{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:45:50Z","timestamp":1762674350541,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T00:00:00Z","timestamp":1566777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500\u20131000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. \u201cRed Edge\u201d parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the \u201cRed Edge\u201d position (REP), \u201cRed Edge\u201d slope (RES) and \u201cRed Edge\u201d area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC\u00a9 HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves\u2019 REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for \u201cRed Edge\u201d parameters extraction.<\/jats:p>","DOI":"10.3390\/rs11172007","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T04:38:23Z","timestamp":1566794303000},"page":"2007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction"],"prefix":"10.3390","volume":"11","author":[{"given":"Changhui","family":"Jiang","sequence":"first","affiliation":[{"name":"Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-3609","authenticated-orcid":false,"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland"},{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, Beijing 100094, China"}]},{"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, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Hui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430079, China"}]},{"given":"Yuming","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[{"name":"Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland"},{"name":"Department of Electronics Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"given":"Shaojing","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Communication and Information Engineering, Shanghai Polytechnic University, Shanghai 200216, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0985-4443","authenticated-orcid":false,"given":"Eetu","family":"Puttonen","sequence":"additional","affiliation":[{"name":"Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland"}]},{"given":"Juha","family":"Hyypp\u00e4","sequence":"additional","affiliation":[{"name":"Center of Excellence of Laser Scanning Research, Finnish Geospatial Research Institute, Masala FI-02430, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and lidar remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11947-016-1817-8","article-title":"Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products","volume":"10","author":"Ravikanth","year":"2017","journal-title":"Food Bioprocess Technol."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Fernandez-Diaz, J., Carter, W., Glennie, C., Shrestha, R., Pan, Z., Ekhtari, N., Singhania, A., Hauser, D., and Sartori, M. (2016). Capability assessment and performance metrics for the Titan multispectral mapping LiDAR. Remote Sens., 8.","DOI":"10.3390\/rs8110936"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.2110\/jsr.2011.31","article-title":"Lidar intensity as a remote sensor of rock properties","volume":"81","author":"Burton","year":"2011","journal-title":"J. Sediment. Res."},{"key":"ref_7","first-page":"1","article-title":"Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR","volume":"99","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","first-page":"259","article-title":"Assessing the possibility of land-cover classification using lidar intensity data","volume":"34","author":"Song","year":"2002","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.ecss.2008.02.003","article-title":"Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery","volume":"78","author":"Chust","year":"2008","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/TGRS.2008.2003351","article-title":"Radiometric calibration of LiDAR intensity with commercially available reference targets","volume":"47","author":"Kaasalainen","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hata, A., and Wolf, D. (2014, January 8\u201311). Road marking detection using LIDAR reflective intensity data and its application to vehicle localization. Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957753"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.isprsjprs.2014.09.009","article-title":"Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology","volume":"97","author":"Eitel","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","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. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0034-4257(03)00139-1","article-title":"Characterizing vertical forest structure using small-footprint airborne LiDAR","volume":"87","author":"Zimble","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Douglas, E.S., Strahler, A., Martel, J., Cook, T., Mendillo, C., Marshall, R., Chakrabarti, S., Schaaf, C., Woodcock, C., and Li, Z. (2012, January 22\u201327). DWEL: A dual-wavelength echidna lidar for ground-based forest scanning. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352489"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"013536","DOI":"10.1117\/1.2794018","article-title":"Carnegie airborne observatory: In-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems","volume":"1","author":"Asner","year":"2007","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2007.01.001","article-title":"Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction","volume":"62","author":"Sohn","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","first-page":"136","article-title":"Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR","volume":"44","author":"Du","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, Y., and Li, C. (2018). A Hyperspectral LiDAR with Eight Channels Covering from VIS to SWIR[C]. IEEE Int. Geosci. Remote Sens. Symp., 4293\u20134296.","DOI":"10.1109\/IGARSS.2018.8517741"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/LGRS.2015.2405573","article-title":"Improving backscatter intensity calibration for multispectral LiDAR","volume":"12","author":"Shi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/LGRS.2006.888848","article-title":"Toward hyperspectral lidar: Measurement of spectral backscatter intensity with a supercontinuum laser source","volume":"4","author":"Kaasalainen","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.agrformet.2014.08.018","article-title":"Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR","volume":"198","author":"Nevalainen","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/isprsannals-II-5-W2-205-2013","article-title":"Nitrogen concentration estimation with hyperspectral LiDAR[J]","volume":"2","author":"Nevalainen","year":"2013","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.isprsjprs.2017.04.005","article-title":"Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating","volume":"128","author":"Matikainen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"013105","DOI":"10.1117\/1.OE.54.1.013105","article-title":"Artificial target detection with a hyperspectral LiDAR over 26-h measurement","volume":"54","author":"Puttonen","year":"2015","journal-title":"Opt. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/LGRS.2015.2410788","article-title":"Design of a new multispectral waveform LiDAR instrument to monitor vegetation","volume":"12","author":"Niu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Hyypp\u00e4, J., Wang, N., Jiang, C., Meng, F., 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_33","doi-asserted-by":"crossref","first-page":"A468","DOI":"10.1364\/OE.27.00A468","article-title":"Portable hyperspectral lidar utilizing 5 GHz multichannel full waveform digitization","volume":"27","author":"Kaasalainen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1109\/TGRS.2003.813555","article-title":"Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index","volume":"41","author":"Pu","year":"2003","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_35","first-page":"123","article-title":"Hyperspectral models for estimating vegetation chlorophyll content based on red edge parameter","volume":"25","author":"Yao","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_36","first-page":"598","article-title":"Detection of chlorophyll fluorescence in vegetation from airborne hyperspectral CASI imagery in the red edge spectral region","volume":"1","year":"2004","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1080\/01431169008955128","article-title":"Quantitative characterization of the vegetation red edge reflectance. An inverted-Gaussian reflectance model","volume":"11","author":"Miller","year":"1990","journal-title":"Remote Sens."},{"key":"ref_38","first-page":"10","article-title":"Hyperspectral imagery for mapping disease infection in oil palm plantationusing vegetation indices and red edge techniques","volume":"6","author":"Shafri","year":"2009","journal-title":"Am. J. Appl. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/2007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:13:57Z","timestamp":1760188437000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/2007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,26]]},"references-count":38,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11172007"],"URL":"https:\/\/doi.org\/10.3390\/rs11172007","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,8,26]]}}}