{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:38:15Z","timestamp":1774622295046,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T00:00:00Z","timestamp":1583971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Foundation of China, &quot;Study on forest thermal infrared remote sensing information model under coupled pest stress&quot;","award":["41571332"],"award-info":[{"award-number":["41571332"]}]},{"name":"The National Basic Research Program of China (973 Program)\uff0c &quot;Remote sensing monitoring and decision making technology for vegetation restoration in burning and cutting areas of Daxing 'an mountains&quot;","award":["2017yfc0504003-4"],"award-info":[{"award-number":["2017yfc0504003-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650\u20131000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.<\/jats:p>","DOI":"10.3390\/rs12060919","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T12:22:51Z","timestamp":1584015771000},"page":"919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8477-4983","authenticated-orcid":false,"given":"Peilun","family":"Hu","sequence":"first","affiliation":[{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, College of Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9355-2338","authenticated-orcid":false,"given":"Huaguo","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, College of Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-3609","authenticated-orcid":false,"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, 02431 Kirkkonummi, Finland"},{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6601-7882","authenticated-orcid":false,"given":"Jianbo","family":"Qi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Forest Cultivation and Protection, Ministry of Education, College of Forestry, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Changhui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, 02431 Kirkkonummi, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2718-114X","authenticated-orcid":false,"given":"Haohao","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4536-3430","authenticated-orcid":false,"given":"Wenxin","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Juha","family":"Hyypp\u00e4","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, 02431 Kirkkonummi, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/LGRS.2018.2870143","article-title":"A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection","volume":"16","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans.Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40362","DOI":"10.1038\/srep40362","article-title":"Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer","volume":"7","author":"Sun","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S0034-4257(01)00243-7","article-title":"Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve","volume":"79","author":"Kland","year":"2002","journal-title":"Remote Sens. Env."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3390\/rs8110936","article-title":"Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar","volume":"8","author":"Juan","year":"2016","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7057","DOI":"10.3390\/s100707057","article-title":"Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source","volume":"10","author":"Chen","year":"2010","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1080\/2150704X.2014.960608","article-title":"Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system","volume":"5","author":"Wang","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1127\/pfg\/2016\/0287","article-title":"Incidence Angle Dependency of Leaf Vegetation Indices from Hyperspectral Lidar Measurements","volume":"2016","author":"Kaasalainen","year":"2016","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Luo, S., Cheng, W., Xiaohuan, X., Hongcheng, Z., Dong, L., Shaobo, X., and Pinghua, W. (2016). Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification. Remote Sens., 8.","DOI":"10.3390\/rs8010003"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2007.03.011","article-title":"Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis","volume":"111","author":"Bork","year":"2007","journal-title":"Remote Sens. Env."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7119","DOI":"10.1364\/OE.20.007119","article-title":"Full waveform hyperspectral LiDAR for terrestrial laser scanning","volume":"20","author":"Hakala","year":"2012","journal-title":"Opt. Express."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Hyypp\u00e4, J., Wang, N., Jiang, C., Meng, F., Tang, L., Puttonen, E., and Li, C. (2019). A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter. Sensors, 19.","DOI":"10.3390\/s19071620"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shao, H., Chen, Y., Yang, Z., Jiang, C., Li, W., Wu, H., Wen, Z., Wang, S., Puttnon, E., and Hyypp\u00e4, J. (2019). A 91-Channel Hyperspectral LiDAR for Coal\/Rock Classification. IEEE Geosci. Remote Sens. Lett., (early access).","DOI":"10.1109\/LGRS.2019.2937720"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiang, C., Chen, Y., Wu, H., Li, W., Zhou, H., Bo, Y., Shao, H., Song, S., Puttonen, E., and Hyypp\u00e4, J. (2019). Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction. Remote Sens., 11.","DOI":"10.3390\/rs11172007"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shao, H., Chen, Y., Yang, Z., Jiang, C., Li, W., Wu, H., Wang, S., Yang, F., Chen, J., and Puttonen, E. (2020). Feasibility Study on Hyperspectral LiDAR for Ancient Huizhou-Style Architecture Preservation. Remote Sens., 12.","DOI":"10.3390\/rs12010088"},{"key":"ref_18","first-page":"291","article-title":"Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","unstructured":"Pesci, A., and Teza, G. (2008). Effects of surface irregularities on intensity data from laser scanning: An experimental approach. Ann. Geophys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isprsjprs.2011.01.005","article-title":"Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points","volume":"66","author":"Soudarissanane","year":"2011","journal-title":"ISPRS J. Photogramm."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.3390\/s110201657","article-title":"The properties of terrestrial laser system intensity for measuring leaf geometries: A case study with conference pear trees (Pyrus Communis)","volume":"11","author":"Balduzzi","year":"2011","journal-title":"Sensors"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2013.01.001","article-title":"The potential of dual-wavelength laser scanning for estimating vegetation moisture content","volume":"132","author":"Gaulton","year":"2013","journal-title":"Remote Sens. Env."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0034-4257(82)90057-8","article-title":"Spectral reflectance of partly transmitting leaves: Laboratory measurements and mathematical modeling","volume":"12","author":"Lillesaeter","year":"1982","journal-title":"Remote Sens. Env."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2005.12.001","article-title":"Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner","volume":"60","author":"Wagner","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1982). Microwave Remote Sensing: Active and Passive. Volume 2-Radar Remote Sensing and Surface Scattering and Emission Theory, NASA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:28Z","timestamp":1760173588000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,12]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12060919"],"URL":"https:\/\/doi.org\/10.3390\/rs12060919","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,12]]}}}