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Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies.<\/jats:p>","DOI":"10.3390\/rs15184407","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:09:50Z","timestamp":1694081390000},"page":"4407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison"],"prefix":"10.3390","volume":"15","author":[{"given":"Michelle S.","family":"Bester","sequence":"first","affiliation":[{"name":"Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4412-5599","authenticated-orcid":false,"given":"Aaron E.","family":"Maxwell","sequence":"additional","affiliation":[{"name":"Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA"}]},{"given":"Isaac","family":"Nealey","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0175-558X","authenticated-orcid":false,"given":"Michael R.","family":"Gallagher","sequence":"additional","affiliation":[{"name":"USDA Forest Service, Northern Research Station, New Lisbon, NJ 08064, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-5614","authenticated-orcid":false,"given":"Nicholas S.","family":"Skowronski","sequence":"additional","affiliation":[{"name":"USDA Forest Service, Northern Research Station, 180 Canfield Street, Morgantown, WV 26505, USA"}]},{"given":"Brenden E.","family":"McNeil","sequence":"additional","affiliation":[{"name":"Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s40725-016-0038-8","article-title":"Conservation and Monitoring of Tree Genetic Resources in Temperate Forests","volume":"2","author":"Aravanopoulos","year":"2016","journal-title":"Curr. 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