[Andersen, H. E., Breindenbach, J., 2007: Statistical Properties of Mean Stand Biomass Estimators in a Lidar- Based Double Sampling Forest Survey Design. Proc. ISPRS III/3, III/4, V/3 and VIII/11., “LaserScanning 2007 and SilviLaser 2007“, p. 8-14.]Search in Google Scholar
[Baatz, M., Schäpe, M., 2000: Multiresolution segmentation - An optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (eds.): Angewandte Geographische Informations- Verarbeitung XII. Wichmann Verlag, Karlsruhe, p. 12-23.]Search in Google Scholar
[Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58:239-258.10.1016/j.isprsjprs.2003.10.002]Search in Google Scholar
[Blaschke, T., 2010: Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65:2-16.10.1016/j.isprsjprs.2009.06.004]Search in Google Scholar
[Bucha, T., Vladovič, J., Juriš, M., Barka, I., 2010: Aplikácie diaľkového prieskumu Zeme využiteľné v prácach HÚL. In: Súčasnosť a budúcnosť hospodárskej úpravy lesov na Slovensku, 10 p.]Search in Google Scholar
[Cleve, C., Kelly, M., Kearns, F. R., Moritz, M., 2008: Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32:317-326.10.1016/j.compenvurbsys.2007.10.001]Search in Google Scholar
[Debella-Gilo, M., K. Bjørkelo, K., Breidenbach, J., Rahlf, J., 2013: Object-Based analysis of aerial photogrammetric point cloud and spectral data for land cover mapping. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40:63-67.10.5194/isprsarchives-XL-1-W1-63-2013]Search in Google Scholar
[Drăguţ, L., Tiede, D., Levick, S. R., 2010: ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24:859-871.10.1080/13658810903174803]Search in Google Scholar
[Drăguţ, L., Csillik, O., Eisank, C., Tiede, D., 2014: Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88:119-127.10.1016/j.isprsjprs.2013.11.018399045524748723]Search in Google Scholar
[Dou, W., Ren, Y., Wu, Q., Ruan, S., Chen, Y., Bloyet, D., Constans, J-M., 2007: Fuzzy kappa for the agreement measure of fuzzy classifications, Neurocomputing, 70:726-734.10.1016/j.neucom.2006.10.007]Search in Google Scholar
[Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T. et al., 2016: Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186:64-87.10.1016/j.rse.2016.08.013]Search in Google Scholar
[Halvoň, Ľ., 2011: Posúdenie presnosti vyhodnotenia leteckých meračských snímok metódami digitálnej fotogrametrie pri lesníckom mapovaní. In: Racionalizácia lesníckeho mapovania - zborník referátov, Zvolen, Technická univerzita vo Zvolene, p. 14-22.]Search in Google Scholar
[Heinzel, J., Koch, B., 2012: Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. International Journal of Applied Earth Observation and Geoinformation, 18:101-110.10.1016/j.jag.2012.01.025]Search in Google Scholar
[Holmgren, J., Persson, Å., Söderman, U., 2008: Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images. International Journal of Remote Sensing, 29:1537-1552.10.1080/01431160701736471]Search in Google Scholar
[Kardoš, M., Medveďová, A., Supek, Š., Škodová, M., 2013: Object-oriented classification of tree species in digital aerial photos of landslide area. Zprávy lesnického výzkumu, 58:195-205.]Search in Google Scholar
[Kim, M., Madden., M., 2006: Determination of optimal scale parameter for alliance-level forest classification of multispectral IKONOS image. Proc. of First International Conference on Object-based Image Analysis (OBIA 2006), Salzburg, XXXVI-4/C42.]Search in Google Scholar
[Kressler, F., Steinnocher, K., 2008: Object-orientedanalysis of image and LiDAR data and its potential for dasymetric mapping applications. In: Blaschke, T., Lang, S., Hay, G. J. (eds.): Object Based Image Analysis. Springer, Heidelberg, Berlin, p. 611-624.10.1007/978-3-540-77058-9_33]Search in Google Scholar
[Korpela, I., Tuomola, T., Välimäki, E., 2007: Mapping forest plots: An efficient method combining photogrammetry and field triangulation. Silva Fennica, 41:457-469.10.14214/sf.283]Search in Google Scholar
[Landis, J. R., Koch, G. G., 1977: The measurement of observer agreement for categorical data. Biometrics, 33:159-174.10.2307/2529310]Search in Google Scholar
[Leppänen, V. J., Tokola, T., Maltamo, M., Mehtätalo, L., Pusa, T., Mustonen, J., 2008: Automatic delineation of forest stands from LiDAR data. Presented at GEOgraphic Object-Based Image Analysis for the 21st Century, Calgary, 6 p.]Search in Google Scholar
[Majlingová, A., 2007: Digitálna obrazová analýza dát DPZ s vysokým priestorovým rozlíšením a jej využitie v lesníctve. In: Sympozium GIS Ostrava. Ostrava, VŠB TU, 16 p.]Search in Google Scholar
[Machala, M., Zejdová, L., 2014: Forest mapping through Object-based image analysis of multispectral and LiDAR Aerial data. European Journal of Remote Sensing, 47:117-131.10.5721/EuJRS20144708]Search in Google Scholar
[Maltamo, M., Packalen, P., 2014: Species-specific management inventory in Finland. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds.): Forestry Applications of Air-borne Laser Scanning: Concepts and Case Studies. Springer, Dordrecht, p. 241-252.10.1007/978-94-017-8663-8_12]Search in Google Scholar
[Myint, S. W., Stow, D., 2011: An Object-Oriented Pattern Recognition Approach for Urban Classification. In: Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment, p. 129-140.10.1002/9780470979563.ch9]Search in Google Scholar
[Ørka, H. O., Wulder, M. A., Gobakken, T., Næsset, E., 2012: Subalpine zone delineation using LiDAR and Landsat imagery. Remote Sensing of Environment, 119:11-20.10.1016/j.rse.2011.11.023]Search in Google Scholar
[Pascual, C., García-Abril, A., García-Montero, L. G., Martín-Fernández, S., Cohen, W. B., 2008: Objectbased semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands. Forest Ecology and Management, 255:3677-3685.10.1016/j.foreco.2008.02.055]Search in Google Scholar
[Petr, M., Smith, M., Suaréz, J. C., 2010: Object-based approach for mapping complex forest structure phases using LiDAR data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 6 p.]Search in Google Scholar
[Pippuri, I., Suvanto, A., Maltamo, M., Korhonen, K.T., Pitkänen, J., Packalen, P., 2016: Classification of forest land attributes using multi-source remotely sensed data, International Journal of Applied Earth Observation and Geoinformation, 44:11-22.10.1016/j.jag.2015.07.002]Search in Google Scholar
[Rittl, T., Cooper, M., Heck, R. J., Ballester, M. V. R., 2013: Object-Based method outperforms per-pixel method for land cover classification in a protected area of the brazilian atlantic rainforest region. Pedosphere, 23:290-297.10.1016/S1002-0160(13)60018-1]Search in Google Scholar
[Sasaki, T., Imanishi, J., Ioki, K., Morimoto, Y., Kitada, K., 2012: Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data. Landscape and Ecological Engineering, 8:157-171.10.1007/s11355-011-0158-z]Search in Google Scholar
[Tiede, D., Lang, S., Hoffmann, Ch., 2006: Supervised and forest type -specific multi-scale segmentation for a one-level-representation of single trees. In: Lang S. et al. (eds.): Bridging remote sensing. 1st international conference on object-based image analysis (OBIA 2006). Salzburg, 5 p.]Search in Google Scholar
[Tomljenovic, I., Tiede, D., Blaschke, T., 2016: A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm, International Journal of Applied Earth Observation and Geoinformation, 52:137-148.10.1016/j.jag.2016.06.007]Search in Google Scholar
[Wack, R., Schardt, M., Barrucho, L., Lohr, U., Oliveira, T., 2003: Forest inventory for eucalyptus plantations based on airborne laserscanner data. WG III/3 Workshop „3-D reconstruction from airborne laserscanner and InSAR data“. Dresden, 7 p.]Search in Google Scholar
[Wang, Z., Boesch, R., 2007: Color- and texture-based image segmentation for improved forest delineation. IEEE Transactions on Geoscience and Remote Sensing, 45:3055-3062.10.1109/TGRS.2007.896283]Search in Google Scholar
[Wang, Z., Boesch, R., Ginzler, C., 2012: Forest delineation of aerial images with Gabor wavelets. International Journal of Remote Sensing, 33:2196-2213.10.1080/01431161.2011.608087]Search in Google Scholar
[Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006: Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72:799-811.10.14358/PERS.72.7.799]Search in Google Scholar
[Zhang, C., Xie, Z., Selch, D., 2013: Fusing lidar and digital aerial photography for object-based forest mapping in the Florida Everglades, GIScience & Remote Sensing, 50:562-573.10.1080/15481603.2013.836807]Search in Google Scholar
