[Baty, F., Ritz, Ch., Charles, S., Brutsche, M., Flandrois, J. P., Delignette-Muller, M., 2015: A Toolbox for Nonlinear Regression in R Z The Package nlstools. Journal of Statistical Software, 66:1–21.10.18637/jss.v066.i05]Search in Google Scholar
[Cosenza, D., Soares, V., Leite, H., Gleriani, J., Amaral, C., Junior, J. et al., 2018: Airborne laser scanning applied to eucalyptus stand inventory at individual tree level. Pesquisa Agropecuaria Brasileira, 53:1373–1382.10.1590/s0100-204x2018001200010]Search in Google Scholar
[Deluzet, M., Erudel, T., Briottet, X., Sheeren, D., Fabre, S., 2022: Individual Tree Crown Delineation Method Based on Multi-Criteria Graph Using Geometric and Spectral Information: Application to Several Temperate Forest Sites. Remote Sensing, 14:1083.10.3390/rs14051083]Search in Google Scholar
[Díaz-Varela, R. A., González-Ferreiro, E., 2021: 3D Point Clouds in Forest Remote Sensing. Remote Sensing, 13:2999.10.3390/rs13152999]Search in Google Scholar
[Fay, M. P., Proschan, M. A., 2010: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Survey, 4:1–39.10.1214/09-SS051285773220414472]Search in Google Scholar
[Ginzler, C., Hobi, M. L., 2015: Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory. Remote Sensing, 7:4343–4370.10.3390/rs70404343]Search in Google Scholar
[Goodbody, T. R. H., Coops, N. C., Luther, J. E., Tompalski, P., Mulverhill, C. F., Fournier, R. et al., 2021: Airborne laser scanning for quantifying criteria and indicators of sustainable forest management in Canada. Canadian Journal of Forest Research, 51:14.10.1139/cjfr-2020-0424]Search in Google Scholar
[Hill, A., Breschan, J., Mandallaz, D., 2014: Accuracy Assessment of Timber Volume Maps Using Forest Inventory Data and LiDAR Canopy Height Models. Forests, 5:2253–2275.10.3390/f5092253]Search in Google Scholar
[Jamru, L. R., 2018: Correction pit free canopy height model derived from LiDAR data for the broad leaf tropical forest. IOP Conference Series: Earth and Environmental Science, 169:012113.10.1088/1755-1315/169/1/012113]Search in Google Scholar
[Jiang, X., Li, G., Lu, D., Chen, E., Wei, X., 2020: Stratification-Based Forest Aboveground Biomass Estimation in a Subtropical Region Using Airborne Lidar Data. Remote Sensing, 12:1101.10.3390/rs12071101]Search in Google Scholar
[Kakoulaki, G., Martinez, A., Florio, P., 2021: Non-commercial Light Detection and Ranging (LiDAR) data in Europe. Luxembourg, Publications Office of the European Union, 35 p.]Search in Google Scholar
[Lamb, S. M., MacLean, D.A., Hennigar, C.R., Pitt, D.G, 2018: Forecasting Forest Inventory Using Imputed Tree Lists for LiDAR Grid Cells and a Tree-List Growth Model. Forests, 9: 167.10.3390/f9040167]Search in Google Scholar
[Lisiewicz, M., Kamińska, A., Kraszewski, B., Stereńczak, K., 2022: Correcting the Results of CHM-Based Individual Tree Detection Algorithms to Improve Their Accuracy and Reliability. Remote Sensing, 14: 1822.10.3390/rs14081822]Search in Google Scholar
[Liu, Q., Fu, L., Chen, Q., Wang, G., Luo, P., Sharma, R. P. et al., 2020: Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry. Remote Sensing, 12:2884.10.3390/rs12182884]Search in Google Scholar
[Madry, S., 2021: Introduction to QGIS: Open Source Geographic Information System. USA, Locate Press LLC, 219 p.]Search in Google Scholar
[Martins-Neto, R. P., Tommaselli, A. M. G., Imai, N. N., David, H. C., Miltiadou, M., Honkavaara, E., 2021: Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest. Remote Sensing, 13:2444.10.3390/rs13132444]Search in Google Scholar
[Mielcarek, M., Stereńczak, K., Khosravipour, A., 2018: Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71:132–143.10.1016/j.jag.2018.05.002]Search in Google Scholar
[Murgaš, V., Sačkov, I., Sedliak, M., Tunák, D., Chudý F., 2018: Assessing horizontal accuracy of inventory plots in forests with different mix of tree species composition and development stage. Journal of Forest Science, 64:478–485.10.17221/92/2018-JFS]Search in Google Scholar
[Novo-Fernández, A., Barrio-Anta, M., Recondo, C., Cámara-Obregón, A., López-Sánchez, C. A., 2019: Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain. Remote Sensing, 11:1693.10.3390/rs11141693]Search in Google Scholar
[Petráš, R., Pajtík, J., 1991: Sústava česko-slovenských objemových tabuliek drevín. Lesnícky časopis, 37:49–56.]Search in Google Scholar
[Polidori, L., El Hage, M., 2020: Digital Elevation Model Quality Assessment Methods – A Critical Review. Remote Sensing, 12:3522.10.3390/rs12213522]Search in Google Scholar
[Sačkov, I., Kulla, L., Bucha, T., 2019a. A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests. Remote Sensing, 11:1431.10.3390/rs11121431]Search in Google Scholar
[Sačkov, I., Scheer, L., Bucha, T., 2019b. Predicting forest stand variables from airborne LiDAR data using a tree detection method in Central European forests. Central European Forestry Journal, 65:191–197.10.2478/forj-2019-0014]Search in Google Scholar
[Surový, P., Kuželka, K., 2019: Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques - A Review. Forests, 10:273.10.3390/f10030273]Search in Google Scholar
[Vauhkonen, J., Maltamo, M., McRoberts, R. E., Næsset, E., 2014: Introduction to forestry applications of airborne laser scanning. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds.): Forestry Application of Airborne Laser Scanning: Concept and Case Studies. Springer Netherlands: Dordrecht, The Netherlands, p. 1–16.10.1007/978-94-017-8663-8_1]Search in Google Scholar
[Versace, S., Gianelle, D., Frizzera, L., Tognetti, R., Garfì, V., Dalponte, M., 2019: Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data. Remote Sensing, 11:2734.10.3390/rs11232734]Search in Google Scholar
[Voght, W. P., Johnson, R. B., 2012: Correlation and Regression Analysis. USA, SAGE Publications Ltd, 1632 p.10.4135/9781446286104]Search in Google Scholar
[Wang, H., Seaborn, T., Wang, Z., Caudill, Ch., Link, T., 2021: Modeling tree canopy height using machine learning over mixed vegetation landscapes. International Journal of Applied Earth Observation and Geoinformation, 101:102353.10.1016/j.jag.2021.102353]Search in Google Scholar
[Zhang, Z., Cao, L., She, G., 2017: Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests. Remote Sensing, 9:940.10.3390/rs9090940]Search in Google Scholar
[Zhang, W., Cai, S., Liang, X., Shao, J., Hu, R., Yu, S. et al., 2020: Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data. Forest Ecosystems, 7:1–13.10.1186/s40663-019-0212-0]Search in Google Scholar
[Zhao, J., Zhao, L., Chen, E., Li, Z., Xu, K., Ding, X., 2022: An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height. Remote Sensing, 14:568.10.3390/rs14030568]Search in Google Scholar
[Zhen, Z., Quackenbush, L. J., Zhang, L., 2016: Trends in Automatic Individual Tree Crown Detection and Delineation – Evolution of LiDAR Data. Remote Sensing, 8:333.10.3390/rs8040333]Search in Google Scholar
[QGIS Development Team, 2022: QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org.]Search in Google Scholar
[R Core Team, 2021: R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org.]Search in Google Scholar