Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management
Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few h...
Uloženo v:
| Vydáno v: | Forests Ročník 13; číslo 1; s. 3 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.01.2022
|
| Témata: | |
| ISSN: | 1999-4907, 1999-4907 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed. |
|---|---|
| AbstractList | Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed. |
| Author | Smith, Alistair M.S. Sparks, Aaron M. |
| Author_xml | – sequence: 1 givenname: Aaron M. orcidid: 0000-0003-1286-3770 surname: Sparks fullname: Sparks, Aaron M. – sequence: 2 givenname: Alistair M.S. orcidid: 0000-0003-0071-9958 surname: Smith fullname: Smith, Alistair M.S. |
| BookMark | eNptkctO3TAQhi0EUill0TewxIYuUnzJzctwgBbpVCAu68ixJ2CU2MEXpPM2fdQaToUq1NXM4vs__Zr5jHats4DQV0q-cy7IyUg5oYQQvoP2qRCiKAVpdv_ZP6HDEJ4yQaqmFazcR787pZKXaoPdiCVem7PupjiVATS-tNq8GJ3khO88AD6DCCoaZ7G0GncxejOkCPgXyJA8zGAj7qYH5018nDP9ApNbsie6rBqdn_GF8xAivvZOJxUDvk3LMm3w6lGarfQGgkteZae08uFN-QXtjXIKcPh3HqD7i_O71c9iffXjctWtC8VZFYtRE9XKshyIUgzoQBjUDciGVlSPgsuq0RUQ3uqGMEFFIzgjeoCyGsq2hbHiB-h46128e065Zj-boGCapAWXQs9qXtc1J4Rl9OgD-pRb29wuU4yyltZlk6mTLaW8C8HD2CsT5ev9opdm6inpX5_Wvz8tJ759SCzezNJv_sP-AYA-mRU |
| CitedBy_id | crossref_primary_10_1109_JSTARS_2025_3528834 crossref_primary_10_33764_2411_1759_2025_30_4_32_41 crossref_primary_10_1093_forsci_fxad047 crossref_primary_10_3390_app14114479 crossref_primary_10_3390_s22228858 crossref_primary_10_3390_rs15030664 crossref_primary_10_3390_f16050784 crossref_primary_10_3390_rs17111934 crossref_primary_10_3390_f15081462 crossref_primary_10_3390_rs17132245 crossref_primary_10_1016_j_foreco_2023_121246 crossref_primary_10_3390_rs14112567 crossref_primary_10_1016_j_rsase_2025_101535 crossref_primary_10_1016_j_rse_2025_115007 crossref_primary_10_3390_f16050815 crossref_primary_10_3390_rs14143480 crossref_primary_10_3390_rs17101761 |
| Cites_doi | 10.1080/07038992.2016.1196582 10.3390/rs11070819 10.3390/rs8040333 10.1371/journal.pcbi.1009180 10.14358/PERS.70.3.351 10.3390/rs12050885 10.3390/f4010001 10.1016/j.rse.2020.112061 10.14214/sf.203 10.1080/01431160902882561 10.1016/j.isprsjprs.2018.02.002 10.1029/2008JG000748 10.1071/WF15130 10.1080/07038992.2016.1207484 10.1016/j.foreco.2016.11.041 10.1109/JSTARS.2012.2196978 10.1080/01431160701736471 10.3390/f9050268 10.3390/rs3030638 10.3390/rs3112494 10.1016/j.compag.2020.105815 10.3390/f12050550 10.3390/rs1040934 10.1016/j.rse.2012.02.023 10.3390/rs6043475 10.3390/rs11091086 10.14358/PERS.70.5.589 10.1139/cjfr-2018-0196 10.1139/X09-183 10.5589/m08-048 10.5589/m06-007 10.1016/j.rse.2014.03.038 10.1016/j.rse.2012.03.027 10.1071/WF16139 10.3390/rs1040776 10.1029/2007JG000544 10.1080/01431160701736489 10.1007/s40725-017-0051-6 10.5589/m08-056 10.1016/j.rse.2021.112540 10.1016/j.rse.2009.07.010 10.5589/m08-055 10.1080/01431160500444764 10.1080/01431160701422213 10.1080/07038992.2016.1232587 10.1093/treephys/25.7.903 10.1109/JSTARS.2019.2942811 10.3390/su12114508 10.1139/cjfr-2020-0424 10.1016/j.isprsjprs.2006.10.006 10.1016/j.foreco.2021.119155 10.3390/f5071682 10.1093/forestry/cpr051 10.5589/m06-030 10.5589/m06-005 10.1080/01431161.2021.1956699 10.1139/x11-117 10.14358/PERS.80.7.627 10.1016/S0378-1127(97)00019-4 10.14358/PERS.78.1.75 10.1016/j.rse.2017.09.037 10.1080/01431161.2016.1214302 10.1109/36.921414 10.1016/j.foreco.2017.09.019 10.1139/x04-055 10.1016/j.rse.2009.02.002 10.1080/01431161.2015.1030043 10.1016/j.rse.2016.12.022 10.1016/j.isprsjprs.2015.03.014 10.1016/S0034-4257(02)00050-0 10.1016/j.foreco.2020.118619 10.1016/j.rse.2010.01.016 10.3390/rs2040968 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SN 7SS 7X2 8FE 8FH 8FK ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BHPHI BKSAR C1K CCPQU DWQXO GNUQQ HCIFZ M0K PATMY PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS PYCSY 7S9 L.6 |
| DOI | 10.3390/f13010003 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Ecology Abstracts Entomology Abstracts (Full archive) Agricultural Science Collection ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials - QC ProQuest Central Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea ProQuest Central Student SciTech Premium Collection Agricultural Science Database Environmental Science Database Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Environmental Science Collection AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Sustainability Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection ProQuest Central (New) ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database Agricultural Science Collection ProQuest SciTech Collection Ecology Abstracts Environmental Science Collection Entomology Abstracts ProQuest One Academic UKI Edition Environmental Science Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Agricultural Science Database CrossRef AGRICOLA |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Forestry |
| EISSN | 1999-4907 |
| ExternalDocumentID | 10_3390_f13010003 |
| GeographicLocations | United States--US Idaho |
| GeographicLocations_xml | – name: Idaho – name: United States--US |
| GroupedDBID | 2XV 5VS 7X2 7XC 8FE 8FH AADQD AAFWJ AAHBH AAYXX ADBBV AENEX AEUYN AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ATCPS BANNL BCNDV BENPR BHPHI BKSAR CCPQU CITATION ECGQY EDH HCIFZ IAG IAO IEP ITC ITG ITH KQ8 LK5 M0K M7R MODMG M~E OK1 PATMY PCBAR PHGZM PHGZT PIMPY PROAC PYCSY TR2 3V. 7SN 7SS 8FK ABUWG AZQEC C1K DWQXO GNUQQ OZF PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6 |
| ID | FETCH-LOGICAL-c325t-fd0c8a44b0cc2e1b02e67ea7151df93a57d5e038d70291979320dbe45b488ef53 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000746387600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1999-4907 |
| IngestDate | Sun Nov 09 14:40:19 EST 2025 Sun Nov 09 06:00:43 EST 2025 Sat Nov 29 07:09:00 EST 2025 Tue Nov 18 21:47:37 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c325t-fd0c8a44b0cc2e1b02e67ea7151df93a57d5e038d70291979320dbe45b488ef53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0071-9958 0000-0003-1286-3770 |
| OpenAccessLink | https://www.proquest.com/docview/2621281647?pq-origsite=%requestingapplication% |
| PQID | 2621281647 |
| PQPubID | 2032398 |
| ParticipantIDs | proquest_miscellaneous_2636663002 proquest_journals_2621281647 crossref_citationtrail_10_3390_f13010003 crossref_primary_10_3390_f13010003 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-01-01 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Forests |
| PublicationYear | 2022 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Shifley (ref_44) 1993; 39 Silva (ref_51) 2021; 491 Smith (ref_84) 2016; 25 Falkowski (ref_16) 2006; 32 ref_57 Holopainen (ref_10) 2014; 5 Popescu (ref_15) 2004; 16 ref_53 Maltamo (ref_72) 2004; 34 Vastaranta (ref_59) 2014; 6 Shi (ref_78) 2018; 137 (ref_79) 2009; 113 Yadav (ref_26) 2021; 42 Smith (ref_5) 2008; 34 White (ref_3) 2016; 42 Goodbody (ref_8) 2021; 51 Garrity (ref_34) 2008; 34 Bokalo (ref_65) 2013; 4 Yin (ref_55) 2016; 37 Kim (ref_80) 2009; 113 Shi (ref_25) 2021; 98 Andersen (ref_20) 2006; 32 ref_69 Tinkham (ref_7) 2018; 48 ref_68 Lindberg (ref_30) 2017; 3 Smith (ref_12) 2008; 34 ref_62 Hoover (ref_66) 2017; 385 Pham (ref_24) 2016; 50 Wang (ref_36) 2004; 70 ref_29 Leckie (ref_4) 2008; 29 Hyyppa (ref_11) 1999; 16 Yao (ref_75) 2012; 123 Dinuls (ref_23) 2012; 5 Mokros (ref_52) 2021; 104 Robison (ref_63) 2004; 25 Yancho (ref_73) 2019; 12 Edson (ref_28) 2011; 3 North (ref_45) 2017; 405 Lee (ref_37) 2010; 31 Budei (ref_27) 2018; 204 Vauhkonen (ref_70) 2012; 85 Smith (ref_21) 2008; 113 Eitel (ref_64) 2007; 28 Hudak (ref_47) 2012; 123 Tinkham (ref_14) 2016; 42 ref_31 Evans (ref_1) 2009; 1 Hyyppa (ref_71) 2001; 39 Holmgren (ref_22) 2008; 29 Bagdon (ref_67) 2021; 480 Smith (ref_86) 2014; 154 Falkowski (ref_9) 2008; 34 Souza (ref_42) 2020; 179 Mellor (ref_74) 2015; 105 Zhen (ref_39) 2015; 36 Swayze (ref_50) 2021; 263 Vauhkonen (ref_76) 2010; 114 Jeronimo (ref_17) 2018; 116 ref_82 Poznanovic (ref_19) 2014; 80 Robinson (ref_61) 2005; 25 Tinkham (ref_56) 2011; 3 Li (ref_60) 2012; 10 ref_46 Sparks (ref_85) 2017; 26 Falkowski (ref_6) 2010; 40 Silva (ref_58) 2016; 42 ref_87 ref_41 ref_40 Roussel (ref_54) 2020; 251 Hudak (ref_2) 2009; 1 McCarley (ref_48) 2017; 191 Korzukhin (ref_13) 1997; 97 Smith (ref_83) 2009; 35 ref_49 Strand (ref_33) 2008; 113 Li (ref_43) 2011; 41 Maltamo (ref_81) 2009; 43 Hudak (ref_18) 2006; 32 Gupta (ref_38) 2010; 2 Brandtberg (ref_77) 2007; 61 Strand (ref_32) 2006; 27 Pouliot (ref_35) 2002; 82 |
| References_xml | – volume: 34 start-page: S3 year: 2008 ident: ref_5 article-title: Preface: Special issue on the Remote Characterization of Vegetation Structure and Productivity: Plant to Landscape Scales publication-title: Can. J. Remote Sens. – volume: 25 start-page: 903 year: 2004 ident: ref_63 article-title: Model validation using equivalence tests publication-title: Ecol. Model. – volume: 42 start-page: 554 year: 2016 ident: ref_58 article-title: Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2016.1196582 – ident: ref_41 doi: 10.3390/rs11070819 – ident: ref_29 doi: 10.3390/rs8040333 – ident: ref_87 doi: 10.1371/journal.pcbi.1009180 – volume: 50 start-page: 187 year: 2016 ident: ref_24 article-title: Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 70 start-page: 351 year: 2004 ident: ref_36 article-title: Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery publication-title: Photogram. Eng. Remote Sens. doi: 10.14358/PERS.70.3.351 – ident: ref_49 doi: 10.3390/rs12050885 – volume: 4 start-page: 1 year: 2013 ident: ref_65 article-title: The validation of the mixedwood growth model (MGM) for use in forest management decision making publication-title: Forests doi: 10.3390/f4010001 – volume: 116 start-page: 336 year: 2018 ident: ref_17 article-title: Applying LiDAR Individual Tree Detection to Management of Structurally Diverse Forest Landscapes publication-title: J. For. – volume: 251 start-page: 112061 year: 2020 ident: ref_54 article-title: lidR: An R package for analysis of Airborne Laser Scanning (ALS) data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112061 – ident: ref_68 – volume: 43 start-page: 203 year: 2009 ident: ref_81 article-title: Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data publication-title: Silva Fenn. doi: 10.14214/sf.203 – volume: 31 start-page: 117 year: 2010 ident: ref_37 article-title: Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests publication-title: Int. J. Remote Sens. doi: 10.1080/01431160902882561 – volume: 137 start-page: 163 year: 2018 ident: ref_78 article-title: Important LiDAR metrics for discriminating forest tree species in Central Europe publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.02.002 – volume: 113 start-page: 3 year: 2008 ident: ref_21 article-title: Introduction to Special Section: The Remote Characterization of Vegetation Structure: New methods and applications to landscape-regional-global scale processes publication-title: J. Geophys. Res. doi: 10.1029/2008JG000748 – volume: 25 start-page: 158 year: 2016 ident: ref_84 article-title: Towards a new paradigm in fire severity research using dose-response experiments publication-title: Int. J. Wildland Fire doi: 10.1071/WF15130 – volume: 42 start-page: 619 year: 2016 ident: ref_3 article-title: Remote sensing technologies for enhancing forest inventories: A review publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2016.1207484 – volume: 385 start-page: 236 year: 2017 ident: ref_66 article-title: Equivalence of live tree carbon stocks produced by three estimation approaches for forests of the western United States publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2016.11.041 – volume: 5 start-page: 594 year: 2012 ident: ref_23 article-title: Tree Species Identification in Mixed Baltic Forest Using LiDAR and Multispectral Data publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2012.2196978 – volume: 29 start-page: 1537 year: 2008 ident: ref_22 article-title: Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701736471 – ident: ref_40 doi: 10.3390/f9050268 – volume: 3 start-page: 638 year: 2011 ident: ref_56 article-title: A comparison of two open source lidar surface filtering algorithms publication-title: Remote Sens. doi: 10.3390/rs3030638 – volume: 3 start-page: 2494 year: 2011 ident: ref_28 article-title: Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements publication-title: Remote Sens. doi: 10.3390/rs3112494 – volume: 179 start-page: 105815 year: 2020 ident: ref_42 article-title: Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2020.105815 – ident: ref_31 doi: 10.3390/f12050550 – volume: 1 start-page: 934 year: 2009 ident: ref_2 article-title: Review: LiDAR Utility for Natural Resource Managers publication-title: Remote Sens. doi: 10.3390/rs1040934 – volume: 123 start-page: 25 year: 2012 ident: ref_47 article-title: Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2012.02.023 – volume: 6 start-page: 3475 year: 2014 ident: ref_59 article-title: Multisource single-tree inventory in the prediction of tree quality variables and logging recoveries publication-title: Remote Sens. doi: 10.3390/rs6043475 – ident: ref_53 doi: 10.3390/rs11091086 – volume: 16 start-page: 589 year: 2004 ident: ref_15 article-title: Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height publication-title: Photogram. Eng. Remote Sens. doi: 10.14358/PERS.70.5.589 – volume: 48 start-page: 1251 year: 2018 ident: ref_7 article-title: Applications of the United States Forest Service Forest Inventory and Analysis dataset: A review and future directions publication-title: Can. J. For. Res. doi: 10.1139/cjfr-2018-0196 – ident: ref_69 – volume: 40 start-page: 184 year: 2010 ident: ref_6 article-title: Landscape-scale parameterization of a tree-level forest growth model: A k-NN imputation approach incorporating LiDAR data publication-title: Can. J. For. Res. doi: 10.1139/X09-183 – volume: 34 start-page: S268 year: 2008 ident: ref_12 article-title: Pro-duction of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multi-temporal aerial photographs publication-title: Can. J. Remote Sens. doi: 10.5589/m08-048 – volume: 32 start-page: 126 year: 2006 ident: ref_18 article-title: Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data publication-title: Can. J. Remote Sens. doi: 10.5589/m06-007 – volume: 154 start-page: 322 year: 2014 ident: ref_86 article-title: Remote Sensing the Vulnerability of Vegetation in Natural Terrestrial Ecosystems publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2014.03.038 – volume: 123 start-page: 368 year: 2012 ident: ref_75 article-title: Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2012.03.027 – volume: 26 start-page: 95 year: 2017 ident: ref_85 article-title: Impacts of fire radiative flux on mature Pinus ponderosa growth and vulnerability to secondary mortality agents publication-title: Int. J. Wildland Fire doi: 10.1071/WF16139 – volume: 1 start-page: 776 year: 2009 ident: ref_1 article-title: Discrete Return lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables publication-title: Remote Sens. doi: 10.3390/rs1040776 – ident: ref_62 – volume: 113 start-page: G01013 year: 2008 ident: ref_33 article-title: Net Changes in Above Ground Woody Carbon Stock in Western Juniper Woodlands, 1946–1998 publication-title: J. Geophys. Res. doi: 10.1029/2007JG000544 – volume: 29 start-page: 1339 year: 2008 ident: ref_4 article-title: Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701736489 – volume: 3 start-page: 19 year: 2017 ident: ref_30 article-title: Individual tree crown methods for 3D data from remote sensing publication-title: Curr. For. Rep. doi: 10.1007/s40725-017-0051-6 – volume: 34 start-page: S376 year: 2008 ident: ref_34 article-title: Automatic detection of shrub location, crown area, and cover using spatial wavelet analysis and aerial photography publication-title: Can. J. Remote Sens. doi: 10.5589/m08-056 – volume: 39 start-page: 776 year: 1993 ident: ref_44 article-title: A generalized methodology for estimating forest ingrowth at multiple threshold diameters publication-title: For. Sci. – volume: 263 start-page: 112540 year: 2021 ident: ref_50 article-title: Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2021.112540 – volume: 113 start-page: 2499 year: 2009 ident: ref_80 article-title: Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.07.010 – volume: 34 start-page: S338 year: 2008 ident: ref_9 article-title: The influence of conifer forest canopy cover upon the accuracy of two individual tree measurement algorithms using lidar data publication-title: Can. J. Remote Sens. doi: 10.5589/m08-055 – volume: 27 start-page: 2049 year: 2006 ident: ref_32 article-title: Wavelet estimation of plant spatial patterns in multi-temporal aerial photography publication-title: Int. J. Remote Sens. doi: 10.1080/01431160500444764 – volume: 28 start-page: 4183 year: 2007 ident: ref_64 article-title: Using in-situ spectroradiometery to evaluate new RapidEye satellite data for prediction of wheat nitrogen status publication-title: Int. J. Remote Sens. doi: 10.1080/01431160701422213 – ident: ref_82 – volume: 42 start-page: 400 year: 2016 ident: ref_14 article-title: Development of height-volume relationships in second growth Abies grandis for use with aerial LiDAR publication-title: Can. J. Remote Sens. doi: 10.1080/07038992.2016.1232587 – volume: 25 start-page: 903 year: 2005 ident: ref_61 article-title: A regression-based equivalence test for model validation: Shifting the burden of proof publication-title: Tree Physiol. doi: 10.1093/treephys/25.7.903 – volume: 12 start-page: 4131 year: 2019 ident: ref_73 article-title: Fine-Scale Spatial and Spectral Clustering of UAV-Acquired Digital Aerial Photogrammetric (DAP) Point Clouds for Individual Tree Crown Detection and Segmentation publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2019.2942811 – ident: ref_46 doi: 10.3390/su12114508 – volume: 51 start-page: 972 year: 2021 ident: ref_8 article-title: Airborne laser scanning for quantifying criteria and indicators of sustainable forest management in Canada publication-title: Can. J. For. Res. doi: 10.1139/cjfr-2020-0424 – volume: 61 start-page: 325 year: 2007 ident: ref_77 article-title: Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2006.10.006 – volume: 491 start-page: 119155 year: 2021 ident: ref_51 article-title: Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2021.119155 – volume: 5 start-page: 1682 year: 2014 ident: ref_10 article-title: Outlook for the next generation’s precision forestry in Finland publication-title: Forests doi: 10.3390/f5071682 – volume: 85 start-page: 27 year: 2012 ident: ref_70 article-title: Comparative testing of single-tree detection algorithms under different types of forest publication-title: Forestry doi: 10.1093/forestry/cpr051 – volume: 32 start-page: 355 year: 2006 ident: ref_20 article-title: A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods publication-title: Can. J. Remote Sens. doi: 10.5589/m06-030 – volume: 16 start-page: 27 year: 1999 ident: ref_11 article-title: Detecting and estimating attributes for single tree using laser scanner publication-title: Photogramm. J. Finl. – volume: 32 start-page: 153 year: 2006 ident: ref_16 article-title: Automated estimation of individual conifer tree height and crown diameter via Two-dimensional spatial wavelet analysis of lidar data publication-title: Can. J. Remote Sens. doi: 10.5589/m06-005 – volume: 42 start-page: 7952 year: 2021 ident: ref_26 article-title: Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.1956699 – volume: 41 start-page: 10 year: 2011 ident: ref_43 article-title: Modeling annualized occurrence, frequency, and composition of ingrowth using mixed-effects zero-inflated models and permanent plots in the Acadian Forest Region of North America publication-title: Can. J. For. Res. doi: 10.1139/x11-117 – volume: 98 start-page: 102311 year: 2021 ident: ref_25 article-title: Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 80 start-page: 45 year: 2014 ident: ref_19 article-title: An accuracy assessment of tree detection algorithms in juniper woodlands publication-title: Photogram. Eng. Remote Sens. doi: 10.14358/PERS.80.7.627 – volume: 97 start-page: 1 year: 1997 ident: ref_13 article-title: Biomass equations for sixty-five North American tree species publication-title: For. Ecol. Manag. doi: 10.1016/S0378-1127(97)00019-4 – volume: 10 start-page: 75 year: 2012 ident: ref_60 article-title: A New Method for Segmenting Individual Trees from the Lidar Point Cloud publication-title: Photogram. Eng. Remote Sens. doi: 10.14358/PERS.78.1.75 – volume: 204 start-page: 632 year: 2018 ident: ref_27 article-title: Identifying the genus or species of individual trees using a three-wavelength airborne lidar system publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2017.09.037 – volume: 37 start-page: 4521 year: 2016 ident: ref_55 article-title: How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1214302 – volume: 39 start-page: 969 year: 2001 ident: ref_71 article-title: A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.921414 – volume: 405 start-page: 166 year: 2017 ident: ref_45 article-title: Cover of tall trees best predicts California spotted owl habitat publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2017.09.019 – volume: 34 start-page: 1791 year: 2004 ident: ref_72 article-title: The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve publication-title: Can. J. For. Res. doi: 10.1139/x04-055 – volume: 113 start-page: 1163 year: 2009 ident: ref_79 article-title: Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2009.02.002 – volume: 35 start-page: 447 year: 2009 ident: ref_83 article-title: A cross-comparison of field, spectral, and lidar estimates of forest canopy cover publication-title: Can. J. For. Res. – volume: 36 start-page: 1965 year: 2015 ident: ref_39 article-title: Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2015.1030043 – volume: 191 start-page: 419 year: 2017 ident: ref_48 article-title: Multi-temporal LiDAR and Landsat quantification of fire induced changes to forest structure publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2016.12.022 – volume: 104 start-page: 102512 year: 2021 ident: ref_52 article-title: Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 105 start-page: 155 year: 2015 ident: ref_74 article-title: Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.03.014 – volume: 82 start-page: 322 year: 2002 ident: ref_35 article-title: Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration publication-title: Remote Sens. Environ. doi: 10.1016/S0034-4257(02)00050-0 – ident: ref_57 – volume: 480 start-page: 118619 year: 2021 ident: ref_67 article-title: A model evaluation framework applied to the Forest Vegetation Simulator (FVS) in Colorado and Wyoming lodgepole pine forests publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2020.118619 – volume: 114 start-page: 1263 year: 2010 ident: ref_76 article-title: Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.01.016 – volume: 2 start-page: 968 year: 2010 ident: ref_38 article-title: Comparative analysis of clustering-based approaches for 3-D single tree detection using airborne fullwave LiDAR data publication-title: Remote Sens. doi: 10.3390/rs2040968 |
| SSID | ssj0000578924 |
| Score | 2.3440964 |
| Snippet | Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 3 |
| SubjectTerms | Accuracy Airborne lasers Airborne sensing Algorithms Biomass Canopies canopy Coniferous forests data collection Datasets Diameters Drones forest inventory Forest products Forestry Forests Landowners Lasers Lidar Plant species Proprietary Recall Remote sensing Resource management Software Species classification supply chain supply chain management Supply chains tree and stand measurements Trees Unmanned aerial vehicles |
| Title | Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management |
| URI | https://www.proquest.com/docview/2621281647 https://www.proquest.com/docview/2636663002 |
| Volume | 13 |
| WOSCitedRecordID | wos000746387600001&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: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: M0K dateStart: 20100301 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: PCBAR dateStart: 20100301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: PATMY dateStart: 20100301 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: BENPR dateStart: 20100301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1999-4907 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000578924 issn: 1999-4907 databaseCode: PIMPY dateStart: 20100301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZoi1AvvBELpRoQBy5Ws3YSJyeUtltRwa6iqkjlFDn2hK5UknY3i9QLv4WfyjjxZlUJceGSQzJyLM143p6PsfdxasmOhWNeknHmoTQhT8jr54mOI1TWRjY1HdiEms2Si4s09wm3pW-rXOvETlHbxrgc-YGIhSv6xKH6eH3DHWqUq656CI0ttuMmlZGc7xxOZvnZkGUhbyShCKMfKSQpvj-oSGm7nLa8a4ju6uHOuJw8-t9tPWYPvVsJWS8HT9g9rJ-yBw5304G5PWO_M2NWC21uoalAw5f5cXbGD8mCWTgdrmTB-QIRjrHt2rNq0LWFrO0hsRCmm2wiZFffaRPt5Q_wTUe0TttAf7cJ-t9C3g-TXUKHHHoLR5d63i-6rhnApvnmOft6Mjk_-sQ9OAM3UkQtr2xgEh2GZWCMwHEZCIwVakUehK1SqSNlIwxkYlUg0nFKakAEtsQwKkllYBXJF2y7bmp8yaBSSkeGnBFr0hB1XJZ2rAymwmCl41iN2Ic1pwrjJ5c7AI2rgiIYx9RiYOqIvRtIr_txHX8j2lvzsvAndllsGDlib4fPdNZcAUXX2KwcjaRoT5IRefXvJV6zXeGuSXSpmj223S5W-IbdNz_b-XKx74V0n21Ng8_u-WtC7_LTaf7tDymz94E |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB1VKYJe-EYECgwIJC5WnV3bax8QMg1VoyZRhIJUTma9u6aRWrskDij_hl_Ab2TWX1ElxK0Hzl6tZft53szszDyA10Gkice8gZMSOTseV54TktfvhDLwjdDa15GqxCbEdBqenkazHfjd9sLYssrWJlaGWhfK5sgPWMDsoU_gifeX3x2rGmVPV1sJjRoWJ2bzk0K21bvRkL7vG8aOPs4Pj51GVcBRnPmlk2lXhdLzUlcpZgapy0wgjBREfTqLuPSF9o3LQy1cFg0iwi9zdWo8PyWsm8yqRJDJ3_Us2HuwOxtNZl-6rA55PyFFNPUII84j9yAjkrA5dH6V-K7a_YrMju78b6_hLtxu3GaMa5zfgx2T34ebVlfUitU9gF-xUuulVBssMpQ4XgzjT84HYmiNo67lDOdLY3Boyqr8LEeZa4zLWvLL4GSbLcX4_Bs9dHl2gU1RFe1TFlj3bmF9W5zVw3JXWCmjbvDwTC7qTdszEdwWFz2Ez9fyfh5BLy9y8xgwE0L6ipwtrSLPyCBN9UAoEzFlMhkEog9vW2QkqpnMbgVCzhOK0CyIkg5EfXjVLb2sx5H8bdF-i52ksUirZAucPrzsLpMtsQdEMjfF2q7hFM1yIskn_97iBdw6nk_GyXg0PXkKe8y2hFRpqX3olcu1eQY31I9ysVo-b34QhK_XDcY_-fhQhw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VKaq48EYNFBgQSFysOOvHeg8ImYaIqG1koSKVk1nvrmmkYpfEAeXf8Dv4dcz6FVVC3Hrg7NVatj_PNzM7Mx_Ay1Bo4jF_7GREzo7vKd-JyOt3IhkGhmsdaKFqsQk-n0dnZyLZgd9dL4wtq-xsYm2odalsjnzEQmYPfUKfj_K2LCKZTN9efnesgpQ9ae3kNBqIHJnNTwrfVm9mE_rWrxibvj89_OC0CgOO8lhQObl2VSR9P3OVYmacucyE3EhONKhz4cmA68C4XqS5y8RYEJaZqzPjBxnh3uRWMYLM_y655D4bwG4yO0k-9xke8oQiim6acUaeJ9xRToRh8-neVRK8ygE1sU1v_8-v5A7cat1pjBv834UdU9yDPas3akXs7sOvWKn1UqoNljlKPF5M4o_OO2JujbO-FQ1Pl8bgxFR1WVqBstAYV40UmMGTbRYV44uv9NDV-Tdsi61on6rEpqcLm9ti0gzRXWGtmLrBw3O5aDbtzkpwW3T0AD5dy_t5CIOiLMw-YM65DBQ5YVoJ38gwy_SYKyOYMrkMQz6E1x1KUtVObLfCIRcpRW4WUGkPqCG86JdeNmNK_rbooMNR2lqqVboF0RCe95fJxtiDI1mYcm3XeBTlekSej_69xTPYIwSmx7P50WO4yWynSJ2tOoBBtVybJ3BD_agWq-XT9l9B-HLdWPwDjnBZRw |
| 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=Accuracy+of+a+LiDAR-Based+Individual+Tree+Detection+and+Attribute+Measurement+Algorithm+Developed+to+Inform+Forest+Products+Supply+Chain+and+Resource+Management&rft.jtitle=Forests&rft.au=Sparks%2C+Aaron+M&rft.au=Smith%2C+Alistair+M.S.&rft.date=2022-01-01&rft.issn=1999-4907&rft.eissn=1999-4907&rft.volume=13&rft.issue=1&rft_id=info:doi/10.3390%2Ff13010003&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1999-4907&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1999-4907&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1999-4907&client=summon |