References
- Ali-Sisto, D., Packalen, P., 2017: Forest Change Detection by Using Point Clouds from Dense Image Matching Together with a LiDAR-Derived Terrain Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10:1197–1206.
- Antonelli, P. L., 1992: The Algorithmic Beauty of Plants (Przemyslaw Prusinkiewicz and Aristid Linden-mayer). SIAM Review, 34:142–143.
- Bollandsås, O. M., Gregoire, T. G., Næsset, E., Øyen, B. H., 2013: Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Statistical Methods and Applications, 22:113–129.
- Bollandsås, O. M., Ørka, H. O., Dalponte, M., Gobakken, T., Næsset, E., 2019: Modelling site index in forest stands using airborne hyperspectral imagery and Bi-temporal laser scanner data. Remote Sensing, 11:1020.
- Bruna, V., Elznicova, J., Pacina, J., 2012: Využití geoinformačních technologií pro hodnocení krajiny přeshraniční oblasti Česko-Saské Švýcarsko. Ústí nad Labem, Univerzita J. E. Purkyně v Ústí nad Labem, Fakulta životního prostředí, 104 p. (In Czech).
- Černý, M., Pařez, J., Malík, Z., 1993: Růstové modely hlavních dřevin České republiky (smrk, borovice, buk, dub) – 2. etapa. Zpráva o výsledcích řešení za rok 1993. Skupina ekologického monitoring, PYRUS, 66 p. (In Czech).
- Cieszewski, C. J., Harrison, M., Martin, S. W., 2000: Practical methods for estimating non-biased parameters in self-referencing growth and yield models. PMRC Technical report. Georgia, University of Georgia. 11 p.
- Cieszewski, C. J., 2001: Three methods of deriving advanced dynamic site equations demonstrated on inland Douglas-fir site curves. Canadian Journal of Forest Research, 31:165–173.
- Cieszewski, C. J., Strub, M., 2018: Comparing properties of self-referencing models based on nonlinear-fixed-effects versus nonlinear-mixed-effects modeling approaches. Mathematical and Computational Forestry and Natural-Resource Sciences, 10:46–57.
- Crespo-Peremarch, P., Fournier, R. A., Nguyen, V. T., van Lier, O. R., Ruiz, L. Á., 2020: A comparative assessment of the vertical distribution of forest components using full-waveform airborne, discrete airborne and discrete terrestrial laser scanning data. Forest Ecology and Management, 473:118268.
- Fassnacht, F. E., White, J. C., Wulder, M. A., Næsset, E., 2023: Remote sensing in forestry: current challenges, considerations and directions. Forestry: An International Journal of Forest Research, 97:11–37.
- Goodbody, T. R. H., Coops, N. C., Luther, J. E., Tompalski, P., Mulverhill, C., Frizzle, C. et al., 2021: Airborne laser scanning for quantifying criteria and indicators of sustainable forest management in Canada. Canadian Journal of Forest Research, 51:972–985.
- Guerra-Hernández, J., Arellano-Pérez, S., González-Ferreiro, E., Pascual, A., Sandoval Altelarrea, V., Ruiz-González, A. D. et al., 2021: Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data. Forest Ecology and Management, 481:118690.
- Hüttnerová, T., Muscarella, R., Surový, P., 2024: Drone microrelief analysis to predict the presence of naturally regenerated seedlings. Frontiers in Forests and Global Change, 6:1329675.
- Kurth, W., Anzola Jürgenson, G., 1997: Triebwachstum und Verzweigung junger Fichten in Abhängigkeit von den beiden Einflußgrößen “Beschattung” und “Wuchsdichte”: Datenaufbereitung und -analyse mit GROGRA. In: Pelz, D. (ed.): Deutscher Verband Forstlicher Forschungsanstalten, Sektion Forstl. Biometrie u. Informatik, 10. Tagung Freiburg i. Br. 24.–26. 9. 1997, Ljubljana, Biotechn. Fakultät, p. 89–108. (In German).
- Kuželka, K., Marušák, R., 2015: KORFit: An efficient growth function fitting tool. Computers and Electronics in Agriculture, 116:187–190.
- Ma, Q., Su, Y., Tao, S., Guo, Q., 2018: Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California. International Journal of Digital Earth, 11:485–503.
- Mauya, E. W., Hansen, E. H., Gobakken, T., Bollandsås, O. M., Malimbwi, R. E., Næsset, E., 2015: Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance and Management, 10:10.
- Melichová, Z., Pekár, S., Surový, P., 2023: Benchmark for automatic clear-cut morphology detection methods derived from airborne LiDAR data. Forests, 14:2408.
- Moan, M. Å., Noordermeer, L., White, J. C., Coops, N. C., Bollandsås, O. M., 2023: Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data. Forestry: An International Journal of Forest Research, 97:48–58.
- Muhamad-Afizzul, M., Siti-Yasmin, Y., Hamdan, O., Tan, S. A., 2019: Estimating stand-level structural and biophysical variables of lowland dipterocarp forest using airborne LiDAR data. Journal of Tropical Forest Science, 31:312–323.
- Næsset, E., Gobakken, T., 2005: Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment, 96:453–465.
- Næsset, E., Gobakken, T., Solberg, S., Gregoire, T. G., Nelson, R., Ståhl, G. et al., 2011: Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area. Remote Sensing of Environment, 115:3599–3614.
- Nigul, K., Padari, A., Kiviste, A., Noe, S. M., Korjus, H., Laarmann, D. et al., 2021: The possibility of using the Chapman-Richards and Näslund functions to model height-diameter relationships in hemiboreal old-growth forest in Estonia. Forests, 12:1–15.
- Noordermeer, L., Bollandsås, O. M., Gobakken, T., Næsset, E., 2018: Direct and indirect site index determination for Norway spruce and Scots pine using bitemporal airborne laser scanner data. Forest Ecology and Management, 428:104–114.
- Noordermeer, L., Økseter, R., Ørka, H. O., Gobakken, T., Næsset, E., Bollandsås, O. M., 2019: Classifications of forest change by using bitemporal airborne laser scanner data. Remote Sensing, 11:2145.
- Noordermeer, L., Gobakken, T., Næsset, E., Bollandsås, O. M., 2020: Predicting and mapping site index in operational forest inventories using bitemporal air-borne laser scanner data. Forest Ecology and Management, 457:117768.
- Noordermeer, L., Gobakken, T., Næsset, E., Bollandsås, O. M., 2021: Economic utility of 3D remote sensing data for estimation of site index in Nordic commercial forest inventories: a comparison of airborne laser scanning, digital aerial photogrammetry and conventional practices. Scandinavian Journal of Forest Research, 36:55–67.
- Patočka, Z., Mikita, T., 2016: Využití plošného přístupu ke zpracování dat leteckého laserového skenování v inventarizaci lesa. Zprávy lesnického výzkumu, 61:115–124. (In Czech).
- Richards, F. J., 1959: A flexible growth function for empirical use. Journal of Experimental Botany, 10:290–301.
- Silva, C. A., Klauberg, C., De Pádua Chaves Carvalho, S., Rodriguez, L. C. E., 2013: Estimation of aboveground carbon stocks in Eucalyptus plantations using LIDAR. International Geoscience and Remote Sensing Symposium (IGARSS), 21–26 July 2013, Melbourne, VIC, Australia, p. 972–974.
- Socha, J., Pierzchalski, M., Bałazy, R., Ciesielski, M., 2017: Modelling top height growth and site index using repeated laser scanning data. Forest Ecology and Management, 406:307–317.
- Socha, J., Hawryło, P., Stereńczak, K., Miścicki, S., Tymińska-Czabańska, L., Młocek, W. et al., 2020: Assessing the sensitivity of site index models developed using bi-temporal airborne laser scanning data to different top height estimates and grid cell sizes. International Journal of Applied Earth Observation and Geoinformation, 91:102129.
- Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., 2014: Simulating the impacts of error in species and height upon tree volume derived from airborne laser scanning data. Forest Ecology and Management, 327:167–177.
- Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., Pickell, P. D., 2015a: Estimating forest site productivity using airborne laser scanning data and Landsat time series. Canadian Journal of Remote Sensing, 41:232–245.
- Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., 2015b: Augmenting site index estimation with airborne laser scanning data. Forest Science, 61:861–873.
- Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., 2015c: Enriching ALS-derived area-based estimates of volume through tree-level downscaling. Forests, 6:2608–2630.
- Tompalski, P., Coops, N. C., White, J. C., Wulder, M. A., 2016: Enhancing forest growth and yield predictions with airborne laser scanning data: Increasing spatial detail and optimizing yield curve selection through template matching. Forests, 7:1–20.
- Tompalski, P., Coops, N. C., Marshall, P. L., White, J. C., Wulder, M. A., Bailey, T., 2018: Combining multi-date airborne laser scanning and digital aerial photogrammetric data for forest growth and yield modelling. Remote Sensing, 10:1–21.
- Vauhkonen, J., Ørka, H. O., Holmgren, J., Dalponte, M., Heinzel, J., Koch, B., 2014: Tree species recognition based on airborne laser scanning and complementary data source. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds.): Forestry applications of airborne laser scanning. Managing Forest Ecosystems. Springer, Dordrecht., p. 135–156.
- Watt, M. S., Dash, J. P., Bhandari, S., Watt, P., 2015: Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning. Forest Ecology and Management, 357:1–9.
- White, J. C., Stepper, C., Tompalski, P., Coops, N. C., Wulder, M. A., 2015: Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests, 6:3704–3732.
- Woo, H., Eskelson, B. N. I., Monleon, V. J., 2020: Tree height increment models for national forest inventory data in the Pacific Northwest, USA. Forests, 11:2. Yu, X., Hyyppä, J., Kaartinen, H., Hyyppä, H., Maltamo, M., Rönnholm, P., 2005: Measuring the growth of individual trees using multi-temporal airborne laser scanning point clouds. ISPRS WG III/3, III/4, V/3 Workshop “Laser scanning 2005”, 12–14 September 2005, Enschede, the Netherlands, p. 204–208.
- Yu, X., Hyyppä, J., Holopainen, M., Vastaranta, M., 2010: Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sensing, 2:1481–1495.
- Zhao-Gang, L., Feng-Ri, L., 2003: The generalized Chapman-Richards function and applications to tree and stand growth. Journal of Forestry Research, 14:19–26.
- Change detection in ArcGIS Pro. Available at https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/change-detection-in-arcgis-pro.htm.
- Minus (Spatial Analyst). Available at https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/minus.htm.
- PDAL. (2022a). Available at https://pdal.io/en/2.4.3/workshop/exercises/analysis/ground/ground.html.
- PDAL. (2022b). Available at https://pdal.io/en/2.4.3/workshop/exercises/analysis/rasterize/rasterize.html.
- PDAL. (2022c). Available at https://pdal.io/en/2.4.3/workshop/exercises/analysis/dtm/dtm.html.
- R Core Team, 2023. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023; Available at https://www.R-project.org/ (accessed on 7 December 2023).
- Zonal Statistics as Table (Spatial Analyst). Available at https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/zonal-statistics-as-table.htm.