The bigger, the better? Sample size effects in drone-estimated forest height

Abstract

Accurate tree height measurement is crucial for assessing forest health and management strategies, as it directly correlates with biomass and carbon storage capabilities. Thus, drones are increasingly used in forest remote sensing due to their high spatial resolution and operational flexibility. They support individual tree detection and detailed structural mapping, yet the reliability of tree height estimates remains influenced by multiple factors. We present a meta-analysis of 36 case studies to evaluate how environmental complexity and sample size influence tree height estimation accuracy. Forest environments, characterised by heterogeneous canopy structures, occlusions, and species diversity, were associated with higher errors and more significant variability. Conversely, plantations and urban sites yielded lower and more consistent errors, reflecting their structural simplicity and spatial regularity. Although lidar and image-based methods performed similarly in forests (1.58 m vs. 1.69 m mean error), lidar clearly outperformed image matching in plantations (0.44 m vs. 0.91 m). Regarding sample size, mean errors were not consistently lowest in larger datasets, but studies with more than 300 trees exhibited the lowest variability, indicating more stable performance. These findings highlight that both environmental structure and sample size affect the robustness of drone-based height estimation. We recommend the implementation of standardised workflows that account for environmental and technical limitations, including terrain complexity, sensor configuration, and weather conditions. Although drones provide considerable benefits, their successful application depends on careful mission planning and grounded operational expectations. Addressing these challenges through improved mission planning and methodological consistency is essential to ensure the robustness and scalability of drone applications in long-term forest monitoring.

Keywords

Site complexity / Number of samples / Height derivation / Unmanned aerial systems

References

[1] Abdollahnejad A, Panagiotidis D. Tree species classification and health status assessment for a mixed broadleaf-conifer forest with UAS multispectral imaging. Remote Sens, 2020, 12(22): 3722

[2] Alexander C, Korstjens AH, Hankinson E, Usher G, Harrison N, Nowak MG, Abdullah A, Wich SA, Hill RA. Locating emergent trees in a tropical rainforest using data from an Unmanned Aerial Vehicle (UAV). Int J Appl Earth Obs Geoinf, 2018, 72: 86-90

[3] Almeida A, Gonçalves F, Silva G, Mendonça A, Gonzaga M, Silva J, Souza R, Leite I, Neves K, Boeno M, Sousa B. Individual tree detection and qualitative inventory of a Eucalyptus sp. stand using UAV photogrammetry data. Remote Sens, 2021, 13(18): 3655

[4] Balsi M, Esposito S, Fallavollita P, Nardinocchi C. Single-tree detection in high-density LiDAR data from UAV-based survey. Eur J Remote Sens, 2018, 511679-692

[5] Banerjee BP, Spangenberg G, Kant S. Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sens, 2020, 12(19): 3164

[6] Belmonte A, Sankey T, Biederman JA, Bradford J, Goetz SJ, Kolb T, Woolley T. UAV-derived estimates of forest structure to inform ponderosa pine forest restoration. Remote Sens Ecol Conserv, 2020, 62181-197

[7] Berra EF, Peppa MV (2020) Advances and challenges of UAV SFM MVS photogrammetry and remote sensing: short review. In: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile. pp 533–538. https://doi.org/10.1109/lagirs48042.2020.9285975

[8] Berveglieri A, Imai NN, M G Tommaselli A, Martins-Neto RP, Takahashi Miyoshi G, Honkavaara E. Forest cover change analysis based on temporal gradients of the vertical structure and density. Ecol Indic, 2021, 126: 107597

[9] Birdal AC, Avdan U, Türk T. Estimating tree heights with images from an unmanned aerial vehicle. Geomat Nat Hazards Risk, 2017, 8(2): 1144-1156

[10] Brovkina O, Cienciala E, Surový P, Janata P. Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo Spatial Inf Sci, 2018, 21(1): 12-20

[11] Buchelt A, Adrowitzer A, Kieseberg P, Gollob C, Nothdurft A, Eresheim S, Tschiatschek S, Stampfer K, Holzinger A. Exploring artificial intelligence for applications of drones in forest ecology and management. For Ecol Manag, 2024, 551: 121530

[12] Camarretta N, Harrison PA, Bailey T, Potts B, Lucieer A, Davidson N, Hunt M. Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches. New for, 2020, 51(4): 573-596

[13] Campos MB, Castanheiro LF, Shah D, Wang YS, Kukko A, Puttonen E (2024) Overview and benchmark on multi-modal lidar point cloud registration for forest applications. Int Arch Photogramm Remote Sens Spatial Inf Sci XLVIII-1-2024: 43–50. https://doi.org/10.5194/isprs-archives-xlviii-1-2024-43-2024

[14] Chakhvashvili E, Machwitz M, Antala M, Rozenstein O, Prikaziuk E, Schlerf M, Naethe P, Wan QX, Komárek J, Klouek T, Wieneke S, Siegmann B, Kefauver S, Kycko M, Balde H, Paz VS, Jimenez-Berni JA, Buddenbaum H, Hänchen L, Wang N, Weinman A, Rastogi A, Malachy N, Buchaillot ML, Bendig J, Rascher U. Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies. Precis Agric, 2024, 25(5): 2614-2642

[15] Chehreh B, Moutinho A, Viegas C. Latest trends on tree classification and segmentation using UAV data: a review of agroforestry applications. Remote Sensing, 2023, 15(9): 2263

[16] Chen QD, Gao T, Zhu JJ, Wu FY, Li XF, Lu DL, Yu FY. Individual tree segmentation and tree height estimation using leaf-off and leaf-on UAV-LiDAR data in dense deciduous forests. Remote Sens, 2022, 14122787

[17] Chen SW, Nian YY, He ZY, Che ML. Measuring the tree height of Picea crassifolia in Alpine Mountain forests in Northwest China based on UAV-LiDAR. Forests, 2022, 1381163

[18] Chen YL, Yang HT, Yang ZK, Yang QL, Liu WY, Huang GR, Ren Y, Cheng K, Xiang TY, Chen MX, Lin DY, Qi ZY, Xu JC, Zhang YX, Xu GC, Guo QH. Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data. Earth Syst Sci Data, 2024, 16115267-5285

[19] Ciais P, Schelhaas MJ, Zaehle S, Piao SL, Cescatti A, Liski J, Luyssaert S, Le-Maire G, Schulze ED, Bouriaud O, Freibauer A, Valentini R, Nabuurs GJ. Carbon accumulation in European forests. Nat Geosci, 2008, 1(7): 425-429

[20] Coops NC, Tompalski P, Goodbody TRH, Achim A, Mulverhill C. Framework for near real-time forest inventory using multi source remote sensing data. Forestry, 2023, 96(1): 1-19

[21] d’Annunzio R, Sandker M, Finegold Y, Min Z. Projecting global forest area towards 2030. Forest Ecol Manage, 2015, 352: 124-133

[22] da Silva BRF, Ucella-Filho JGM, da Conceição Bispo P, Elera-Gonzales DG, Silva EA, Ferreira RLC. Using drones for dendrometric estimations in forests: a bibliometric analysis. Forests, 2024, 15(11): 1993

[23] Dainelli R, Toscano P, Di Gennaro SF, Matese A. Recent advances in unmanned aerial vehicles forest remote sensing: a systematic review. Part II: research applications. Forests, 2021, 124397

[24] De Petris S, Sarvia F, Borgogno-Mondino E. About tree height measurement: theoretical and practical issues for uncertainty quantification and mapping. Forests, 2022, 137969

[25] de Vries W. Impacts of nitrogen emissions on ecosystems and human health: a mini review. Curr Opin Environ Sci Health, 2021, 21: 100249

[26] Díaz-Varela RA, de la Rosa R, León L, Zarco-Tejada PJ. High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens, 2015, 744213-4232

[27] Diez Y, Kentsch S, Fukuda M, Caceres MLL, Moritake K, Cabezas M. Deep learning in forestry using UAV-acquired RGB data: a practical review. Remote Sens, 2021, 13(14): 2837

[28] Dong XY, Zhang ZC, Yu RY, Tian QJ, Zhu XC. Extraction of information about individual trees from high-spatial-resolution UAV-acquired images of an orchard. Remote Sens, 2020, 121133

[29] Drechsel J, Forkel M. Remote sensing forest health assessment: a comprehensive literature review on a European level. Cent Eur for J, 2025, 71114-39

[30] Ecke S, Dempewolf J, Frey J, Schwaller A, Endres E, Klemmt HJ, Tiede D, Seifert T. UAV-based forest health monitoring: a systematic review. Remote Sens, 2022, 14(13): 3205

[31] Ene L, Næsset E, Gobakken T. Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. Int J Remote Sens, 2012, 33165171-5193

[32] Erdem F, Ocer NE, Matci DK, Kaplan G, Avdan U. Apricot tree detection from UAV-images using mask R-CNN and U-Net. Photogramm Eng Remote Sensing, 2023, 89(2): 89-96

[33] Estreguil C, Caudullo G, de Rigo D, San-Miguel-Ayanz J (2013) Forest landscape in Europe: pattern, fragmentation and connectivity. EUR 25717 EN. Luxembourg: Publications Office of the European Union. https://doi.org/10.2788/77842

[34] Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A. Review of studies on tree species classification from remotely sensed data. Remote Sens Environ, 2016, 186: 64-87

[35] Franklin SE. Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles. J Unmanned Veh Syst, 2018, 6(4): 195-211

[36] Fraser BT, Congalton RG. Estimating primary forest attributes and rare community characteristics using unmanned aerial systems (UAS): an enrichment of conventional forest inventories. Remote Sens, 2021, 13(15): 2971

[37] Fujimoto A, Haga C, Matsui T, Machimura T, Hayashi K, Sugita S, Takagi H. An end to end process development for UAV-SfM based forest monitoring: individual tree detection, species classification and carbon dynamics simulation. Forests, 2019, 108680

[38] Ganz S, Käber Y, Adler P. Measuring tree height with remote sensing: a comparison of photogrammetric and LiDAR data with different field measurements. Forests, 2019, 108694

[39] Grznárová A, Mokroš M, Surový P, Slavík M, Pondelík M, Merganič J (2019) The crown diameter estimation from fixed wing type of uav imagery. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-2/W13: 337–341. https://doi.org/10.5194/isprs-archives-xlii-2-w13-337-2019

[40] Guerra-Hernández J, Cosenza DN, Rodriguez LCE, Silva M, Tomé M, Díaz-Varela RA, González-Ferreiro E. Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. Int J Remote Sens, 2018, 3915–165211-5235

[41] Gülci S, Akay AE, Gülci N, Taş İ. An assessment of conventional and drone-based measurements for tree attributes in timber volume estimation: a case study on stone pine plantation. Ecol Inform, 2021, 63: 101303

[42] Gyawali A, Aalto M, Peuhkurinen J, Villikka M, Ranta T. Comparison of individual tree height estimated from LiDAR and digital aerial photogrammetry in young forests. Sustainability, 2022, 14(7): 3720

[43] Hao ZB, Lin LL, Post CJ, Jiang YS, Li MH, Wei NB, Yu KY, Liu J. Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV). New for, 2021, 525843-862

[44] Hao ZB, Lin LL, Post CJ, Mikhailova EA, Li MH, Chen Y, Yu KY, Liu J. Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN). ISPRS J Photogramm Remote Sens, 2021, 178: 112-123

[45] Hao J, Fang Z, Wu B, Liu S, Ma Y, Pan Y (2022) Tree canopy height estimation and accuracy analysis based on uav remote sensing images. Int Arch Photogramm Remote Sens Spatial Inf Sci XLIII-B2-2022: 129–134. https://doi.org/10.5194/isprs-archives-xliii-b2-2022-129-2022

[46] Hell M, Brandmeier M, Briechle S, Krzystek P. Classification of tree species and standing dead trees with lidar point clouds using two deep neural networks: PointCNN and 3DmFV-Net. PFG, 2022, 90(2): 103-121

[47] Hrdina M, Surový P. Internal tree trunk decay detection using close-range remote sensing data and the PointNet deep learning method. Remote Sens, 2023, 15(24): 5712

[48] Huang HY, He SD, Chen CC. Leaf abundance affects tree height estimation derived from UAV images. Forests, 2019, 1010931

[49] Isibue EW, Pingel TJ. Unmanned aerial vehicle based measurement of urban forests. Urban for Urban Green, 2020, 48: 126574

[50] Janoutová R, Homolová L, Novotný J, Navrátilová B, Pikl M, Malenovský Z. Detailed reconstruction of trees from terrestrial laser scans for remote sensing and radiative transfer modelling applications. In Silico Plants, 2021, 32diab026

[51] Jia XY, Wang CC, Da YZ, Tian XC, Ge WY. Tree-level biomass estimation using unmanned aerial vehicle (UAV) imagery and allometric equation. Biomass Bioenergy, 2024, 190: 107420

[52] Jiang MC, Kong JX, Zhang ZC, Hu JB, Qin YC, Shang KK, Zhao MS, Zhang J. Seeing trees from drones: the role of leaf phenology transition in mapping species distribution in species-rich montane forests. Forests, 2023, 145908

[53] Kanerva H, Honkavaara E, Näsi R, Hakala T, Junttila S, Karila K, Koivumäki N, Alves Oliveira R, Pelto-Arvo M, Pölönen I, Tuviala J, Östersund M, Lyytikäinen-Saarenmaa P. Estimating tree health decline caused by Ips typographus L. from UAS RGB images using a deep one-stage object detection neural network. Remote Sens, 2022, 14246257

[54] Karjalainen V, Koivumäki N, Hakala T, George A, Muhojoki J, Hyyppa E, Suomalainen J, Honkavaara E (2024) Autonomous robotic drone system for mapping forest interiors. Int Arch Photogramm Remote Sens Spatial Inf Sci XLVIII-2-2024: 167–172. https://doi.org/10.5194/isprs-archives-xlviii-2-2024-167-2024

[55] Karthigesu J, Owari T, Tsuyuki S, Hiroshima T. UAV photogrammetry for estimating stand parameters of an old Japanese larch plantation using different filtering methods at two flight altitudes. Sensors, 2023, 23249907

[56] Kašpar V, Hederová L, Macek M, Müllerová J, Prošek J, Surový P, Wild J, Kopecký M. Temperature buffering in temperate forests: comparing microclimate models based on ground measurements with active and passive remote sensing. Remote Sens Environ, 2021, 263: 112522

[57] Kašpar V, Zapletal M, Samec P, Komárek J, Bílek J, Juráň S. Unmanned aerial systems for modelling air pollution removal by urban greenery. Urban for Urban Green, 2022, 78: 127757

[58] Kattenborn T, Wieneke S, Montero D, Mahecha MD, Richter R, Guimarães-Steinicke C, Wirth C, Ferlian O, Feilhauer H, Sachsenmaier L, Eisenhauer N, Dechant B. Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations. Commun Earth Environ, 2024, 5: 550

[59] Kautz M, Meddens AJH, Hall RJ, Arneth A. Biotic disturbances in Northern Hemisphere forests: a synthesis of recent data, uncertainties and implications for forest monitoring and modelling. Glob Ecol Biogeogr, 2017, 265533-552

[60] Klouček T, Klápště P, Marešová J, Komárek J (2022) UAV-borne imagery can supplement airborne Lidar in the precise description of dynamically changing Shrubland Woody vegetation. Remote Sens 14(9):2287. https://doi.org/10.3390/rs14092287

[61] Klouček T, Modlinger R, Zikmundová M, Kycko M, Komárek J. Early detection of bark beetle infestation using UAV-borne multispectral imagery: a case study on the spruce forest in the Czech Republic. Front for Glob Change, 2024, 7: 1215734

[62] Komárek J. When the wind blows: exposing the constraints of drone-based environmental mapping. Nat Sci, 2025, 5(1–2): e70003

[63] Komárek J, Klápště P, Hrach K, Klouček T. The potential of widespread UAV cameras in the identification of conifers and the delineation of their crowns. Forests, 2022, 135710

[64] Komárek J, Lagner O, Klouček T. UAV leaf-on, leaf-off and ALS-aided tree height: a case study on the trees in the vicinity of roads. Urban for Urban Green, 2024, 93: 128229

[65] Krause S, Sanders TGM, Mund JP, Greve K. UAV-based photogrammetric tree height measurement for intensive forest monitoring. Remote Sens, 2019, 117758

[66] Kuželka K, Slavík M, Surový P. Very high density point clouds from UAV laser scanning for automatic tree stem detection and direct diameter measurement. Remote Sens, 2020, 1281236

[67] Kuzmin A, Korhonen L, Kivinen S, Hurskainen P, Korpelainen P, Tanhuanpää T, Maltamo M, Vihervaara P, Kumpula T. Detection of European aspen (Populus tremula L.) based on an unmanned aerial vehicle approach in boreal forests. Remote Sens, 2021, 1391723

[68] Kwong IHY, Fung T. Tree height mapping and crown delineation using LiDAR, large format aerial photographs, and unmanned aerial vehicle photogrammetry in subtropical urban forest. Int J Remote Sens, 2020, 41145228-5256

[69] Lechner AM, Foody GM, Boyd DS. Applications in remote sensing to forest ecology and management. One Earth, 2020, 2(5): 405-412

[70] Lewis SL, Edwards DP, Galbraith D. Increasing human dominance of tropical forests. Science, 2015, 349(6250): 827-832

[71] Lin JY, Wang MM, Ma MG, Lin Y. Aboveground tree biomass estimation of sparse subalpine coniferous forest with UAV oblique photography. Remote Sens, 2018, 10(11): 1849

[72] Lin LL, Hao ZB, Post CJ, Mikhailova EA. Protection of coastal shelter forests using UAVs: individual tree and tree-height detection in Casuarina equisetifolia L. forests. Forests, 2023, 142233

[73] Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests, 2013, 44922-944

[74] Liu Y, El-Kassaby YA. Evapotranspiration and favorable growing degree-days are key to tree height growth and ecosystem functioning: Meta-analyses of Pacific Northwest historical data. Sci Rep, 2018, 8: 8228

[75] Malavasi M, Carranza ML, Moravec D, Cutini M. Reforestation dynamics after land abandonment: a trajectory analysis in Mediterranean mountain landscapes. Reg Environ Change, 2018, 18(8): 2459-2469

[76] Miraki M, Sohrabi H, Fatehi P, Kneubuehler M. Individual tree crown delineation from high-resolution UAV images in broadleaf forest. Ecol Inform, 2021, 61: 101207

[77] Moe KT, Owari T, Furuya N, Hiroshima T. Comparing individual tree height information derived from field surveys, LiDAR and UAV-DAP for high-value timber species in northern Japan. Forests, 2020, 11(2): 223

[78] Mohan M, Silva C, Klauberg C, Jat P, Catts G, Cardil A, Hudak A, Dia M. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 2017, 89340

[79] Mohan M, Leite RV, Broadbent EN, Jaafar WSWM, Srinivasan S, Bajaj S, Corte APD, do Amaral CH, Gopan G, Saad SNM, Kamarulzaman AMM, Prata GA, Llewelyn E, Johnson DJ, Doaemo W, Bohlman S, Zambrano AMA, Cardil A. Individual tree detection using UAV-lidar and UAV-SfM data: a tutorial for beginners. Open Geosci, 2021, 1311028-1039

[80] Moudrý V, Cord AF, Gábor L, Laurin GV, Barták V, Gdulová K, Malavasi M, Rocchini D, Stereńczak K, Prošek J, Klápště P, Wild J. Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability. Divers Distrib, 2023, 29(1): 39-50

[81] Nasiri V, Darvishsefat AA, Arefi H, Pierrot-Deseilligny M, Namiranian M, Le Bris A. Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (case study: Hyrcanian mixed forest). Can J for Res, 2021, 51(7): 962-971

[82] Navarro A, Young M, Allan B, Carnell P, Macreadie P, Ierodiaconou D. The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems. Remote Sens Environ, 2020, 242: 111747

[83] Nemmaoui A, Aguilar FJ, Aguilar MA. Benchmarking of individual tree segmentation methods in Mediterranean forest based on point clouds from unmanned aerial vehicle imagery and low-density airborne laser scanning. Remote Sensing, 2024, 16(21): 3974

[84] Neuville R, Bates JS, Jonard F. Estimating forest structure from UAV-mounted LiDAR point cloud using machine learning. Remote Sensing, 2021, 133352

[85] Nex F, Armenakis C, Cramer M, Cucci DA, Gerke M, Honkavaara E, Kukko A, Persello C, Skaloud J. UAV in the advent of the twenties: where we stand and what is next. ISPRS J Photogramm Remote Sens, 2022, 184: 215-242

[86] Östersund M, Honkavaara E, Oliveira RA, Näsi R, Hakala T, Koivumäki N, Pelto-Arvo M, Tuviala J, Nevalainen O, Lyytikäinen-Saarenmaa P. Exploring forest changes in an Ips typographus L. outbreak area: insights from multi-temporal multispectral UAS remote sensing. Eur J Forest Res, 2024, 143(6): 1871-1892

[87] Ouaknine A, Kattenborn T, Laliberté E, Rolnick D. OpenForest: a data catalog for machine learning in forest monitoring. Environ Data Sci, 2025, 4: e15

[88] Pan YD, Birdsey RA, Fang JY, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao SL, Rautiainen A, Sitch S, Hayes D. A large and persistent carbon sink in the world’s forests. Science, 2011, 333(6045): 988-993

[89] Pan YD, Birdsey RA, Phillips OL, Jackson RB. The structure, distribution, and biomass of the world’s forests. Annu Rev Ecol Evol Syst, 2013, 44: 593-622

[90] Panagiotidis D, Surový P, Kuželka K. Accuracy of structure from motion models in comparison with terrestrial laser scanner for the analysis of DBH and height influence on error behaviour. J Forest Sci, 2016, 62(8): 357-365

[91] Panagiotidis D, Abdollahnejad A, Surový P, Chiteculo V. Determining tree height and crown diameter from high-resolution UAV imagery. Int J Remote Sens, 2017, 38(8–10): 2392-2410

[92] Peña L, Casado-Arzuaga I, Onaindia M. Mapping recreation supply and demand using an ecological and a social evaluation approach. Ecosyst Serv, 2015, 13: 108-118

[93] Peng X, Zhao AJ, Chen YF, Chen Q, Liu HD. Tree height measurements in degraded tropical forests based on UAV-LiDAR data of different point cloud densities: a case study on Dacrydium pierrei in China. Forests, 2021, 12(3): 328

[94] Picos J, Bastos G, Míguez D, Alonso L, Armesto J. Individual tree detection in a Eucalyptus plantation using unmanned aerial vehicle (UAV)-LiDAR. Remote Sens, 2020, 125885

[95] Pulido D, Salas J, Rös M, Puettmann K, Karaman S. Assessment of tree detection methods in multispectral aerial images. Remote Sens, 2020, 12152379

[96] Puliti S, Breidenbach J, Schumacher J, Hauglin M, Klingenberg TF, Astrup R. Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat. Remote Sens Environ, 2021, 265: 112644

[97] Puliti S, McLean JP, Cattaneo N, Fischer C, Astrup R. Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning. Forestry, 2023, 96137-48

[98] Savinelli B, Tagliabue G, Vignali L, Garzonio R, Gentili R, Panigada C, Rossini M. Integrating drone-based LiDAR and multispectral data for tree monitoring. Drones, 2024, 8(12): 744

[99] Schmitz A, Sanders TGM, Bolte A, Bussotti F, Dirnböck T, Johnson J, Peñuelas J, Pollastrini M, Prescher AK, Sardans J, Verstraeten A, de Vries W. Responses of forest ecosystems in Europe to decreasing nitrogen deposition. Environ Pollut, 2019, 244: 980-994

[100] Shugart HH, Saatchi S, Hall FG. Importance of structure and its measurement in quantifying function of forest ecosystems. J Geophys Res Biogeosci, 2010, 115(G2): G00E13

[101] Simic Milas A, Cracknell AP, Warner TA. Drones: the third generation source of remote sensing data. Int J Remote Sens, 2018, 39217125-7137

[102] Slavík M, Kuželka K, Modlinger R, Tomášková I, Surový P. Uav laser scans allow detection of morphological changes in tree canopy. Remote Sens, 2020, 12(22): 3829

[103] Smith P, House JI, Bustamante M, Sobocká J, Harper R, Pan GX, West PC, Clark JM, Adhya T, Rumpel C, Paustian K, Kuikman P, Cotrufo MF, Elliott JA, McDowell R, Griffiths RI, Asakawa S, Bondeau A, Jain AK, Meersmans J, Pugh TAM. Global change pressures on soils from land use and management. Glob Change Biol, 2016, 2231008-1028

[104] Stahl AT, Andrus R, Hicke JA, Hudak AT, Bright BC, Meddens AJH. Automated attribution of forest disturbance types from remote sensing data: a synthesis. Remote Sens Environ, 2023, 285: 113416

[105] Surový P, Almeida Ribeiro N, Panagiotidis D. Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems. Int J Remote Sens, 2018, 39(14): 4786-4800

[106] Szostak M, Knapik K, Wężyk P, Likus-Cieślik J, Pietrzykowski M. Fusing sentinel-2 imagery and ALS point clouds for defining LULC changes on reclaimed areas by afforestation. Sustainability, 2019, 11(5): 1251

[107] Terryn L, Calders K, Bartholomeus H, Bartolo RE, Brede B, D’Hont B, Disney M, Herold M, Lau A, Shenkin A, Whiteside TG, Wilkes P, Verbeeck H. Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests. Remote Sens Environ, 2022, 271: 112912

[108] Tian JR, Dai TT, Li HD, Liao CR, Teng WX, Hu QW, Ma WB, Xu YN. A novel tree height extraction approach for individual trees by combining TLS and UAV image-based point cloud integration. Forests, 2019, 107537

[109] Torresan C, Carotenuto F, Chiavetta U, Miglietta F, Zaldei A, Gioli B. Individual tree crown segmentation in two-layered dense mixed forests from UAV LiDAR data. Drones, 2020, 4210

[110] Torresani M, Rocchini D, Alberti A, Moudrý V, Heym M, Thouverai E, Kacic P, Tomelleri E. LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems. Ecol Inform, 2023, 76: 102082

[111] Vacca G, Vecchi E. UAV photogrammetric surveys for tree height estimation. Drones, 2024, 83106

[112] Vastaranta M, Kankare V, Holopainen M, Yu XW, Hyyppä J, Hyyppä H. Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data. ISPRS J Photogramm Remote Sens, 2012, 67: 73-79

[113] White JC, Coops NC, Wulder MA, Vastaranta M, Hilker T, Tompalski P. Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens, 2016, 42(5): 619-641

[114] Winsen M, Hamilton G. A comparison of UAV-derived dense point clouds using LiDAR and NIR photogrammetry in an Australian eucalypt forest. Remote Sens, 2023, 15(6): 1694

[115] Young DJN, Koontz MJ, Weeks JM. Optimizing aerial imagery collection and processing parameters for drone-based individual tree mapping in structurally complex conifer forests. Methods Ecol Evol, 2022, 1371447-1463

[116] Yurtseven H, Akgul M, Coban S, Gulci S. Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques. Measurement, 2019, 145: 651-664

[117] Zainuddin K, Jaffri MH, Zainal MZ, Ghazali N, Samad AM (2016) Verification test on ability to use low-cost UAV for quantifying tree height. In: 2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), Melaka, Malaysia. pp 317–321. https://doi.org/10.1109/CSPA.2016.7515853

[118] Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron, 2014, 55: 89-99

[119] Zhang CL, Valente J, Kooistra L, Guo LF, Wang WS. Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches. Precis Agric, 2021, 22(6): 2007-2052

[120] Zhao MF, Sun MD, Xiong T, Tian SH, Liu SG. On the link between tree size and ecosystem carbon sequestration capacity across continental forests. Ecosphere, 2022, 13(6): e4079

[121] Zhao YT, Im J, Zhen Z, Zhao YH. Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data. Gisci Remote Sens, 2023, 6012197281

[122] Zheng JP, Yuan S, Li WJ, Fu HH, Yu L, Huang JX. A review of individual tree crown detection and delineation from optical remote sensing images: current progress and future. IEEE Geosci Remote Sens Mag, 2025, 131209-236

[123] Zhou XM, Zhang XL. Individual tree parameters estimation for plantation forests based on UAV oblique photography. IEEE Access, 2020, 8: 96184-96198

Funding

Czech University of Life Sciences Prague

RIGHTS & PERMISSIONS

The Author(s)