| 1 |
Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson CB, Sinca F, Vaughn NR, Llactayo W. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science, 2017, 355: 385-389,
DOI
|
| 2 |
Balenović I, Liang X, Jurjević L, Hyyppä J, Seletković A, Kukko A. Hand-held personal laser scanning: current status and perspectives for forest inventory application. Croat J for Eng, 2021, 42: 165-183,
DOI
|
| 3 |
Beland M, Parker G, Sparrow B, Harding D, Chasmer L, Phinn S, Antonarakis A, Strahler A. On promoting the use of lidar systems in forest ecosystem research. For Ecol Manag, 2019, 450,
DOI
|
| 4 |
Bravo-Oviedo A, Pretzsch H, Ammer C, Andenmatten E, Barbati A, Barreiro S, Brang P, Bravo F, Coll L, Corona P, Den Ouden J, Ducey MJ, Forrester DI, Giergiczny M, Jacobsen JB, Lesinski J, Löf M, Mason WL, Matovic B, Metslaid M, Morneau F, Motiejunaite J, O’Reilly C, Pach M, Ponette Q, Del Rio M, Short I, Skovsgaard JP, Soliño M, Spathelf P, Sterba H, Stojanovic D, Strelcova K, Svoboda M, Verheyen K, Von Lüpke N, Zlatanov T. European Mixed Forests: definition and research perspectives. Forest Syst, 2014, 23: 518,
DOI
|
| 5 |
Cabo C, Del Pozo S, Rodríguez-Gonzálvez P, Ordóñez C, González-Aguilera D. Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for individual tree modeling at plot level. Remote Sens, 2018, 10: 540,
DOI
|
| 6 |
|
| 7 |
Chamberlain CP, Kane VR, Case MJ. Accelerating the development of structural complexity: lidar analysis supports restoration as a tool in coastal Pacific Northwest forests. For Ecol Manag, 2021, 500,
DOI
|
| 8 |
Chen SL, Liu HY, Feng ZK, Shen CY, Chen PP. Applicability of personal laser scanning in forestry inventory. PLoS ONE, 2019, 14,
DOI
|
| 9 |
Chiappini S, Pierdicca R, Malandra F, Tonelli E, Malinverni ES, Urbinati C, Vitali A (2022) Comparing Mobile Laser Scanner and manual measurements for dendrometric variables estimation in a black pine ( Pinus nigra Arn.) plantation. Comput Electron Agr 198:107069. https://doi.org/10.1016/j.compag.2022.107069
|
| 10 |
Davison S, Donoghue DNM, Galiatsatos N. The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity. Int J Appl Earth Obs, 2020, 92,
DOI
|
| 11 |
Del Perugia B, Giannetti F, Chirici G, Travaglini D. Influence of scan density on the estimation of single-tree attributes by hand-held mobile laser scanning. Forests, 2019, 10: 277,
DOI
|
| 12 |
Del Río M, Pretzsch H, Alberdi I, Bielak K, Bravo F, Brunner A, Condés S, Ducey MJ, Fonseca T, Von Lüpke N, Pach M, Peric S, Perot T, Souidi Z, Spathelf P, Sterba H, Tijardovic M, Tomé M, Vallet P, Bravo-Oviedo A. Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives. Eur J Forest Res, 2016, 135: 23-49,
DOI
|
| 13 |
Disney MI, Boni Vicari M, Burt A, Calders K, Lewis SL, Raumonen P, Wilkes P. Weighing trees with lasers: advances, challenges and opportunities. Interface Focus, 2018, 8: 20170048,
DOI
|
| 14 |
Dubayah R, Armston J, Healey SP, Bruening JM, Patterson PL, Kellner JR, Duncanson L, Saarela S, Ståhl G, Yang Z, others (2022) GEDI launches a new era of biomass inference from space. Environ Res Lett 17:095001. https://iopscience.iop.org/article/ https://doi.org/10.1088/1748-9326/ac8694
|
| 15 |
Ehbrecht M, Schall P, Ammer C, Seidel D. Quantifying stand structural complexity and its relationship with forest management, tree species diversity, and microclimate. Agri For Meteorol, 2017, 242: 1-9,
DOI
|
| 16 |
|
| 17 |
Fan WW, Liu HR, Xu YS, Lin WS. Comparison of estimation algorithms for individual tree diameter at breast height based on hand-held mobile laser scanning. Scand J For Res, 2021, 36: 460-473,
DOI
|
| 18 |
Fisher A, Armston J, Goodwin N, Scarth P. Modelling canopy gap probability, foliage projective cover, and crown projective cover from airborne lidar metrics in Australian forests and woodlands. Remote Sens Environ, 2020, 237,
DOI
|
| 19 |
Giannetti F, Puletti N, Quatrini V, Travaglini D, Bottalico F, Corona P, Chirici G. Integrating terrestrial and airborne laser scanning for the assessment of single-tree attributes in Mediterranean forest stands. Eur J Remote Sens, 2018, 51: 795-807,
DOI
|
| 20 |
Gollob C, Ritter T, Nothdurft A. Comparison of 3D point clouds obtained by terrestrial laser scanning and personal laser scanning on forest inventory sample plots. Data, 2020, 5: 103,
DOI
|
| 21 |
Gonzalez de Tanago J, Lau A, Bartholomeus H, Herold M, Avitabile V, Raumonen P, Martius C, Goodman RC, Disney M, Manuri S, Burt A, Calders K. Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol Evol, 2018, 9: 223-234,
DOI
|
| 22 |
Hyyppä E, Yu X, Kaartinen H, Hakala T, Kukko A, Vastaranta M, Hyyppä J. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV laser scanning for field reference data collection in boreal forests. Remote Sens, 2020, 12: 3327,
DOI
|
| 23 |
|
| 24 |
Jayathunga S, Owari T, Tsuyuki S. Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan. J for Res, 2018, 29: 479-493,
DOI
|
| 25 |
Keefe RF, Zimbelman EG, Picchi G. Use of individual tree and product level data to improve operational forestry. Curr for Rep, 2022, 8: 148-165,
DOI
|
| 26 |
Krisanski S, Taskhiri MS, Gonzalez Aracil S, Herries D, Muneri A, Gurung MB, Montgomery J, Turner P. Forest structural complexity tool—an open source, fully-automated tool for measuring forest point clouds. Remote Sens, 2021, 13: 4677,
DOI
|
| 27 |
Krisanski S, Taskhiri MS, Gonzalez Aracil S, Herries D, Turner P. Sensor Agnostic semantic segmentation of structurally diverse and complex forest point clouds using deep learning. Remote Sens, 2021, 13: 1413,
DOI
|
| 28 |
Kükenbrink D, Marty M, Bösch R, Ginzler C. Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest. Int J Appl Earth Obs, 2022, 113,
DOI
|
| 29 |
Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F, Holopainen M, Vastaranta M. Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens, 2016, 115: 63-77,
DOI
|
| 30 |
Liang X, Hyyppä J, Kaartinen H, Lehtomäki M, Pyörälä J, Pfeifer N, Holopainen M, Brolly G, Francesco P, Hackenberg J, Huang H, Jo HW, Katoh M, Liu L, Mokroš M, Morel J, Olofsson K, Poveda-Lopez J, Trochta J, Wang D, Wang J, Xi Z, Yang B, Zheng G, Kankare V, Luoma V, Yu X, Chen L, Vastaranta M, Saarinen N, Wang Y. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS J Photogramm Remote Sens, 2018, 144: 137-179,
DOI
|
| 31 |
Lin YC, Shao J, Shin SY, Saka Z, Joseph M, Manish R, Fei S, Habib A. Comparative analysis of multi-platform, multi-resolution, multi-temporal LiDAR data for forest inventory. Remote Sens, 2022, 14: 649,
DOI
|
| 32 |
Lindberg E, Holmgren J. Individual tree crown methods for 3D data from remote sensing. Curr for Rep, 2017, 3: 19-31,
DOI
|
| 33 |
López Serrano FR, Rubio E, García Morote FA, Andrés Abellán M, Picazo Córdoba MI, García Saucedo F, Martínez García E, Sánchez García JM, Serena Innerarity J, Carrasco Lucas L, García González O, García González JC. Artificial intelligence-based software (AID-FOREST) for tree detection: a new framework for fast and accurate forest inventorying using LiDAR point clouds. Int J Appl Earth Obs, 2022, 113,
DOI
|
| 34 |
Maas HG, Bienert A, Scheller S, Keane E. Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens, 2008, 29: 1579-1593,
DOI
|
| 35 |
Marvin DC, Koh LP, Lynam AJ, Wich S, Davies AB, Krishnamurthy R, Stokes E, Starkey R, Asner GP. Integrating technologies for scalable ecology and conservation. Glob Ecol Conserv, 2016, 7: 262-275,
DOI
|
| 36 |
McElhinny C, Gibbons P, Brack C, Bauhus J. Forest and woodland stand structural complexity: Its definition and measurement. For Ecol Manag, 2005, 218: 1-24,
DOI
|
| 37 |
|
| 38 |
Öhman M, Miettinen M, Kannas K, Jutila J, Visala A, Forsman P (2008) Tree Measurement and Simultaneous Localization and Mapping System for Forest Harvesters. In: Laugier C, Siegwart R (Eds.) Field and Service Robotics: Results of the 6th International Conference. Springer, Berlin, Heidelberg, pp 369–378. https://doi.org/10.1007/978-3-540-75404-6_35
|
| 39 |
Pascual,. Using tree detection based on airborne laser scanning to improve forest inventory considering edge effects and the co-registration factor. Remote Sens, 2019, 11: 2675,
DOI
|
| 40 |
Persson HJ, Olofsson K, Holmgren J. Two-phase forest inventory using very-high-resolution laser scanning. Remote Sens Environ, 2022, 271,
DOI
|
| 41 |
Pettorelli N, Wegmann M, Skidmore A, Mücher S, Dawson TP, Fernandez M, Lucas R, Schaepman ME, Wang T, O’Connor B, Jongman RHG, Kempeneers P, Sonnenschein R, Leidner AK, Böhm M, He KS, Nagendra H, Dubois G, Fatoyinbo T, Hansen MC, Paganini M, de Klerk HM, Asner GP, Kerr JT, Estes AB, Schmeller DS, Heiden U, Rocchini D, Pereira HM, Turak E, Fernandez N, Lausch A, Cho MA, Alcaraz-Segura D, McGeoch MA, Turner W, Mueller A, St-Louis V, Penner J, Vihervaara P, Belward A, Reyers B, Geller GN. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens Ecol Conserv, 2016, 2: 122-131,
DOI
|
| 42 |
Pierzchała M, Giguère P, Astrup R. Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM. Comput Electron Agr, 2018, 145: 217-225,
DOI
|
| 43 |
Pretzsch H, Zenner EK. Toward managing mixed-species stands: from parametrization to prescription. For Ecosyst, 2017, 4: 19,
DOI
|
| 44 |
R Core Team (2022) R: A Language and Environment for Statistical Computing.
|
| 45 |
Roussel JR, Auty D, Coops NC, Tompalski P, Goodbody TRH, Meador AS, Bourdon JF, de Boissieu F, Achim A. LidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sens Environ, 2020, 251,
DOI
|
| 46 |
Ryding J, Williams E, Smith M, Eichhorn M. Assessing handheld mobile laser scanners for forest surveys. Remote Sens, 2015, 7: 1095-1111,
DOI
|
| 47 |
Stovall AEL, MacFarlane DW, Crawford D, Jovanovic T, Frank J, Brack C. Comparing mobile and terrestrial laser scanning for measuring and modelling tree stem taper. Forestry, 2023, 96: 705-717,
DOI
|
| 48 |
Taneja R, Wallace L, Hillman S, Reinke K, Hilton J, Jones S, Hally B. Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model. Remote Sens, 2023, 15: 1273,
DOI
|
| 49 |
Tang J, Chen Y, Kukko A, Kaartinen H, Jaakkola A, Khoramshahi E, Hakala T, Hyyppä J, Holopainen M, Hyyppä H. SLAM-aided stem mapping for forest inventory with small-footprint mobile LiDAR. Forests, 2015, 6: 4588-4606,
DOI
|
| 50 |
Tupinambá-Simões F, Pascual A, Guerra-Hernández J, Ordóñez C, de Conto T, Bravo F. Assessing the performance of a handheld laser scanning system for individual tree mapping—a mixed forests showcase in Spain. Remote Sens, 2023, 15: 1169,
DOI
|
| 51 |
Tupinambá-Simões F, Bravo F, Guerra-Hernández J, Pascual A (2022) Assessment of drought effects on survival and growth dynamics in eucalypt commercial forestry using remote sensing photogrammetry. A showcase in Mato Grosso, Brazil. For Ecol Manag 505:119930. https://doi.org/10.1016/j.foreco.2021.119930
|
| 52 |
Uzquiano S, Barbeito I, San Martín R, Ehbrecht M, Seidel D, Bravo F. Quantifying crown morphology of mixed pine-oak forests using terrestrial laser scanning. Remote Sens, 2021, 13: 4955,
DOI
|
| 53 |
Vandendaele B, Martin-Ducup O, Fournier RA, Pelletier G, Lejeune P. Mobile laser scanning for estimating tree structural attributes in a temperate hardwood forest. Remote Sens, 2022, 14: 4522,
DOI
|
| 54 |
Vandendaele B, Martin-Ducup O, Fournier RA, Pelletier G, Lejeune P. Evaluation of Mobile LiDAR acquisition scenarios for automatic wood volume estimation in a mature hardwood forest using quantitative structural models. Can J For Res, 2022,
DOI
|
| 55 |
|
| 56 |
Yin T, Cook BD, Morton DC. Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data. Agr for Meteorol, 2022, 314,
DOI
|