{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:49:08Z","timestamp":1769726948636,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Influence Seed Fund Project of National University of Defense Technology","award":["KY23C201"],"award-info":[{"award-number":["KY23C201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system\u2019s performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.<\/jats:p>","DOI":"10.3390\/s24175741","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T05:54:47Z","timestamp":1725429287000},"page":"5741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method"],"prefix":"10.3390","volume":"24","author":[{"given":"Simin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanhai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1194-6390","authenticated-orcid":false,"given":"Shaojing","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanhai 201209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5670-0054","authenticated-orcid":false,"given":"Yicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9443-777X","authenticated-orcid":false,"given":"Fashuai","family":"Li","sequence":"additional","affiliation":[{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-3609","authenticated-orcid":false,"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/LGRS.2015.2410788","article-title":"Design of a new multi-spectral waveform LiDAR instrument to monitor vegetation","volume":"12","author":"Niu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10342-010-0381-4","article-title":"Retrieval of forest structural parameters using LiDAR remote sensing","volume":"129","author":"Nieuwenhuis","year":"2010","journal-title":"Eur. J. For. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fernandez-Diaz, J.C., Carter, W.E., Glennie, C., Shrestha, R.L., 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_4","doi-asserted-by":"crossref","first-page":"5158","DOI":"10.3390\/s110505158","article-title":"Tree classification with fused mobile laser scanning and hyperspectral data","volume":"11","author":"Puttonen","year":"2011","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/TCI.2017.2703144","article-title":"Robust spectral unmixing of sparse multi-spectral lidar waveforms using gamma Markov random fields","volume":"3","author":"Altmann","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Takhtkeshha, N., Mandlburger, G., Remondino, F., and Hyypp\u00e4, J. (2024). Multi-spectral Light Detection and Ranging Technology and Applications: A Review. Sensors, 24.","DOI":"10.3390\/s24051669"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1364\/OE.477499","article-title":"Multi-spectral SWIR lidar for imaging and spectral discrimination through partial obscurations","volume":"31","author":"Sivaprakasam","year":"2023","journal-title":"Opt. Express"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"370","DOI":"10.3390\/geomatics2030021","article-title":"Classification of multi-spectral airborne lidar data using geometric and radiometric information","volume":"2","author":"Morsy","year":"2022","journal-title":"Geomatics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.rse.2017.09.037","article-title":"Identifying the genus or species of individual trees using a three-wavelength airborne lidar system","volume":"204","author":"Budei","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mielczarek, D., Sikorski, P., Archici\u0144ski, P., Ci\u0119\u017ckowski, W., Zaniewska, E., and Chorma\u0144ski, J. (2023). The Use of an Airborne Laser Scanner for Rapid Identification of Invasive Tree Species Acer negundo in Riparian Forests. Remote Sens., 15.","DOI":"10.3390\/rs15010212"},{"key":"ref_11","first-page":"100449","article-title":"Land cover mapping of urban environments using multispectral LiDAR data under data imbalance","volume":"21","author":"Ghaseminik","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/22797254.2020.1816142","article-title":"Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images","volume":"53","author":"Maltamo","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_13","first-page":"181","article-title":"Generation of digital terrain model from multispectral LiDar using different ground filtering techniques","volume":"24","author":"Taha","year":"2021","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"119","DOI":"10.5194\/isprsarchives-XL-5-W2-119-2013","article-title":"Multi-wavelength airborne laser scanning for archaeological prospection","volume":"40","author":"Briese","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","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. (2019). Feasibility study on hyperspectral LiDAR for ancient Huizhou-style architecture preservation. Remote Sens., 12.","DOI":"10.3390\/rs12010088"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/LGRS.2019.2937720","article-title":"A 91-channel hyperspectral LiDAR for coal\/rock classification","volume":"17","author":"Shao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Taher, J., Hakala, T., Jaakkola, A., Hyyti, H., Kukko, A., Manninen, P., Maanp\u00e4\u00e4, J., and Hyypp\u00e4, J. (2022). Feasibility of hyperspectral single photon lidar for robust autonomous vehicle perception. Sensors, 22.","DOI":"10.3390\/s22155759"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., and El-Rabbany, A. (2017). Multi-spectral LiDAR data for land cover classification of urban areas. Sensors, 17.","DOI":"10.3390\/s17050958"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2017.07.022","article-title":"Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America","volume":"199","author":"Yang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2012.02.025","article-title":"An alternative spectral index for rapid fire severity assessments","volume":"123","author":"Veraverbeke","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Okhrimenko, M., Coburn, C., and Hopkinson, C. (2019). Multi-spectral lidar: Radiometric calibration, canopy spectral reflectance, and vegetation vertical SVI profiles. Remote Sens., 11.","DOI":"10.3390\/rs11131556"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/LGRS.2011.2113312","article-title":"A multi-spectral canopy LiDAR demonstrator project","volume":"8","author":"Woodhouse","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1016\/j.rse.2009.05.019","article-title":"Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling","volume":"113","author":"Morsdorf","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2012.02.001","article-title":"Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance","volume":"69","author":"Morsdorf","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Tsoulias, N., Saha, K.K., and Zude-Sasse, M. (2022). 3D point cloud of normalized difference vegetation index (NDVI) of segmented fruit and leaves in apple production. bioRxiv, 2022.","DOI":"10.1101\/2022.10.24.513567"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, S., Jeong, T.I., Kim, S., Choi, E., Yang, E., Song, M., Eom, T.J., Kim, C., Gliserin, A., and Kim, S. (2024). Time division multiplexing based multi-spectral semantic camera for LiDAR applications. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-62342-2"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, Y., Sun, Z., Wang, S., Sun, S., Jia, J., Jiang, C., Hu, P., Yang, H., and Yang, X. (2024). Miniaturizing Hyperspectral Lidar System Employing Integrated Optical Filters. Remote Sens., 16.","DOI":"10.3390\/rs16091642"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"10586","DOI":"10.3390\/s111110586","article-title":"Absolute radiometric calibration of ALS intensity data: Effects on accuracy and target classification","volume":"11","author":"Kaasalainen","year":"2011","journal-title":"Sensors"},{"key":"ref_32","unstructured":"Kriegler, F.J. (1969, January 13\u201316). Preprocessing transformations and their effects on multspectral recognition. Proceedings of the Sixth International Symposium on Remote Sesning of Environment, Ann Arbor, MI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107611","DOI":"10.1016\/j.compag.2022.107611","article-title":"In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)","volume":"205","author":"Tsoulias","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5741\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:48:30Z","timestamp":1760111310000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175741"],"URL":"https:\/\/doi.org\/10.3390\/s24175741","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,4]]}}}