{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:26:47Z","timestamp":1762543607468,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,17]],"date-time":"2022-04-17T00:00:00Z","timestamp":1650153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009612","name":"Oregon State University","doi-asserted-by":"publisher","award":["Department of Forest Engineering, Resources and Management","College of Forestry Research Forests"],"award-info":[{"award-number":["Department of Forest Engineering, Resources and Management","College of Forestry Research Forests"]}],"id":[{"id":"10.13039\/100009612","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["2019-67019-29462"],"award-info":[{"award-number":["2019-67019-29462"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional inventories require large investments of resources and a trained workforce to measure tree sizes and characteristics that affect wood quality and value, such as the presence of defects and damages. Handheld light detection and ranging (LiDAR) and photogrammetric point clouds developed using Structure from Motion (SfM) algorithms achieved promising results in tree detection and dimensional measurements. However, few studies have utilized handheld LiDAR or SfM to assess tree defects or damages. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital models using SfM, and a handheld GeoSLAM Zeb Horizon to create LiDAR point cloud models of some of the main tree species from the Pacific Northwest. We compared measurements of damage count and damage length obtained from handheld LiDAR, SfM photogrammetry, and traditional field methods using linear mixed-effects models. The field method recorded nearly twice as many damages per tree as the handheld LiDAR and SfM methods, but there was no evidence that damage length measurements varied between the three survey methods. Lower damage counts derived from LiDAR and SfM were likely driven by the limited point cloud reconstructions of the upper stems, as usable tree heights were achieved, on average, at 13.6 m for LiDAR and 9.3 m for SfM, even though mean field-measured tree heights was 31.2 m. Our results suggest that handheld LiDAR and SfM approaches show potential for detection and measurement of tree damages, at least on the lower stem.<\/jats:p>","DOI":"10.3390\/rs14081938","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"1938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Estimating Tree Defects with Point Clouds Developed from Active and Passive Sensors"],"prefix":"10.3390","volume":"14","author":[{"given":"Carli J.","family":"Morgan","sequence":"first","affiliation":[{"name":"Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA"}]},{"given":"Matthew","family":"Powers","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9068-9010","authenticated-orcid":false,"given":"Bogdan M.","family":"Strimbu","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR 97331, USA"},{"name":"Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, 500036 Brasov, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,17]]},"reference":[{"key":"ref_1","unstructured":"Natural Resources Conservation Service (2018). Forestry Technical Note No. FOR-1: Forest Inventory Methods."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1093\/sjaf\/32.1.38","article-title":"An improved tree height measurement technique tested on mature southern pines","volume":"32","author":"Bragg","year":"2008","journal-title":"South. J. Appl. For."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"657","DOI":"10.14214\/sf.133","article-title":"Effects of training for inexperienced surveyors on data quality of tree diameter and height measurements","volume":"44","author":"Kitahara","year":"2010","journal-title":"Silva Fenn."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Donager, J.J., Meador, A.J.S., and Blackburn, R.C. (2021). Adjudicating perspectives on forest structure: How do airborne, terrestrial, and mobile lidar-derived estimates compare?. Remote Sens., 13.","DOI":"10.3390\/rs13122297"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gollob, C., Ritter, T., and Nothdurft, A. (2020). Forest inventory with long range and high-speed personal laser scanning (PLS) and simultaneous localization and mapping (SLAM) Technology. Remote Sens., 12.","DOI":"10.3390\/rs12091509"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hyypp\u00e4, E., Yu, X., Kaartinen, H., Hakala, T., Kukko, A., Vastaranta, M., and Hyypp\u00e4, J. (2020). Comparison of backpack, handheld, under-canopy UAV, and above-Canopy UAV laser scanning for field reference data collection in boreal forests. Remote Sens., 12.","DOI":"10.3390\/rs12203327"},{"key":"ref_7","unstructured":"GeoSLAM (2021, October 10). ZEB Horizon. Available online: https:\/\/geoslam.com\/solutions\/zeb-horizon."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fang, R., and Strimbu, B.M. (2017). Stem measurements and taper modeling using photogrammetric point clouds. Remote Sens., 9.","DOI":"10.3390\/rs9070716"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40725-019-00094-3","article-title":"Structure from Motion photogrammetry in forestry: A review","volume":"5","author":"Iglhaut","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6587","DOI":"10.3390\/rs6076587","article-title":"The use of a hand-held camera for individual tree 3D mapping in forest sample plots","volume":"6","author":"Liang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mikita, T., Janata, P., and Surov\u00fd, P. (2016). Forest stand inventory based on combined aerial and terrestrial close-range photogrammetry. Forests, 7.","DOI":"10.3390\/f7080165"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Surov\u00fd, P., Yoshimoto, A., and Panagiotidis, D. (2016). Accuracy of reconstruction of the tree stem surface using terrestrial close-range photogrammetry. Remote Sens., 8.","DOI":"10.3390\/rs8020123"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, J., Feng, Z., Yang, L., Mannan, A., Khan, T., Zhao, Z., and Cheng, Z. (2018). Extraction of sample plot parameters from 3D point cloud reconstruction based on combined RTK and CCD continuous photography. Remote Sens., 10.","DOI":"10.3390\/rs10081299"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mokro\u0161, M., Liang, X., Surov\u00fd, P., Valent, P., \u010cer\u0148ava, J., Chud\u00fd, F., Tun\u00e1k, D., Salo\u0148, \u0160., and Mergani\u010d, J. (2018). Evaluation of close-range photogrammetry image collection methods for estimating tree diameters. ISPRS Int. J. Geo-Inform., 7.","DOI":"10.3390\/ijgi7030093"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Piermattei, L., Karel, W., Wang, D., Wieser, M., Mokro\u0161, M., Surov\u00fd, P., Kore\u0148, M., Toma\u0161t\u00edk, J., Pfeifer, N., and Hollaus, M. (2018). Terrestrial Structure from Motion photogrammetry for deriving forest inventory data. Remote Sens., 11.","DOI":"10.3390\/rs11080950"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1093\/forestry\/cpz067","article-title":"Estimating tree stem diameters and volume from smartphone photogrammetric point clouds","volume":"93","author":"Raumonen","year":"2020","journal-title":"Forestry"},{"key":"ref_17","unstructured":"Bell, J.F., and Dilworth, J.R. (2007). Log Scaling and Timber Cruising, John Bell & Associates, Inc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105332","DOI":"10.1016\/j.compag.2020.105332","article-title":"A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR","volume":"171","author":"Nguyen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1139\/cjfr-2013-0170","article-title":"Clear wood content in standing trees predicted from branch scar measurements with terrestrial LiDAR and verified with X-ray computed tomography","volume":"44","author":"Stangle","year":"2014","journal-title":"Can. J. For. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1080\/02827581.2015.1043340","article-title":"Stem quality assessment using terrestrial laser scanning technology: A case study of ash trees with a range of defects in two stands in Ireland","volume":"30","author":"Mengesha","year":"2015","journal-title":"Scand. J. For. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1139\/cjfr-2020-0484","article-title":"Impact of stem lean on estimation of Douglas-fir (Pseudotsuga menziesii) diameter and volume using mobile lidar scans","volume":"51","author":"Garms","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bauwens, S., Bartholomeus, H., Calders, K., and Lejeune, P. (2016). Forest inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests, 7.","DOI":"10.3390\/f7060127"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.isprsjprs.2020.09.014","article-title":"Is field-measured tree height as reliable as believed\u2014Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest","volume":"169","author":"Liang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"357","DOI":"10.17221\/92\/2015-JFS","article-title":"Accuracy of Structure from Motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour","volume":"62","author":"Panagiotidis","year":"2016","journal-title":"J. For. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Akpo, H.A., Atindogb\u00e9, G., Obiakara, M.C., Adjinanoukon, A.B., Gbedolo, M., Lejeune, P., and Fonton, N.H. (2020). Image data acquisition for estimating individual trees metrics: Closer is better. Forests, 11.","DOI":"10.3390\/f11010121"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1002\/ece3.4126","article-title":"Three-dimensional digitization of the arid land plant Haloxylon ammodendron using a consumer-grade camera","volume":"8","author":"Huang","year":"2018","journal-title":"Ecol. Evol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1111\/2041-210X.13388","article-title":"A quick, easy and non-invasive method to quantify coral growth rates using photogrammetry and 3D model comparisons","volume":"11","author":"Lange","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Krisanski, S., Taskhiri, M.S., and Turner, P. (2020). Enhancing methodsw for under-canopy unmanned aircraft systems based photogrammetry in complex forests for tree diameter measurement. Remote Sens., 12.","DOI":"10.3390\/rs12101652"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:55:41Z","timestamp":1760136941000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,17]]},"references-count":28,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081938"],"URL":"https:\/\/doi.org\/10.3390\/rs14081938","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,4,17]]}}}