler, and M. K. Albert, Instance-based learning algorithms, Mach. Learn, vol.6, pp.37-66, 1991.

L. R. Breiman and . Forest, Mach. Learn, vol.45, pp.5-32, 2001.

C. Mlpclassifier, , p.27, 2018.

J. R. Quinlan and . C4, 5: Programs for Machine Learning

J. R. Landis and G. Koch, The measurement of observer agreement for categorical data, Biometrics, vol.33, pp.159-174, 1977.

A. Ghiyamata, H. Z. Shafri, G. A. Mahdirajic, M. Abdul-rashid, A. R. Shariff et al., Hyperspectral discrimination of tree species with different classifications using single-and multiple-endmember, Int. J. Appl. Earth Obs. Geoinf, vol.23, pp.177-191, 2013.

C. A. Baldeck, G. P. Asner, R. E. Martin, C. B. Anderson, D. E. Knapp et al., Operational tree species mapping in a diverse tropical forest with airborne imaging spectroscopy, PLoS ONE, vol.10, 2015.

G. Pried¯-itis, I. ?mits, S. Da-'-gis, L. Paura, ?. Kr-¯-umin et al., Assessment of hyperspectral data analysis methods to classify tree species, Res. Rural Dev, vol.2, pp.7-13, 2015.

, Remote Sens, vol.10, 2018.

G. Tagliabue, C. Panigada, R. Colombo, F. Fava, C. Cilia et al., Forest species mapping using airborne hyperspectral APEX data. Misc. Geogr, Reg. Stud. Dev, vol.20, pp.28-33, 2016.

D. A. Roberts, S. L. Ustin, S. Ogunjemiyo, J. Greenberg, S. Z. Dobrowski et al., Spectral and structural measures of northwest forest vegetation at leaf to landscape scales, vol.7, pp.545-562, 2004.

J. Kuusk, A. Kuusk, M. Lang, and A. Kallis, Hyperspectral reflectance of boreo-nemoral forests in a dry and normal summer, Int. J. Remote Sens, vol.31, pp.159-175, 2010.

G. Masaitis and G. Mozgeris, The influence of the growing season on the spectral reflectance properties of forest tree species, Res. Rural Dev, vol.2, pp.20-26, 2013.

G. Mozgeris, Mi?kotvarkoje naudojamu ? ortofototransformuotu ? aerovaizdu ? de?ifravimo po?ymiai

. Mi?kininkyst?, , vol.1, pp.49-59, 2004.

J. Lee, X. Cai, J. Lellmann, M. Dalponte, Y. Malhi et al., Individual tree species classification from airborne multi-sensor imagery using robust PCA, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, vol.9, pp.2554-2567, 2016.

W. Ouerghemmi, S. Gadal, G. Mozgeris, and D. Jonikavi?ius, Urban vegetation mapping by airborne hyperspetral imagery: Feasability and limitations, Proceedings of the 9th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), pp.23-26, 2018.

A. Kamilaris and F. X. Prenafeta-boldú, Deep learning in agriculture: A survey, Comput. Electron. Agric, vol.147, pp.70-90, 2018.

T. Murakami, Seasonal variation in classification accuracy of forest-cover types examined by a single band or band combinations, J. For. Res, vol.3, pp.211-215, 2004.

A. Burkholder, Seasonal Trends in Separability of Leaf Reflectance Spectra for Ailanthus altissima and Four Other Tree Species, 2010.

M. Hesketh and G. A. Sánchez-azofeifa, The effect of seasonal spectral variation on species classification in the Panamanian tropical forest, Remote Sens. Environ, vol.118, pp.73-82, 2012.

G. Masaitis, The Potential of Hyperspectral Imaging to Detect Forest Tree Species and Evaluate Their Condition, 2013.

W. M. Ciesla, Remote Sensing in Forest Health Protection

, Remote Sensing Applications Center: Salt Lake City