{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:46:44Z","timestamp":1771476404032,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"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>Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km \u00d7 4 km. The classification model\u2019s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (\u03ba) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a \u03ba = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.<\/jats:p>","DOI":"10.3390\/rs14215345","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"5345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest"],"prefix":"10.3390","volume":"14","author":[{"given":"Nenad","family":"Brodi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2574-6445","authenticated-orcid":false,"given":"\u017deljko","family":"Cvijetinovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3256-6669","authenticated-orcid":false,"given":"Milutin","family":"Milenkovi\u0107","sequence":"additional","affiliation":[{"name":"International Institute for Applied Systems Analysis (IIASA), Novel Data Ecosystems for Sustainability (NODES), Schlossplatz 1, A-2361 Laxenburg, Austria"},{"name":"Laboratory of Geo-Information Science and Remote Sensing, Department of Environmental Sciences, Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9980-5797","authenticated-orcid":false,"given":"Jovan","family":"Kova\u010devi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-3063","authenticated-orcid":false,"given":"Nikola","family":"Stan\u010di\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia"}]},{"given":"Momir","family":"Mitrovi\u0107","sequence":"additional","affiliation":[{"name":"MapSoft d.o.o, Ustani\u010dka 64\/7, 11000 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6122-3158","authenticated-orcid":false,"given":"Dragan","family":"Mihajlovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5589\/m13-051","article-title":"Status and Prospects for LiDAR Remote Sensing of Forested Ecosystems","volume":"39","author":"Wulder","year":"2013","journal-title":"Can. 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