{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:55:17Z","timestamp":1777362917141,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,3]],"date-time":"2018-11-03T00:00:00Z","timestamp":1541203200000},"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>Accurate measurement of the field leaf area index (LAI) is crucial for assessing forest growth and health status. Three-dimensional (3-D) structural information of trees from terrestrial laser scanning (TLS) have information loss to various extents because of the occlusion by canopy parts. The data with higher loss, regarded as poor-quality data, heavily hampers the estimation accuracy of LAI. Multi-location scanning, which proved effective in reducing the occlusion effects in other forests, is hard to carry out in the mangrove forest due to the difficulty of moving between mangrove trees. As a result, the quality of point cloud data (PCD) varies among plots in mangrove forests. To improve retrieval accuracy of mangrove LAI, it is essential to select only the high-quality data. Several previous studies have evaluated the regions of occlusion through the consideration of laser pulses trajectories. However, the model is highly susceptible to the indeterminate profile of complete vegetation object and computationally intensive. Therefore, this study developed a new index (vegetation horizontal occlusion index, VHOI) by combining unmanned aerial vehicle (UAV) imagery and TLS data to quantify TLS data quality. VHOI is asymptotic to 0.0 with increasing data quality. In order to test our new index, the VHOI values of 102 plots with a radius of 5 m were calculated with TLS data and UAV image. The results showed that VHOI had a strong linear relationship with estimation accuracy of LAI (R2 = 0.72, RMSE = 0.137). In addition, as TLS data were selected by VHOI less than different thresholds (1.0, 0.9, \u2026, 0.1), the number of remaining plots decreased while the agreement between LAI derived from TLS and field-measured LAI was improved. When the VHOI threshold is 0.3, the optimal trade-off is reached between the number of plots and LAI measurement accuracy (R2 = 0.67). To sum up, VHOI can be used as an index to select high-quality data for accurately measuring mangrove LAI and the suggested threshold is 0.30.<\/jats:p>","DOI":"10.3390\/rs10111739","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T10:43:45Z","timestamp":1541414625000},"page":"1739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Vegetation Horizontal Occlusion Index (VHOI) from TLS and UAV Image to Better Measure Mangrove LAI"],"prefix":"10.3390","volume":"10","author":[{"given":"Xianxian","family":"Guo","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography, The State University of New York at Buffalo, Buffalo, NY 14261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyan","family":"Tian","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-2744","authenticated-orcid":false,"given":"Dameng","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Geography, The State University of New York at Buffalo, Buffalo, NY 14261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China"},{"name":"State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Ministry of Science and Technology, Capital Normal University, Beijing 100048, China"},{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining leaf area index for non-flat leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant Cell Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving leaf area index (LAI) using remote sensing: Theories, methods and sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/JSTARS.2016.2557074","article-title":"Differentiating tree and shrub lai in a mixed forest with icesat\/glas spaceborne LiDAR","volume":"10","author":"Tian","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.rse.2016.02.013","article-title":"Assessment of multi-resolution image data for mangrove leaf area index mapping","volume":"176","author":"Kamal","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/10106049109354302","article-title":"The measurement of mangrove characteristics in southwest Florida using spot multispectral data","volume":"6","author":"Jensen","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1177\/0309133310385371","article-title":"Satellite remote sensing of mangrove forests: Recent advances and future opportunities","volume":"35","author":"Heumann","year":"2011","journal-title":"Progr. Phys. Geogr."},{"key":"ref_7","first-page":"22","article-title":"Comparison of UAV and worldview-2 imagery for mapping leaf area index of mangrove forest","volume":"61","author":"Tian","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0304-3770(97)00013-2","article-title":"Estimating leaf area index of mangroves from satellite data","volume":"58","author":"Green","year":"1997","journal-title":"Aquat. Bot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.agrformet.2006.09.007","article-title":"Influence of measurement set-up of ground-based LiDAR for derivation of tree structure","volume":"141","author":"Hoet","year":"2006","journal-title":"Agric. For. Meteorol."},{"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","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","author":"Breda","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/j.ecolind.2015.10.034","article-title":"Estimation of big sagebrush leaf area index with terrestrial laser scanning","volume":"61","author":"Olsoy","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1016\/j.rse.2010.08.030","article-title":"Measuring effective leaf area index, foliage profile, and stand height in New England forest stands using a full-waveform ground-based LiDAR","volume":"115","author":"Zhao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"206108","DOI":"10.1155\/2015\/206108","article-title":"Study of subtropical forestry index retrieval using terrestrial laser scanning and hemispherical photography","volume":"2015","author":"Yun","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.rse.2013.05.012","article-title":"Integrating terrestrial and airborne LiDAR to calibrate a 3D canopy model of effective leaf area index","volume":"136","author":"Hopkinson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/2150704X.2015.1111536","article-title":"Estimating leaf area index of maize using airborne full-waveform LiDAR data","volume":"7","author":"Nie","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.3390\/rs6032601","article-title":"Retrieval of gap fraction and effective plant area index from phase-shift terrestrial laser scans","volume":"6","author":"Pueschel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TGRS.2012.2205003","article-title":"Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning","volume":"51","author":"Zheng","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/treephys\/tpn022","article-title":"Estimating forest LAI profiles and structural parameters using a ground-based laser called \u2018echidna\u00ae","volume":"29","author":"Jupp","year":"2009","journal-title":"Tree Physiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3958","DOI":"10.1109\/TGRS.2012.2187907","article-title":"Computational-geometry-based retrieval of effective leaf area index using terrestrial laser scanning","volume":"50","author":"Zheng","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.agrformet.2015.03.008","article-title":"Terrestrial LiDAR remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution","volume":"209","author":"Zhao","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.agrformet.2015.06.005","article-title":"Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem","volume":"213","author":"Li","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.rse.2016.10.023","article-title":"Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm","volume":"194","author":"Schneider","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6324","DOI":"10.1109\/TGRS.2018.2836947","article-title":"Quality Assessment of Terrestrial Laser Scanner Ecosystem Observations Using Pulse Trajectories","volume":"56","author":"Paynter","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"778","article-title":"Evaluating the relationship between the photochemical reflectance index and light use efficiency in a mangrove forest with spartina alterniflora invasion","volume":"73","author":"Yang","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"838","DOI":"10.17520\/biods.2018067","article-title":"Researches on mangrove forest monitoring methods based on multi-source remote sensing","volume":"26","author":"Wang","year":"2018","journal-title":"Biodivers. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"921","DOI":"10.14358\/PERS.74.7.921","article-title":"Neural network classification of mangrove species from multiseasonal IKONOS imagery","volume":"74","author":"Wang","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.rse.2004.04.005","article-title":"Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama","volume":"91","author":"Wang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1080\/014311602331291215","article-title":"Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s13157-014-0558-6","article-title":"Assessing mangrove above-ground biomass and structure using terrestrial laser scanning: A case study in the Everglades National Park","volume":"34","author":"Feliciano","year":"2014","journal-title":"Wetlands"},{"key":"ref_33","first-page":"226","article-title":"Spatial variability of terrestrial laser scanning based leaf area index","volume":"19","author":"Zheng","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1016\/j.agrformet.2011.05.004","article-title":"Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements","volume":"151","author":"Widlowski","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3610","DOI":"10.1109\/TGRS.2006.881743","article-title":"Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning LiDAR","volume":"44","author":"Hosoi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_36","first-page":"284","article-title":"Sub-footprint analysis to uncover tree height variation using ICESat\/GLAS","volume":"35","author":"Tian","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4003","DOI":"10.3390\/rs6054003","article-title":"Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over antarctic moss beds","volume":"6","author":"Turner","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1111\/avsc.12024","article-title":"Unmanned aerial vehicles as innovative remote sensing platforms for high-resolution infrared imagery to support restoration monitoring in cut-over bogs","volume":"16","author":"Knoth","year":"2013","journal-title":"Appl. Veg. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tian, J., Li, X., Duan, F., Wang, J., and Ou, Y. (2016). An efficient seam elimination method for uav images based on wallis dodging and gaussian distance weight enhancement. Sensors, 16.","DOI":"10.3390\/s16050662"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1080\/2150704X.2018.1504339","article-title":"Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in mangrove forest","volume":"9","author":"Liu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1080\/01431161.2016.1214302","article-title":"How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review","volume":"37","author":"Yin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/0168-1923(91)90108-3","article-title":"Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand","volume":"56","author":"Chen","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","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."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2012.07.007","article-title":"Measuring gap fraction, element clumping index and LAI in Sierra Forest stands using a full-waveform ground-based lidar","volume":"125","author":"Zhao","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.03.011","article-title":"Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning","volume":"194","author":"Bailey","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1739\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:52Z","timestamp":1760196472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,3]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111739"],"URL":"https:\/\/doi.org\/10.3390\/rs10111739","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,3]]}}}