{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:24:25Z","timestamp":1769552665477,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007172","name":"National Agriculture and Food Research Organization","doi-asserted-by":"publisher","award":["the research project for the future agricultural production utilizing artificial intelligence"],"award-info":[{"award-number":["the research project for the future agricultural production utilizing artificial intelligence"]}],"id":[{"id":"10.13039\/501100007172","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50\u2013100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.<\/jats:p>","DOI":"10.3390\/s20174809","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T09:05:37Z","timestamp":1598432737000},"page":"4809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods"],"prefix":"10.3390","volume":"20","author":[{"given":"Hiroyuki","family":"Obanawa","sequence":"first","affiliation":[{"name":"Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Sapporo 062-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6589-4442","authenticated-orcid":false,"given":"Rena","family":"Yoshitoshi","sequence":"additional","affiliation":[{"name":"Western Region Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Oda 694-0013, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nariyasu","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Western Region Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Oda 694-0013, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiichi","family":"Sakanoue","sequence":"additional","affiliation":[{"name":"Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Sapporo 062-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cooper, S.D., Roy, D.P., Schaaf, C.B., and Paynter, I. (2017). Examination of the potential of terrestrial laser scanning and structure-from-motion photogrammetry for rapid nondestructive field measurement of grass biomass. Remote Sens., 9.","DOI":"10.3390\/rs9060531"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"163","DOI":"10.7211\/jjsrt.41.163","article-title":"Tree height measurement from aerial images taken by a small unmanned aerial vehicle using structure from motion","volume":"41","author":"Tamura","year":"2015","journal-title":"J. Jpn. Soc. Reveg. Technol."},{"key":"ref_3","first-page":"15","article-title":"A comparison study on three-dimensional measurement of vegetation using lidar and SfM on the ground","volume":"30","author":"Itakura","year":"2018","journal-title":"Eco-Engineering"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Panday, U.S., Shrestha, N., Maharjan, S., Pratihast, A.K., Shrestha, K.L., and Aryal, J. (2020). Correlating the plant height of wheat with above-ground biomass and crop yield using drone imagery and crop surface model, a case study from Nepal. Drones, 4.","DOI":"10.3390\/drones4030028"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Viljanen, N., Honkavaara, E., N\u00e4si, R., Hakala, T., Niemel\u00e4inen, O., and Kaivosoja, J. (2018). A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture, 8.","DOI":"10.3390\/agriculture8050070"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.33584\/jnzg.2019.81.394","article-title":"Photogrammetry for assessment of pasture biomass","volume":"81","author":"Wigley","year":"2019","journal-title":"J. N. Z. Grassl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Batistoti, J., Marcato Junior, J., \u00cdtavo, L., Matsubara, E., Gomes, E., Oliveira, B., Souza, M., Siqueira, H., Filho, G.S., and Akiyama, T. (2019). Estimating pasture biomass and canopy height in Brazilian savanna using UAV photogrammetry. Remote Sens., 11.","DOI":"10.3390\/rs11202447"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1111\/gfs.12439","article-title":"Canopy height measurements and non-destructive biomass estimation of Lolium perenne swards using UAV imagery","volume":"74","author":"Muylle","year":"2019","journal-title":"Grass Forage Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"539","DOI":"10.5194\/isprs-archives-XLII-2-539-2018","article-title":"Watching grass grow\u2014A pilot study on the suitability of photogrammetric techniques for quantifying change in aboveground biomass in grassland experiments","volume":"XLII-2","author":"Anderson","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Heuschmidt, F., G\u00f3mez-Cand\u00f3n, D., Soares, C., Cerasoli, S., and Silva, J.M.N. (2020). Cork oak woodland land-cover types classification: A comparison between UAV sensed imagery and field survey. Int. J. Remote Sens., 41.","DOI":"10.1080\/2150704X.2020.1767822"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Horning, N., Fleishman, E., Ersts, P.J., Fogarty, F.A., and Zillig, M.W. (2020). Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles. Remote Sens. Ecol. Conserv.","DOI":"10.1002\/rse2.144"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"299","DOI":"10.5026\/jgeography.125.299","article-title":"Applications of terrestrial laser scanning in geomorphology","volume":"125","author":"Hayakawa","year":"2016","journal-title":"J. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez-Arellano, M., Griepentrog, H.W., Reiser, D., and Paraforos, D.S. (2016). 3-D imaging systems for agricultural applications\u2014A review. Sensors, 16.","DOI":"10.3390\/s16050618"},{"key":"ref_14","first-page":"210","article-title":"Application of ground-based laser scanner to plant measurement","volume":"49","author":"Omasa","year":"2010","journal-title":"J. Jpn. Soc. Photogramm."},{"key":"ref_15","first-page":"399","article-title":"Grass height and yield estimation using a 3-dimensional laser scanner","volume":"735","author":"Kaizu","year":"2010","journal-title":"Hokuno"},{"key":"ref_16","first-page":"21","article-title":"Measurements of vertical plant area density profiles of a rice plant using a portable scanning lidar","volume":"24","author":"Hosoi","year":"2012","journal-title":"Eco-Engineering"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1016\/j.agrformet.2010.07.010","article-title":"An evaluation of overhead laser scanning to estimate herbage removals in pasture quadrats","volume":"150","author":"Radtke","year":"2010","journal-title":"Agric. Forest Meteorol."},{"key":"ref_18","unstructured":"Kurosaki, H. (2020, August 13). Automatic Grass Height Measurement System Using Inexpensive 3D Shape Measurement Sensor. (In Japanese)."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schaefer, M.T., and Lamb, D.W. (2016). A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in tall fescue (Festuca arundinacea var. Fletcher). Remote Sens., 8.","DOI":"10.3390\/rs8020109"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5194\/isprsarchives-XL-7-101-2014","article-title":"Fusion of high resolution remote sensing images and terrestrial laser scanning for improved biomass estimation of maize","volume":"XL-7","author":"Schiedung","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2015.02.023","article-title":"Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR","volume":"164","author":"Greaves","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:47Z","timestamp":1760177207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,26]]},"references-count":22,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174809"],"URL":"https:\/\/doi.org\/10.3390\/s20174809","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,26]]}}}