Information on stand characteristics is of great importance for forest inventory, management and conservation. For more than two decades, Airborne Laser Scanning (ALS) data have enabled remotely sensed estimation of forest stand attributes. Two main approaches are used to estimate biometric forest attributes using Airborne Laser Scanning (ALS): the area−based approach (ABA) and individual tree detection (ITD). So far, the ABA method has been much more commonly used in forestry, as it requires only point cloud metrics. However, with the requirement for precise height information and the development of ITD methods, it is increasingly used to estimate tree biometric characteristics and stand attributes. With this in mind, this study assessed the impact of an ITD correction method based on the Canopy Height Model (CHM) on the estimation of forest characteristics such as tree density and average tree height. The three−step correction method first classifies erroneous segments from ITD methods, which are then refined. In this study, two ITD methods were tested and their results subsequently corrected on a diverse forest area within Białowieża Forest in Poland. In general, more accurate estimates of stand attributes were obtained using the Local ITD method developed in this study area, while correction procedure produced greater improvement using the basic ITD method, which is a marker−controlled watershed with a kernel size of five pixels (MCWS 5×5). Both ITD methods were reliable for estimating tree density for deciduous trees. The correction worked most reliably for estimating tree density with both methods for the area consisted of deciduous trees, while it was most reliable for estimating average tree height with the Local method for the deciduous trees and with the MCWS 5×5 for the conifers. The results indicate that correction improved ITD estimates of stand characteristics, but this varied with species groups, tree height and amount of height variation. Therefore, further development of ITD methods is advisable, as estimating stand attributes using ALS at the individual tree level offers possibilities for improved forest management.

Apostol, B., Lorenţ, A., Petrila, M., Gancz, V., Badea, O., 2016. Height extraction and stand volume estimation based on fusion airborne LiDAR data and terrestrial measurements for a Norway spruce [Picea abies (L.) Karst.] test site in Romania. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 44 (1): 313-323. DOI: https://doi.org/10.15835/nbha44110155.
Ba, A., Laslier, M., Dufour, S., Hubert-Moy, L., 2020. Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France. International Journal of Remote Sensing, 41 (5): 1645-1667. DOI: https://doi.org/10.1080/01431161.2019.1674457.
Błaszczak-Bąk, W., Janicka, J., Kozakiewicz, T., Chudzikiewicz, K., Bąk, G., 2022. Methodology of Calculating the Number of Trees Based on ALS Data for Forestry Applications for the Area of Samławki Forest District. Remote Sensing, 14 (1). DOI: https://doi.org/10.3390/rs14010016.
Coomes, D.A., Dalponte, M., Jucker, T., Asner, G.P., Banin, L.F., Burslem, D.F.R.P., Lewis, S.L., Nilus, R., Phillips, O.L., Phua, M.H., Qie, L., 2017. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sensing of Environment, 194: 77-88. DOI: https://doi.org/10.1016/j.rse.2017.03.017.
Faliński, J.B., 1986. Vegetation Dynamics in Temperate Lowland Primeval Forests: Ecological Studies in Białowieża Forest. Dordrecht: Springer Netherlands, 555 pp.
Gupta, S., Weinacker, H., Stereńczak, K., Koch, B., 2013. Single Tree Delineation Using Airborne Lidar Data. European Scientific Journal, 9 (32): 405-435.
Hugershoff, R., 1930. Photogrammetrie und Luftbildwesen. Wien: Julius Springer, 266 pp. DOI: https://doi.org/10.1007/978-3-7091-5367-3.
Huo, L., Lindberg, E., Holmgren, J., 2022. Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD). Remote Sensing of Environment, 270: 112857. DOI: https://doi.org/10.1016/j.rse.2021.112857.
Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., Maltamo, M., 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29 (5): 1339-1366. DOI: https://doi.org/10.1080/01431160701736489.
Hyyppä, J., Inkinen, M., 1999. Detecting and estimating attribute for single trees using laser scanner. The Photogrammetric Journal of Finland, 16 (2): 27-42.
Jung, S.-E., Kwak, D.-A., Park, T., Lee, W.-K., Yoo, S., 2011. Estimating Crown Variables of Individual Trees Using Airborne and Terrestrial Laser Scanners. Remote Sensing, 3 (11): 2346-2363. DOI: https://doi.org/10.3390/rs3112346.
Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., Holopainen, M., Heipke, C., Hirschmugl, M., Morsdorf, F., Nćsset, E., Pitkänen, J., Popescu, S., Solberg, S., Wolf, B.M., Wu, J.C., 2012. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing, 4 (4): 950-974. DOI: https://doi.org/10.3390/rs4040950.
Kamińska, A., Lisiewicz, M., Stereńczak, K., Kraszewski, B., Sadkowski, R., 2018. Species-related single dead tree detection using multi-temporal ALS data and CIR imagery. Remote Sensing of Environment, 219: 31-43. DOI: https://doi.org/10.1016/j.rse.2018.10.005.
Kankare, V., Räty, M., Yu, X., Holopainen, M., Vastaranta, M., Kantola, T., Hyyppä, J., Hyyppä, H., Alho, P., Viitala, R., 2013. Single tree biomass modelling using airborne laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 85: 66-73. DOI: https://doi.org/10.1016/j.isprsjprs.2013.08.008.
Larsen, M., Eriksson, M., Descombes, X., Perrin, G., Brandtberg, T., Gougeon, F.A., 2011. Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. International Journal of Remote Sensing, 32 (20): 5827-5852. DOI: https://doi.org/10.1080/01431161.2010.507790.
Lindberg, E., Holmgren, J., 2017. Individual Tree Crown Methods for 3D Data from Remote Sensing. Current Forestry Reports, 3 (1): 19-31. DOI: https://doi.org/10.1007/s40725-017-0051-6.
Lisiewicz, M., Kamińska, A., Kraszewski, B., Stereńczak, K., 2022a. Correcting the Results of CHM-Based Individual Tree Detection Algorithms to Improve Their Accuracy and Reliability. Remote Sensing, 14 (8): 1822. DOI: https://doi.org/10.3390/rs14081822.
Lisiewicz, M., Kamińska, A., Stereńczak, K., 2022b. Recognition of specified errors of Individual Tree Detection methods based on Canopy Height Model. Remote Sensing Applications: Society and Environment, 25: 100690. DOI: https://doi.org/10.1016/j.rsase.2021.100690.
Meyer, F., Beucher, S., 1990. Morphological segmentation. Journal of Visual Communication and Image Representation, 1 (1): 21-46. DOI: https://doi.org/10.1016/1047-3203(90)90014-M.
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. DOI: https://doi.org/10.1016/J.JAG.2018.05.002.
Miścicki, S., Stereńczak, K., 2012. Wykorzystanie cech określonych na podstawie wysokościowego modelu koron w dwu-fazowej metodzie inwentaryzacji zapasu drzewostanu (in Polish). Roczniki Geomatyki, 10, 5 (55): 47-54.
Miścicki, S., Stereńczak, K., 2013. A two-phase inventory method for calculating standing volume and tree-density of forest stands in central Poland based on airborne laser-scanning data. Forest Research Papers, 74 (2): 127-136. DOI: https://doi.org/10.2478/frp-2013-0013.
Nćsset, E., 1997. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 52 (2): 49-56. DOI: https://doi.org/10.1016/S0924-2716(97)83000-6.
Nćsset, E., 2014. Area-based inventory in Norway – from innovations to operational reality. In: M. Maltamo, E. Nćsset, J. Vauhkonen, eds. Forestry Applications of Airborne Laser Scanning, Concepts and Case Studies. New York: Springer, pp. 214-240. DOI: https://doi.org/10.1007/978-94-017-8663-8.
Nilsson, M., 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sensing of Environment, 56 (1): 1-7. DOI: https://doi.org/10.1016/0034-4257(95)00224-3.
Packalén, P., Pitkänen, J., Maltamo, M., 2008. Comparison of individual tree detection and canopy height distribution approaches: a case study in Finland. Proceedings of the SilviLaser 2008, 8th International Conference on LiDAR Applications in Forest Assessment and Inventory, 17-19 September 2008, Edinburgh, UK, 22-29.
Parkitna, K., Krok, G., Miścicki, S., Ukalski, K., Lisańczuk, M., Mitelsztedt, K., Magnussen, S., Markiewicz, A., Stereńczak, K., 2021. Modelling growing stock volume of forest stands with various ALS area-based approaches. Forestry: An International Journal of Forest Research, 94 (5): 630-650. DOI: https://doi.org/10.1093/forestry/cpab011.
Persson, Ĺ., Holmgren, J., Söderman, U., 2002. Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing, 68 (9): 925-932.
Peuhkurinen, J., Mehtätalo, L., Maltamo, M., 2011. Comparing individual tree detection and the areabased statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands. Canadian Journal of Forest Research, 41 (3): 583-598. DOI: https://doi.org/10.1139/X10-223.
Popescu, S.C., Wynne, R.H., 2004. Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height. Photogrammetric Engineering and Remote Sensing, 70 (5): 589-604. DOI: https://doi.org/10.14358/pers.70.5.589.
Simula, J., Holopainen, M., Imangholiloo, M., 2022. Utilizing Single Photon Laser Scanning Data For Estimating Individual Tree Attributes. Proceedings of the XXIV ISPRS Congress, 6-11 June 2022, Nice, France, 431-438.
Socha, J., Hawryło, P., Pierzchalski, M., Stereńczak, K., Krok, G., Wężyk, P., Tymińska-Czabańska, L., 2019. An allometric area-based approach-a cost-effective method for stand volume estimation based on ALS and NFI data. Forestry: An International Journal of Forest Research, 93 (3): 344-358. DOI: https://doi.org/10.1093/forestry/cpz062.
Soille, P., 1999. Morphological Image Analysis: Principles and Applications. Berlin: Springer-Verlag, 392 pp. DOI: https://doi.org/10.1007/978-3-662-03939-7.
Stereńczak, K., 2010. Technologia lotniczego skanowania laserowego jako źródło danych w półautomatycznej inwentaryzacji lasu (in Polish). Sylwan, 154 (2): 88-99. DOI: https://doi.org/10.26202/sylwan.2009041.
Stereńczak, K., 2013. Factors influencing individual tree crowns detection based on airborne laser scanning data. Forest Research Papers, 74 (4): 323-333. DOI: https://doi.org/10.2478/frp-2013-0031.
Stereńczak, K., Będkowski, K., Weinacker, H., 2008. Accuracy of crown segmentation and estimation of selected trees and forest stand parameters in order to resolution of used DSM and NDSM models generated from dense small footprint LIDAR data. Proceedings of the ISPRS Congress, 3-11 July 2008, Beijing, China, 27-32.
Stereńczak, K., Kraszewski, B., Mielcarek, M., Kamińska, A., Lisiewicz, M., Modzelewska, A., Sadkowski, R., Białczak, M., Piasecka, Ż., Wilkowska, R., 2020a. Monitorowanie Puszczy Białowieskiej z wykorzystaniem danych teledetekcyjnych (in Polish). In: K. Stereńczak, ed. Zimowa Szkoła Leśna XI Sesja – Zastosowanie geoinformatyki w leśnictwie. Sękocin Stary: Instytut Badawczy Leśnictwa, pp. 99-108.
Stereńczak, K., Kraszewski, B., Mielcarek, M., Piasecka, Ż., Lisiewicz, M., Heurich, M., 2020b. Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm. International Journal of Applied Earth Observation and Geoinformation, 93: 102191. DOI: https://doi.org/10.1016/j.jag.2020.102191.
Straub, C., Koch, B., 2011. Estimating single tree stem volume of Pinus sylvestris using airborne laser scanner and multispectral line scanner data. Remote Sensing, 3 (5): 929-944. DOI: https://doi.org/10.3390/rs3050929.
Vastaranta, M., Holopainen, M., Yu, X., Haapanen, R., Melkas, T., Hyyppä, J., Hyyppä, H., 2011a. Individual tree detection and area-based approach in retrieval of forest inventory characteristics from low-pulse airborne laser scanning data. Photogrammetric Journal of Finland, 22 (2): 1-13.
Vastaranta, M., Holopainen, M., Yu, X., Hyyppä, J., Mäkinen, A., Rasinmäki, J., Melkas, T., Kaartinen, H., Hyyppä, H., 2011b. Effects of individual tree detection error sources on forest management planning calculations. Remote Sensing, 3 (8): 1614-1626. DOI: https://doi.org/10.3390/rs3081614.
Vastaranta, M., Wulder, M.A., White, J.C., Pekkarinen, A., Tuominen, S., Ginzler, C., Kankare, V., Holopainen, M., Hyyppä, J., Hyyppä, H., 2013. Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update. Canadian Journal of Remote Sensing, 39 (5): 382-395. DOI: https://doi.org/10.5589/m13-046.
Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkänen, J., Solberg, S., Wang, Y., Weinacker, H., Hauglin, K.M., Lien, V., Packalén, P., Gobakken, T., Koch, B., Nćsset, E., Tokola, T., Maltamo, M., 2012. Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85 (1): 27-40. DOI: https://doi.org/10.1093/forestry/cpr051.
Wang, Y., Weinacker, H., Koch, B., 2008. A Lidar point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest. Sensors, 8 (6): 3938-3951. DOI: https://doi.org/10.3390/s8063938.
Wang, Y., Lehtomäki, M., Liang, X., Pyörälä, J., Kukko, A., Jaakkola, A., Liu, J., Feng, Z., Chen, R., Hyyppä, J., 2019. Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest. ISPRS Journal of Photogrammetry and Remote Sensing, 147: 132-145. DOI: https://doi.org/10.1016/j.isprsjprs.2018.11.008.
Wästlund, A., Holmgren, J., Lindberg, E., Olsson, H., 2018. Forest variable estimation using a high altitude single photon lidar system. Remote Sensing, 10 (9). DOI: https://doi.org/10.3390/rs10091422.
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 (10): 3704-3732. DOI: https://doi.org/10.3390/f6103704.
White, J.C., Tompalski, P., Vastaranta, M., Wulder, M.A., Saarinen, N., Stepper, C., Coops, N.C., 2017. A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach [online]. Available from: https://cfs.nrcan.gc.ca/publications?id=38945 [accessed: 27.05.2022].
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 (6): 1481-1495. DOI: https://doi.org/10.3390/rs2061481.
Yu, X., Kukko, A., Kaartinen, H., Wang, Y., Liang, X., Matikainen, L., Hyyppä, J., 2020. Comparing features of single and multi-photon lidar in boreal forests. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 268-276. DOI: https://doi.org/10.1016/j.isprsjprs.2020.08.013.