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bamboo utilization. However, the bamboo age is usually manually determined in the field, which is time-consuming and labor-intensive. Due to the ability to generate very high-density point clouds, terrestrial laser scanning (TLS) has been applied in forestry to acquire forest parameters. This study evaluated the potential of using the laser echo intensity data generated by TLS technology to determine the Moso bamboo age represented by \u201cdu.\u201d The intensity data were first corrected for the distance and incidence angle effects using an intensity correction method that constructed an empirical correction model by fitting piecewise polynomials to the intensity data collected based on a reference target. Then the models expressing the relationship between intensity and bamboo culm section number were constructed for different bamboo du by fitting polynomials to the intensity data of individual bamboo culms through least-squares adjustment. For a bamboo plant whose age is determined, the bamboo du could be determined based on the constructed intensity-culm section models. The proposed bamboo age determination method was tested at a site in a managed Moso bamboo forest in Lin\u2019an District, Hangzhou City, Zhejiang Province, China. From the test site, 56 and 120 bamboo plants with known bamboo ages were selected to construct the intensity-culm section models and to validate the bamboo age determination method, respectively. The bamboo age determination accuracies for each bamboo du were all above 90%. The result indicates a great potential for automatic determination of bamboo age in practice using TLS technology.<\/jats:p>","DOI":"10.3390\/rs14112550","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:25:12Z","timestamp":1653956712000},"page":"2550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of the Moso Bamboo Age Determination Based on Laser Echo Intensity"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1457-5986","authenticated-orcid":false,"given":"Wenbing","family":"Xu","sequence":"first","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Zihao","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Suying","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Susu","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rashidi, M., Mohammadi, M., Sadeghlou Kivi, S., Abdolvand, M.M., Truong-Hong, L., and Samali, B. (2020). A Decade of modern bridge monitoring using terrestrial laser scanning: Review and future directions. Remote Sens., 12.","DOI":"10.3390\/rs12223796"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2016.01.006","article-title":"Terrestrial laser scanning in forest inventories","volume":"115","author":"Liang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TGRS.2011.2161613","article-title":"Automatic stem mapping using single-scan terrestrial laser scanning","volume":"50","author":"Liang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S426","DOI":"10.5589\/m08-046","article-title":"Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna\u00ae)","volume":"34","author":"Strahler","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.03.020","article-title":"Three-dimensional forest reconstruction and structural parameter retrievals using a terrestrial full-waveform lidar instrument (Echidna\u00ae)","volume":"135","author":"Yang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.agrformet.2018.08.026","article-title":"Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning","volume":"263","author":"Zhu","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LGRS.2006.887064","article-title":"Forest canopy gap fraction from terrestrial laser scanning","volume":"4","author":"Danson","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.isprsjprs.2017.06.006","article-title":"Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner","volume":"130","author":"Li","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2015","journal-title":"Methods Ecol. Evol."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Disney, M., Burt, A., Wilkes, P., Armston, J., and Duncanson, L. (2020). New 3D measurements of large redwood trees for biomass and structure. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-73733-6"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ye, W., Qian, C., Tang, J., Liu, H., Fan, X., Liang, X., and Zhang, H. (2020). Improved 3D stem mapping method and elliptic hypothesis-based DBH estimation from terrestrial laser scanning data. Remote Sens., 12.","DOI":"10.3390\/rs12030352"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, X., Gong, Y., Sun, Y., Jiang, J., Zhang, Y., and Wen, X. (2021). Analysis of dynamic forest structures based on hierarchical features extracted from multi-station LiDAR scanning. Environ. Sci. Proc., 3.","DOI":"10.3390\/IECF2020-07871"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Puttonen, E., Lehtom\u00e4ki, M., Litkey, P., N\u00e4si, R., Feng, Z., Liang, X., Wittke, S., Pand\u017ei\u0107, M., Hakala, T., and Karjalainen, M. (2019). A clustering framework for monitoring circadian rhythm in structural dynamics in plants from terrestrial laser scanning time series. Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.00486"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lv, W., Zhou, G., Chen, G., Zhou, Y., Ge, Z., Niu, Z., Xu, L., and Shi, Y. (2020). Effects of Different Management Practices on the Increase in Phytolith-Occluded Carbon in Moso Bamboo Forests. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.591852"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.flora.2007.11.002","article-title":"Growth pattern and photosynthetic activity of different bamboo species growing in the Botanical Garden of Rome","volume":"203","author":"Gratani","year":"2008","journal-title":"Flora"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/BF02911032","article-title":"Plantation future of bamboo in China","volume":"15","author":"Li","year":"2004","journal-title":"J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jenvman.2015.03.030","article-title":"Current and potential carbon stocks in Moso bamboo forests in China","volume":"156","author":"Li","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_19","first-page":"87","article-title":"Determination of age of Phyllostachys pubescens","volume":"10","author":"Xiong","year":"1965","journal-title":"Sci. Silvae"},{"key":"ref_20","first-page":"83","article-title":"Study on age determination of Phyllostachys pubescens","volume":"10","author":"Yang","year":"1965","journal-title":"Sci. Silvae"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1080\/01431160903551389","article-title":"Estimation of aboveground carbon stock of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest with a Landsat Thematic Mapper image","volume":"32","author":"Xu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yan, Y., Xia, M., Fan, S., Zhan, M., and Guan, F. (2018). Detecting the competition between Moso bamboos and broad-leaved trees in mixed forests using a terrestrial laser scanner. Forests, 9.","DOI":"10.3390\/f9090520"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Xu, W. (2020). Volume-biomass conversation model of Moso bamboo shoots based on point cloud data. Laser Optoelectron. Prog., 57, (In Chinese).","DOI":"10.3788\/LOP57.212803"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, C., Cai, Y., Xiao, L., Gao, X., Shi, Y., Zhou, Y., Du, H., and Zhou, G. (2021). Rhizome extension characteristics, structure and carbon storage relationships with culms in a 10-year moso bamboo reforestation period. Forest Ecol. Manag., 498.","DOI":"10.1016\/j.foreco.2021.119556"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.isprsjprs.2011.10.005","article-title":"Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction","volume":"67","author":"Yan","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3097","DOI":"10.1080\/01431160500217277","article-title":"Radiometric correction in laser scanning","volume":"27","author":"Coren","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.isprsjprs.2007.05.008","article-title":"Correction of laser scanning intensity data: Data and model-driven approaches","volume":"62","author":"Pfeifer","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/rs1030144","article-title":"Radiometric calibration of terrestrial laser scanners with external reference targets","volume":"1","author":"Kaasalainen","year":"2009","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Errington, A.F.C., and Daku, B.L.F. (2017). Temperature compensation for radiometric correction of terrestrial LiDAR intensity data. Remote Sens., 9.","DOI":"10.3390\/rs9040356"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, K., and Cheng, X. (2015). Intensity data correction based on incidence angle and distance for terrestrial laser scanner. J. Appl. Remote Sens., 9.","DOI":"10.1117\/1.JRS.9.094094"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/LGRS.2019.2922226","article-title":"Distance effect correction on TLS intensity data using naturally homogeneous targets","volume":"17","author":"Tan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7038","DOI":"10.1109\/TGRS.2020.3032167","article-title":"Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners","volume":"59","author":"Tan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, T., Xu, L., Yang, B., Li, X., and Yao, J. (2017). Terrestrial laser scanning intensity correction by piecewise fitting and overlap-driven adjustment. Remote Sens., 9.","DOI":"10.3390\/rs9111090"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2015.12.004","article-title":"Correction of terrestrial LiDAR intensity channel using Oren\u2013Nayar reflectance model: An application to lithological differentiation","volume":"113","author":"Carrea","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.3390\/rs3102207","article-title":"Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods","volume":"3","author":"Kaasalainen","year":"2011","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1109\/TGRS.2014.2330852","article-title":"Intensity correction of terrestrial laser scanning data by estimating laser transmission function","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"28099","DOI":"10.3390\/s151128099","article-title":"A review of LiDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration","volume":"15","author":"Kashani","year":"2015","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Xu, W., and Cheng, X. (2020). Impact of plant surface features on 3D laser point cloud. Laser Optoelectron. Prog., 57, (In Chinese).","DOI":"10.3788\/LOP57.242802"},{"key":"ref_39","first-page":"338","article-title":"Variation analysis of physical characteristics in Phyllostachy pubescens stem at different growth ages","volume":"30","author":"Cui","year":"2010","journal-title":"J. Fujian Coll. For."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2550\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:19:21Z","timestamp":1760138361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112550"],"URL":"https:\/\/doi.org\/10.3390\/rs14112550","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,26]]}}}