Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 13; H. 3; S. 352 - 19
Hauptverfasser:	Neuville, Romain, Bates, Jordan Steven, Jonard, François
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	MDPI AG 20.01.2021
Schlagworte:	Accurate estimation Aircraft detection algorithms Antennas Application contexts Biologie végétale (sciences végétales, sylviculture, mycologie...) canopy Clustering algorithms Computer science data collection Density-Based Spatial Clustering Diameter-at-breast heights Earth sciences & physical geography Ecosystems Engineering, computing & technology Environmental sciences & ecology forest stands Forestry forests HDBSCAN Hierarchical clustering Ingénierie, informatique & technologie LiDAR Life sciences Light detection and ranging Machine learning Omission and commission errors Optical radar PCA Physical, chemical, mathematical & earth Sciences Physique, chimie, mathématiques & sciences de la terre Phytobiology (plant sciences, forestry, mycology...) Point cloud principal component analysis Sciences de la terre & géographie physique Sciences de l’environnement & écologie Sciences du vivant Sciences informatiques Segmentation procedure Semantics Site-specific parameters tree and stand measurements Tree diameter Tree stem segmentation trees UAV Unmanned aerial vehicles (UAV)
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.
AbstractList	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose. Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Author	Jonard, François Neuville, Romain Bates, Jordan Steven
Author_xml	– sequence: 1 givenname: Romain orcidid: 0000-0003-1949-1332 surname: Neuville fullname: Neuville, Romain – sequence: 2 givenname: Jordan Steven orcidid: 0000-0002-1349-7189 surname: Bates fullname: Bates, Jordan Steven – sequence: 3 givenname: François orcidid: 0000-0002-8562-2073 surname: Jonard fullname: Jonard, François
BookMark	eNptkd9rFDEQx4NUsNa--BfkUYTV_Nrs7uNxtlq4UrE9X8NsdnKm7CU1yQr-96Z3iiKdeZhh-H6-MDMvyUmIAQl5zdk7KQf2PmUuWc1WPCOngnWiUWIQJ__0L8h5zveshpR8YOqUbC9y8XsoPuzoZUyYC70tabFlSUhdinu6XX1truMSCk504z-svtDP0YdC13NcJrrNj-Q12G8-IN0gpFAHr8hzB3PG89_1jGwvL-7Wn5rNzcer9WrTWNXp0gx2nITi3Ek58R5sJ62TnVbgwDLlBEiQymmEQWnHoEPdj7plo9bW6U5M8oxcHX2nCPfmIdVN0k8TwZvDIKadgVS8ndH0Ix-4AmwFA9VrB73VEq3GfoShc6x6yaPX7HGHlR29-SEOZod-mauZNSMaIXRvhJa6ayv15kg9pPh9qecze58tzjMEjEs2om350Oqe8SplR6lNMeeEzlhf6uVjKAn8bDgzj280f99Ykbf_IX92fEL8Cwtkne8
CitedBy_id	crossref_primary_10_3390_rs14235992 crossref_primary_10_3390_mi15060712 crossref_primary_10_3390_rs16122215 crossref_primary_10_3390_rs17071271 crossref_primary_10_1016_j_foreco_2025_122977 crossref_primary_10_3390_agronomy12030555 crossref_primary_10_3390_sym14040726 crossref_primary_10_1139_cjfr_2021_0217 crossref_primary_10_3390_f14061159 crossref_primary_10_3390_s22010139 crossref_primary_10_1016_j_isprsjprs_2025_03_014 crossref_primary_10_1186_s13595_025_01291_w crossref_primary_10_3390_f16081347 crossref_primary_10_1016_j_rse_2021_112522 crossref_primary_10_3390_su15032649 crossref_primary_10_3390_rs17101682 crossref_primary_10_1080_19475705_2024_2327399 crossref_primary_10_3390_ijgi11080423 crossref_primary_10_3390_rs17132245 crossref_primary_10_1016_j_rsase_2024_101260 crossref_primary_10_3390_ijgi10110762 crossref_primary_10_3390_f14091876 crossref_primary_10_3390_rs14030720 crossref_primary_10_1109_JSTARS_2025_3546651 crossref_primary_10_1016_j_jenvman_2023_117693 crossref_primary_10_1080_01431161_2022_2161853 crossref_primary_10_3390_rs16101734 crossref_primary_10_1016_j_rsase_2023_100997 crossref_primary_10_3390_f15071111 crossref_primary_10_1016_j_ecoinf_2025_103127 crossref_primary_10_1088_1755_1315_806_1_012033 crossref_primary_10_3390_rs15010115 crossref_primary_10_1016_j_optlaseng_2022_107178 crossref_primary_10_3390_rs17050785 crossref_primary_10_1016_j_srs_2025_100254 crossref_primary_10_3390_f16030457 crossref_primary_10_1016_j_ufug_2025_129018 crossref_primary_10_1007_s40725_025_00251_x crossref_primary_10_1080_22797254_2025_2491750 crossref_primary_10_1109_TGRS_2024_3518567 crossref_primary_10_1109_JSTARS_2023_3317500 crossref_primary_10_3390_drones6090240 crossref_primary_10_4005_jjfs_107_85 crossref_primary_10_3390_rs17020229 crossref_primary_10_3390_f16030453 crossref_primary_10_3390_s25185798 crossref_primary_10_3390_rs13040710 crossref_primary_10_3390_rs14010235 crossref_primary_10_3390_f13071039 crossref_primary_10_3390_rs14225904 crossref_primary_10_1038_s41598_025_86704_6 crossref_primary_10_3390_f14122392 crossref_primary_10_1016_j_compag_2022_107209 crossref_primary_10_34133_plantphenomics_0264 crossref_primary_10_1109_JSYST_2021_3100278 crossref_primary_10_3390_s22072666 crossref_primary_10_3390_rs13132627 crossref_primary_10_1016_j_jhydrol_2022_128681 crossref_primary_10_1111_2041_210X_13912 crossref_primary_10_3390_s24165409 crossref_primary_10_3390_rs14194757 crossref_primary_10_1016_j_jag_2025_104493 crossref_primary_10_3390_f16091481 crossref_primary_10_3390_rs14122753 crossref_primary_10_3389_fpls_2023_1139232 crossref_primary_10_3390_f13122077 crossref_primary_10_3390_app13010276 crossref_primary_10_1080_01431161_2024_2370499 crossref_primary_10_3390_f16091483 crossref_primary_10_3390_rs17020328 crossref_primary_10_3390_rs16101783 crossref_primary_10_3390_agriculture13112097 crossref_primary_10_3390_f14122456 crossref_primary_10_3390_f12070856 crossref_primary_10_3390_rs15092380 crossref_primary_10_3390_rs15092263 crossref_primary_10_1007_s42979_023_02592_5 crossref_primary_10_3390_app11125340 crossref_primary_10_3390_rs14215487 crossref_primary_10_3390_rs17081456 crossref_primary_10_3390_land13111856 crossref_primary_10_1109_JSTARS_2025_3545482 crossref_primary_10_1016_j_rsase_2025_101691 crossref_primary_10_1061_JSUED2_SUENG_1410 crossref_primary_10_1016_j_ecoleng_2022_106671 crossref_primary_10_32604_cmc_2023_034892 crossref_primary_10_1016_j_compag_2022_106966 crossref_primary_10_1080_01431161_2024_2398228 crossref_primary_10_1016_j_ymssp_2023_111050 crossref_primary_10_1109_ACCESS_2023_3296066 crossref_primary_10_3390_wevj16030171 crossref_primary_10_3390_f16071155 crossref_primary_10_1155_2022_3175998 crossref_primary_10_1080_10106049_2022_2048902 crossref_primary_10_1016_j_infrared_2023_104802 crossref_primary_10_1016_j_rse_2021_112540 crossref_primary_10_3390_rs13214312 crossref_primary_10_3390_en15145245
Cites_doi	10.3390/f10030277 10.3390/f9070395 10.1007/BF01934268 10.1016/j.isprsjprs.2018.04.019 10.1016/j.foreco.2004.07.077 10.3390/rs5020584 10.3390/rs12101667 10.3390/f7030062 10.1023/A:1007586507433 10.1002/eap.2154 10.1016/j.isprsjprs.2014.03.014 10.1016/j.isprsjprs.2010.08.002 10.3390/f9010006 10.1002/rob.21863 10.1007/BF00892986 10.1016/j.isprsjprs.2009.04.002 10.1080/01431161.2016.1277044 10.1109/TNN.2005.845141 10.1016/j.foreco.2015.06.003 10.1016/j.envsci.2010.12.004 10.3390/rs6054323 10.1016/S0898-1221(98)00101-1 10.1016/j.isprsjprs.2017.11.013 10.3390/rs12081245 10.3390/f9070398 10.1016/j.rse.2006.03.003 10.1126/science.aax0848 10.1109/ICCV.2017.569 10.3390/s140101228 10.1016/j.envsoft.2016.04.025 10.3390/rs70708631 10.1029/93WR03553 10.1109/TGRS.2003.810682 10.1007/978-3-662-03664-8 10.3390/rs12050885 10.1080/07038992.2014.987376 10.1191/0309133305pp432ra 10.3390/rs11131550 10.3390/rs10091403 10.1016/B978-044482107-2/50036-4 10.2737/PNW-GTR-768 10.1080/01431160701736406 10.1029/1999WR900034 10.3390/rs12050863 10.3390/rs5020491 10.3390/rs9080785 10.1109/TGRS.2014.2308208 10.1109/TGRS.2006.890412 10.3390/rs11232781 10.3233/IDA-2007-11602 10.3390/rs11060615 10.1126/science.aac6759 10.3390/f7060127 10.1007/978-3-540-31865-1_25 10.1016/j.isprsjprs.2017.07.001 10.3390/rs11111271 10.1139/juvs-2013-0017 10.14358/PERS.76.6.701 10.1109/TGRS.2011.2161613 10.3390/rs12101652 10.3390/rs12203327 10.3390/rs12152426 10.1111/gcb.13388 10.1016/j.isprsjprs.2020.01.018 10.3390/rs9111154 10.14358/PERS.78.11.1275 10.3390/f6113923 10.3133/tm11B7 10.1016/S0924-2716(98)00009-4 10.1109/TGRS.2014.2315649 10.1016/j.isprsjprs.2018.06.021 10.1016/j.jhydrol.2011.08.032 10.1111/j.1467-9671.2004.00169.x 10.3390/rs70809975 10.3390/rs10111759 10.1145/3233794 10.1139/x99-215 10.1016/j.rse.2018.12.034 10.3390/rs8060501 10.1080/01431161.2016.1219425 10.3390/rs10111845 10.14358/PERS.81.4.281 10.17221/28/2017-JFS 10.1016/j.isprsjprs.2020.03.021 10.2514/6.2009-6113 10.1016/S0378-1127(98)00431-9 10.1016/j.isprsjprs.2015.10.004 10.3390/rs12081236 10.1109/ACCESS.2020.2995389 10.1080/01431161.2010.494184 10.1038/d41586-019-01026-8 10.3390/s17102371 10.1007/s10846-016-0348-x 10.1016/j.isprsjprs.2016.03.016
ContentType	Journal Article
DBID	AAYXX CITATION 7S9 L.6 JLOSS Q33 DOA
DOI	10.3390/rs13030352
DatabaseName	CrossRef AGRICOLA AGRICOLA - Academic Université de Liège - Open Repository and Bibliography (ORBI) (Open Access titles only) Université de Liège - Open Repository and Bibliography (ORBI) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitleList	CrossRef AGRICOLA
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Forestry Computer Science
EISSN	2072-4292
EndPage	19
ExternalDocumentID	oai_doaj_org_article_8b1914ae520a486fa8c63ec6e8ba97f0 oai_orbi_ulg_ac_be_2268_263675 10_3390_rs13030352
GroupedDBID	29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7S9 L.6 PUEGO C1A IPNFZ JLOSS Q33 RIG
ID	FETCH-LOGICAL-c476t-9cbd2411f33d18ac73cf3764afac04f2a3a34f6ea946f0a7e68b650b66cf672d3
IEDL.DBID	DOA
ISICitedReferencesCount	117
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000615480700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Tue Oct 14 18:48:14 EDT 2025 Sat Nov 29 01:28:03 EST 2025 Thu Oct 02 07:47:38 EDT 2025 Sat Nov 29 07:14:25 EST 2025 Tue Nov 18 21:47:40 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	3
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c476t-9cbd2411f33d18ac73cf3764afac04f2a3a34f6ea946f0a7e68b650b66cf672d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 scopus-id:2-s2.0-85099840710
ORCID	0000-0003-1949-1332 0000-0002-8562-2073 0000-0002-1349-7189
OpenAccessLink	https://doaj.org/article/8b1914ae520a486fa8c63ec6e8ba97f0
PQID	2551956801
PQPubID	24069
PageCount	19
ParticipantIDs	doaj_primary_oai_doaj_org_article_8b1914ae520a486fa8c63ec6e8ba97f0 liege_orbi_v2_oai_orbi_ulg_ac_be_2268_263675 proquest_miscellaneous_2551956801 crossref_citationtrail_10_3390_rs13030352 crossref_primary_10_3390_rs13030352
PublicationCentury	2000
PublicationDate	20210120
PublicationDateYYYYMMDD	2021-01-20
PublicationDate_xml	– month: 01 year: 2021 text: 20210120 day: 20
PublicationDecade	2020
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2021
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Wallace (ref_28) 2014; 52 Liang (ref_49) 2014; 14 ref_92 Zhang (ref_83) 1994; 30 Wang (ref_100) 2008; 36 ref_11 Buttazzo (ref_21) 2016; 83 ref_98 ref_95 ref_19 Zhao (ref_78) 2016; 117 ref_17 Forsman (ref_30) 2018; 135 Liang (ref_42) 2018; 144 Boyd (ref_10) 2005; 29 Ke (ref_8) 2011; 32 Kukko (ref_50) 2020; 161 Caruso (ref_89) 1998; 35 Bastin (ref_2) 2019; 365 Guo (ref_88) 2010; 76 ref_22 ref_20 Rognant (ref_85) 1998; 32 ref_29 Zhang (ref_76) 2003; 41 ref_26 Liang (ref_48) 2018; 143 Lovell (ref_34) 2005; 214 Jonard (ref_3) 2011; 409 Pajares (ref_13) 2015; 81 Zimmerman (ref_91) 1999; 31 Xia (ref_67) 2015; 6 ref_70 ref_79 ref_74 Dey (ref_94) 2016; 7 Xu (ref_93) 2005; 16 Wu (ref_55) 2013; 5 Kienzle (ref_87) 2004; 8 Romijn (ref_6) 2015; 352 ref_84 Toth (ref_15) 2016; 115 Liang (ref_66) 2012; 50 Jaakkola (ref_14) 2010; 65 Pestana (ref_23) 2019; 36 Jucker (ref_31) 2017; 23 Salach (ref_75) 2017; 38 Yin (ref_27) 2019; 223 ref_58 ref_57 Almeida (ref_9) 2019; 79 ref_54 Wallace (ref_24) 2012; 39 ref_53 ref_51 Henning (ref_40) 2006; 52 Banskota (ref_1) 2014; 40 Gorte (ref_71) 2004; 35 Vonderach (ref_73) 2012; 39 Wang (ref_68) 2020; 8 Wallace (ref_25) 2014; 52 ref_59 Lamprecht (ref_97) 2015; 7 Maas (ref_39) 2008; 29 ref_61 Reitberger (ref_101) 2009; 64 ref_60 Williams (ref_33) 2000; 30 Jing (ref_103) 2012; 78 ref_64 ref_63 ref_62 Muhairwe (ref_32) 1999; 113 Walker (ref_86) 1999; 35 Chisholm (ref_56) 2013; 01 Trumbore (ref_4) 2015; 349 Sattar (ref_105) 2006; Volume 4304 Lu (ref_106) 2014; 94 Rahman (ref_102) 2014; 38 ref_36 ref_35 Hamraz (ref_37) 2017; 130 ref_111 ref_110 Cressie (ref_90) 1988; 20 ref_113 Omran (ref_96) 2007; 11 ref_112 ref_38 Evans (ref_80) 2007; 45 (ref_52) 2017; 63 Kraus (ref_81) 1998; 53 Goodwin (ref_12) 2006; 103 Goutte (ref_104) 2005; Volume 3408 ref_108 ref_107 (ref_7) 2011; 14 Gander (ref_99) 1994; 34 Lewis (ref_5) 2019; 568 Montealegre (ref_77) 2015; 7 ref_109 ref_46 Hakala (ref_69) 2020; 164 ref_45 ref_44 ref_43 ref_41 Marselis (ref_47) 2016; 82 Ioki (ref_82) 2012; 02 Raumonen (ref_72) 2013; 5 Goodbody (ref_16) 2017; 38 Hepp (ref_18) 2018; 38 Olofsson (ref_65) 2014; 6
References_xml	– ident: ref_74 – ident: ref_46 doi: 10.3390/f10030277 – ident: ref_53 doi: 10.3390/f9070395 – volume: 34 start-page: 558 year: 1994 ident: ref_99 article-title: Least-Squares Fitting of Circles and Ellipses publication-title: BIT doi: 10.1007/BF01934268 – volume: 143 start-page: 97 year: 2018 ident: ref_48 article-title: In-Situ Measurements from Mobile Platforms: An Emerging Approach to Address the Old Challenges Associated with Forest Inventories publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.04.019 – volume: 214 start-page: 398 year: 2005 ident: ref_34 article-title: Simulation Study for Finding Optimal Lidar Acquisition Parameters for Forest Height Retrieval publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2004.07.077 – volume: 5 start-page: 584 year: 2013 ident: ref_55 article-title: A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data publication-title: Remote Sens. doi: 10.3390/rs5020584 – ident: ref_111 doi: 10.3390/rs12101667 – ident: ref_26 doi: 10.3390/f7030062 – volume: 31 start-page: 375 year: 1999 ident: ref_91 article-title: An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting publication-title: Math. Geol. doi: 10.1023/A:1007586507433 – ident: ref_36 doi: 10.1002/eap.2154 – volume: 94 start-page: 1 year: 2014 ident: ref_106 article-title: A Bottom-up Approach to Segment Individual Deciduous Trees Using Leaf-off Lidar Point Cloud Data publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2014.03.014 – volume: 65 start-page: 514 year: 2010 ident: ref_14 article-title: A Low-Cost Multi-Sensoral Mobile Mapping System and Its Feasibility for Tree Measurements publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2010.08.002 – ident: ref_43 doi: 10.3390/f9010006 – volume: 36 start-page: 734 year: 2019 ident: ref_23 article-title: Overview Obstacle Maps for Obstacle-Aware Navigation of Autonomous Drones publication-title: J. Field Robot. doi: 10.1002/rob.21863 – volume: 20 start-page: 17 year: 1988 ident: ref_90 article-title: Spatial Prediction and Ordinary Kriging publication-title: Math. Geol. doi: 10.1007/BF00892986 – volume: 64 start-page: 561 year: 2009 ident: ref_101 article-title: 3D Segmentation of Single Trees Exploiting Full Waveform LIDAR Data publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2009.04.002 – volume: 38 start-page: 2921 year: 2017 ident: ref_75 article-title: Evaluation of the Accuracy of Lidar Data Acquired Using a UAS for Levee Monitoring: Preliminary Results publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1277044 – volume: 16 start-page: 645 year: 2005 ident: ref_93 article-title: Survey of Clustering Algorithms publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2005.845141 – volume: 352 start-page: 109 year: 2015 ident: ref_6 article-title: Assessing Change in National Forest Monitoring Capacities of 99 Tropical Countries publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2015.06.003 – volume: 14 start-page: 181 year: 2011 ident: ref_7 article-title: Community Forest Monitoring in REDD+: The ‘M’ in MRV? publication-title: Environ. Sci. Policy doi: 10.1016/j.envsci.2010.12.004 – volume: 6 start-page: 4323 year: 2014 ident: ref_65 article-title: Tree Stem and Height Measurements Using Terrestrial Laser Scanning and the RANSAC Algorithm publication-title: Remote Sens. doi: 10.3390/rs6054323 – volume: 39 start-page: 451 year: 2012 ident: ref_73 article-title: Voxel-Based Approach for Estimating Urban Tree Volume from Terrestrial Laser Scanning Data publication-title: ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – volume: 35 start-page: 109 year: 1998 ident: ref_89 article-title: Interpolation Methods Comparison publication-title: Comput. Math. Appl. doi: 10.1016/S0898-1221(98)00101-1 – volume: 135 start-page: 84 year: 2018 ident: ref_30 article-title: Bias of Cylinder Diameter Estimation from Ground-Based Laser Scanners with Different Beam Widths: A Simulation Study publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.11.013 – ident: ref_60 doi: 10.3390/rs12081245 – volume: 36 start-page: 45 year: 2008 ident: ref_100 article-title: LiDAR Point Cloud Based Fully Automatic 3d Single Tree Modelling in Forest and Evaluations of the Procedure publication-title: Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – ident: ref_41 doi: 10.3390/f9070398 – volume: 103 start-page: 140 year: 2006 ident: ref_12 article-title: Assessment of Forest Structure with Airborne LiDAR and the Effects of Platform Altitude publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2006.03.003 – volume: 365 start-page: 76 year: 2019 ident: ref_2 article-title: The Global Tree Restoration Potential publication-title: Science doi: 10.1126/science.aax0848 – ident: ref_17 doi: 10.1109/ICCV.2017.569 – volume: 14 start-page: 1228 year: 2014 ident: ref_49 article-title: Possibilities of a Personal Laser Scanning System for Forest Mapping and Ecosystem Services publication-title: Sensors doi: 10.3390/s140101228 – ident: ref_92 – volume: 82 start-page: 142 year: 2016 ident: ref_47 article-title: Deriving Comprehensive Forest Structure Information from Mobile Laser Scanning Observations Using Automated Point Cloud Classification publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2016.04.025 – volume: 7 start-page: 8631 year: 2015 ident: ref_77 article-title: Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications publication-title: Remote Sens. doi: 10.3390/rs70708631 – volume: 30 start-page: 1019 year: 1994 ident: ref_83 article-title: Digital Elevation Model Grid Size, Landscape Representation, and Hydrologic Simulations publication-title: Water Resour. Res. doi: 10.1029/93WR03553 – volume: 41 start-page: 872 year: 2003 ident: ref_76 article-title: A Progressive Morphological Filter for Removing Nonground Measurements from Airborne LIDAR Data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2003.810682 – volume: 02 start-page: 89 year: 2012 ident: ref_82 article-title: Estimating Vertical Distribution of Vegetation Cover in Temperate Heterogeneous Forests Using Airborne Laser Scanning Data publication-title: Open J. For. – ident: ref_35 doi: 10.1007/978-3-662-03664-8 – ident: ref_70 doi: 10.3390/rs12050885 – volume: 40 start-page: 362 year: 2014 ident: ref_1 article-title: Forest Monitoring Using Landsat Time Series Data: A Review publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2014.987376 – volume: 29 start-page: 1 year: 2005 ident: ref_10 article-title: Satellite Remote Sensing of Forest Resources: Three Decades of Research Development publication-title: Prog. Phys. Geogr. Earth Environ. doi: 10.1191/0309133305pp432ra – volume: 38 start-page: 123 year: 2014 ident: ref_102 article-title: Tree Crown Delineation from High Resolution Airborne Lidar Based on Densities of High Points publication-title: Int. Arch. Photogramm. Remote Sens. – ident: ref_22 doi: 10.3390/rs11131550 – ident: ref_51 doi: 10.3390/rs10091403 – ident: ref_98 doi: 10.1016/B978-044482107-2/50036-4 – ident: ref_107 doi: 10.2737/PNW-GTR-768 – volume: 29 start-page: 1579 year: 2008 ident: ref_39 article-title: Automatic Forest Inventory Parameter Determination from Terrestrial Laser Scanner Data publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701736406 – volume: 35 start-page: 2259 year: 1999 ident: ref_86 article-title: On the Effect of Digital Elevation Model Accuracy on Hydrology and Geomorphology publication-title: Water Resour. Res. doi: 10.1029/1999WR900034 – volume: Volume 4304 start-page: 1015 year: 2006 ident: ref_105 article-title: Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation publication-title: AI 2006: Advances in Artificial Intelligence – ident: ref_58 doi: 10.3390/rs12050863 – ident: ref_64 – ident: ref_19 – ident: ref_95 – volume: 5 start-page: 491 year: 2013 ident: ref_72 article-title: Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data publication-title: Remote Sens. doi: 10.3390/rs5020491 – ident: ref_59 doi: 10.3390/rs9080785 – volume: 52 start-page: 7160 year: 2014 ident: ref_28 article-title: An Assessment of the Repeatability of Automatic Forest Inventory Metrics Derived From UAV-Borne Laser Scanning Data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2308208 – volume: 45 start-page: 1029 year: 2007 ident: ref_80 article-title: A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.890412 – ident: ref_109 doi: 10.3390/rs11232781 – ident: ref_113 – volume: 11 start-page: 583 year: 2007 ident: ref_96 article-title: An Overview of Clustering Methods publication-title: Intell. Data Anal. doi: 10.3233/IDA-2007-11602 – ident: ref_108 doi: 10.3390/rs11060615 – volume: 349 start-page: 814 year: 2015 ident: ref_4 article-title: Forest Health and Global Change publication-title: Science doi: 10.1126/science.aac6759 – volume: 79 start-page: 192 year: 2019 ident: ref_9 article-title: Monitoring the Structure of Forest Restoration Plantations with a Drone-Lidar System publication-title: Int. J. Appl. Earth Obs. Geoinf. – ident: ref_45 doi: 10.3390/f7060127 – volume: Volume 3408 start-page: 345 year: 2005 ident: ref_104 article-title: A Probabilistic Interpretation of Precision, Recall and F -Score, with Implication for Evaluation publication-title: Proceedings of the Advances in Information Retrieval doi: 10.1007/978-3-540-31865-1_25 – volume: 130 start-page: 385 year: 2017 ident: ref_37 article-title: Vertical Stratification of Forest Canopy for Segmentation of Understory Trees within Small-Footprint Airborne LiDAR Point Clouds publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.07.001 – ident: ref_11 doi: 10.3390/rs11111271 – volume: 01 start-page: 61 year: 2013 ident: ref_56 article-title: UAV LiDAR for Below-Canopy Forest Surveys publication-title: J. Unmanned Veh. Syst. doi: 10.1139/juvs-2013-0017 – volume: 76 start-page: 701 year: 2010 ident: ref_88 article-title: Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.76.6.701 – volume: 50 start-page: 661 year: 2012 ident: ref_66 article-title: Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2011.2161613 – ident: ref_110 doi: 10.3390/rs12101652 – ident: ref_62 doi: 10.3390/rs12203327 – ident: ref_112 doi: 10.3390/rs12152426 – volume: 23 start-page: 177 year: 2017 ident: ref_31 article-title: Allometric Equations for Integrating Remote Sensing Imagery into Forest Monitoring Programmes publication-title: Glob. Change Biol. doi: 10.1111/gcb.13388 – volume: 161 start-page: 246 year: 2020 ident: ref_50 article-title: Accurate Derivation of Stem Curve and Volume Using Backpack Mobile Laser Scanning publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.01.018 – ident: ref_57 doi: 10.3390/rs9111154 – volume: 78 start-page: 1275 year: 2012 ident: ref_103 article-title: Automated Delineation of Individual Tree Crowns from Lidar Data by Multi-Scale Analysis and Segmentation publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.78.11.1275 – volume: 6 start-page: 3923 year: 2015 ident: ref_67 article-title: Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS publication-title: Forests doi: 10.3390/f6113923 – ident: ref_84 doi: 10.3133/tm11B7 – volume: 39 start-page: 499 year: 2012 ident: ref_24 article-title: Assessing the Feasibility of Uav-Based Lidar for High Resolution Forest Change Detection publication-title: ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – volume: 53 start-page: 193 year: 1998 ident: ref_81 article-title: Determination of terrain models in wooded areas with airborne laser scanner data publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/S0924-2716(98)00009-4 – volume: 52 start-page: 7619 year: 2014 ident: ref_25 article-title: Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2315649 – volume: 144 start-page: 137 year: 2018 ident: ref_42 article-title: International Benchmarking of Terrestrial Laser Scanning Approaches for Forest Inventories publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.06.021 – volume: 409 start-page: 371 year: 2011 ident: ref_3 article-title: Sap Flux Density and Stomatal Conductance of European Beech and Common Oak Trees in Pure and Mixed Stands during the Summer Drought of 2003 publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2011.08.032 – volume: 32 start-page: 494 year: 1998 ident: ref_85 article-title: Triangulated Digital Elevation Model: Definition of a New Representation publication-title: Int. Arch. Photogramm. Remote Sens. – volume: 8 start-page: 83 year: 2004 ident: ref_87 article-title: The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives publication-title: Trans. GIS doi: 10.1111/j.1467-9671.2004.00169.x – volume: 7 start-page: 9975 year: 2015 ident: ref_97 article-title: ATrunk—An ALS-Based Trunk Detection Algorithm publication-title: Remote Sens. doi: 10.3390/rs70809975 – ident: ref_54 doi: 10.3390/rs10111759 – volume: 38 start-page: 1 year: 2018 ident: ref_18 article-title: Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction publication-title: ACM Trans. Graph. doi: 10.1145/3233794 – volume: 30 start-page: 306 year: 2000 ident: ref_33 article-title: Guidelines for Choosing Volume Equations in the Presence of Measurement Error in Height publication-title: Can. J. For. Res. doi: 10.1139/x99-215 – volume: 223 start-page: 34 year: 2019 ident: ref_27 article-title: Individual Mangrove Tree Measurement Using UAV-Based LiDAR Data: Possibilities and Challenges publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.12.034 – ident: ref_63 – ident: ref_79 doi: 10.3390/rs8060501 – volume: 38 start-page: 2938 year: 2017 ident: ref_16 article-title: Updating Residual Stem Volume Estimates Using ALS- and UAV-Acquired Stereo-Photogrammetric Point Clouds publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1219425 – ident: ref_44 doi: 10.3390/rs10111845 – volume: 81 start-page: 281 year: 2015 ident: ref_13 article-title: Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) publication-title: Photogramm. Eng. Remote Sens. doi: 10.14358/PERS.81.4.281 – volume: 63 start-page: 433 year: 2017 ident: ref_52 article-title: Estimation of Diameter at Breast Height from Mobile Laser Scanning Data Collected under a Heavy Forest Canopy publication-title: J. For. Sci. doi: 10.17221/28/2017-JFS – volume: 164 start-page: 41 year: 2020 ident: ref_69 article-title: Under-Canopy UAV Laser Scanning for Accurate Forest Field Measurements publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2020.03.021 – ident: ref_20 doi: 10.2514/6.2009-6113 – volume: 113 start-page: 251 year: 1999 ident: ref_32 article-title: Taper Equations for Eucalyptus Pilularis and Eucalyptus Grandis for the North Coast in New South Wales, Australia publication-title: For. Ecol. Manag. doi: 10.1016/S0378-1127(98)00431-9 – volume: 115 start-page: 22 year: 2016 ident: ref_15 article-title: Remote Sensing Platforms and Sensors: A Survey publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.10.004 – ident: ref_29 – ident: ref_61 doi: 10.3390/rs12081236 – volume: 7 start-page: 6 year: 2016 ident: ref_94 article-title: Machine Learning Algorithms: A Review publication-title: Int. J. Comput. Sci. Inf. Technol. – volume: 8 start-page: 99783 year: 2020 ident: ref_68 article-title: Combining Trunk Detection With Canopy Segmentation to Delineate Single Deciduous Trees Using Airborne LiDAR Data publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2995389 – volume: 32 start-page: 4725 year: 2011 ident: ref_8 article-title: A Review of Methods for Automatic Individual Tree-Crown Detection and Delineation from Passive Remote Sensing publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2010.494184 – volume: 35 start-page: 929 year: 2004 ident: ref_71 article-title: Structuring Laser-Scanned Trees Using 3d Mathematical Morphology publication-title: Nternational Arch. Photogramm. Remote Sens. – volume: 568 start-page: 25 year: 2019 ident: ref_5 article-title: Restoring Natural Forests Is the Best Way to Remove Atmospheric Carbon publication-title: Nature doi: 10.1038/d41586-019-01026-8 – ident: ref_38 doi: 10.3390/s17102371 – volume: 83 start-page: 445 year: 2016 ident: ref_21 article-title: Coverage Path Planning for UAVs Photogrammetry with Energy and Resolution Constraints publication-title: J. Intell. Robot. Syst. doi: 10.1007/s10846-016-0348-x – volume: 117 start-page: 79 year: 2016 ident: ref_78 article-title: Improved Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data in Forested Areas publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2016.03.016 – volume: 52 start-page: 67 year: 2006 ident: ref_40 article-title: Detailed Stem Measurements of Standing Trees from Ground-Based Scanning Lidar publication-title: For. Sci.
SSID	ssj0000331904
Score	2.5869253
Snippet	Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned...
SourceID	doaj liege proquest crossref
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database
StartPage	352
SubjectTerms	Accurate estimation Aircraft detection algorithms Antennas Application contexts Biologie végétale (sciences végétales, sylviculture, mycologie...) canopy Clustering algorithms Computer science data collection Density-Based Spatial Clustering Diameter-at-breast heights Earth sciences & physical geography Ecosystems Engineering, computing & technology Environmental sciences & ecology forest stands Forestry forests HDBSCAN Hierarchical clustering Ingénierie, informatique & technologie LiDAR Life sciences Light detection and ranging Machine learning Omission and commission errors Optical radar PCA Physical, chemical, mathematical & earth Sciences Physique, chimie, mathématiques & sciences de la terre Phytobiology (plant sciences, forestry, mycology...) Point cloud principal component analysis Sciences de la terre & géographie physique Sciences de l’environnement & écologie Sciences du vivant Sciences informatiques Segmentation procedure Semantics Site-specific parameters tree and stand measurements Tree diameter Tree stem segmentation trees UAV Unmanned aerial vehicles (UAV)
Title	Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning
URI	https://www.proquest.com/docview/2551956801 https://orbi.uliege.be/handle/2268/263675 https://doaj.org/article/8b1914ae520a486fa8c63ec6e8ba97f0
Volume	13
WOSCitedRecordID	wos000615480700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9swEBelLWwvY58s6xY0tpfCTGVLkeXHtEvZYAmmbUa3FyHLpy4QnJGPQl_6t-9OdrvABnvZizC2LMT9pNMduvsdY-9z9I11JmQCMIBEee0T4wgQqWVaFw71ZltsIp9MzOVlUW6V-qKYsJYeuBXckamIgczBIBNOGR2c8VqC12AqV-QheusiL7acqaiDJS4toVo-Uol-_dFyRdpaxAyjrRMoEvWjYTqnK-o_9HE8ZE4fs0eddciH7ayesB1onrIHXaHyHzfP2HSEW5KMzOaKU1XN1ZqfRwbYzRI4pYrw6fBrMqYCEFDzL7OPwzNeLmbNmp_MF5uaxwgBPo4RlMA7ctWr52x6Oro4-ZR0lRESr3K9Tgpf1Xj0pkHKOjXO59IH1BTKBeeFCpmTTqqgwRVKB-Fy0KZCU6zS2gedZ7V8wXabRQMvGa-dLwSArohnR2npEC8cqIYAaCoK3WOHd9KyvqMNp-oVc4vuA0nW_pZsj7277_uzJcv4a69jEvp9DyK4ji8QdtvBbv8Fe499iJDhT9XMXmdxlPi8meMo3lZg0bI0NtMSfaIee3uHrMX9Q5ciroHFZmXRpYopkyJ99T_mdcAeZhT1IlLUP6_ZLi4BeMP2_fV6tlr22d7xaFKe9eNi7VOc6Tm1tyNsy8F3_F5-HpfffgGOFfHG
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+Forest+Structure+from+UAV-Mounted+LiDAR+Point+Cloud+Using+Machine+Learning&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Neuville%2C+Romain&rft.au=Bates%2C+Jordan+Steven&rft.au=Jonard%2C+Fran%C3%A7ois&rft.date=2021-01-20&rft.issn=2072-4292&rft.eissn=2072-4292&rft.volume=13&rft.issue=3&rft_id=info:doi/10.3390%2Frs13030352&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon