{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:50:50Z","timestamp":1764784250360,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,16]],"date-time":"2019-06-16T00:00:00Z","timestamp":1560643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%\u201364%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon\u2019s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p &lt; 0.05).<\/jats:p>","DOI":"10.3390\/rs11121431","type":"journal-article","created":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T03:24:41Z","timestamp":1560741881000},"page":"1431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8379-5635","authenticated-orcid":false,"given":"Ivan","family":"Sa\u010dkov","sequence":"first","affiliation":[{"name":"Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 \u00c5s, Norway"},{"name":"Department of Forest Policy, Economics and Forest Management, National Forest Centre-Forest Research Institute Zvolen, T.G. Masaryka 22, 960 01 Zvolen, Slovak Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0968-2995","authenticated-orcid":false,"given":"Ladislav","family":"Kulla","sequence":"additional","affiliation":[{"name":"Department of Forest Policy, Economics and Forest Management, National Forest Centre-Forest Research Institute Zvolen, T.G. Masaryka 22, 960 01 Zvolen, Slovak Republic"}]},{"given":"Tom\u00e1\u0161","family":"Bucha","sequence":"additional","affiliation":[{"name":"Department of Forest Policy, Economics and Forest Management, National Forest Centre-Forest Research Institute Zvolen, T.G. Masaryka 22, 960 01 Zvolen, Slovak Republic"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.12.039","article-title":"On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters","volume":"175","author":"Renaud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cao, L., and She, G. (2017). Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests. Remote Sens., 9.","DOI":"10.3390\/rs9090940"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Maltamo, M., N\u00e6sset, E., and Vauhkonen, J. (2014). Introduction to forestry applications of airborne laser scanning. 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