Barriers and gaps in the implementation of close-range remote sensing technologies in forestry

Abstract

Close-range remote sensing (CRRS) technologies are increasingly used in forestry, but there is a lack of awareness of the challenges, needs and expectations of both service providers and end users. We used a customised online questionnaire to interview professionals in the field, recruited through direct (existing networks) and indirect channels (social media). The main barriers we identified include the cost of equipment, the complexity of data processing workflows and insufficient access to specialised training. Our findings emphasise the need for interdisciplinary collaboration, the development of more intuitive and user-friendly tools and the expansion of specialised training programmes. The results of the questionnaire suggest that stronger partnerships between industry and academia should be encouraged to drive innovation and knowledge sharing. In addition, the development of standardised protocols for CRRS applications and the creation of accessible educational resources proved essential to support both novice and experienced users. Scientific conferences are the most important platform to gather all stakeholders in one place, and have underutilised potential to narrow the gap between theory and application. The recommendations we have made aim to facilitate the widespread adoption and efficient utilisation of CRRS technologies in practical forestry.

Keywords

Precision forestry / Forest monitoring / LiDAR / Photogrammetry / User-provider collaboration

References

[1] Balestra M, Marselis S, Sankey TT, Cabo C, Liang XL, Mokroš M, Peng X, Singh A, Stereńczak K, Vega C, Vincent G, Hollaus M. LiDAR data fusion to improve forest attribute estimates: a review. Curr for Rep, 2024, 10(4): 281-297

[2] Bannari A, Ait-Thami A, Sbai M, El-Ghmari A (2023) Assessment of long-term vegetation cover change in mountainous areas of the Moroccan high-atlas using close-range remote sensing and climatic variables. In: IGARSS 2023—2023 IEEE international geoscience and remote sensing symposium. IEEE, Pasadena, pp 2985–2988

[3] Barrett F, McRoberts RE, Tomppo E, Cienciala E, Waser LT. A questionnaire-based review of the operational use of remotely sensed data by national forest inventories. Remote Sens Environ, 2016, 174: 279-289

[4] Borz SA, Proto AR, Keefe R, Niţă MD. Electronics, close-range sensors and artificial intelligence in forestry. Forests, 2022, 13(10): 1669

[5] Bright BC, Loudermilk EL, Pokswinski SM, Hudak AT, O’Brien JJ. Introducing close-range photogrammetry for characterizing forest understory plant diversity and surface fuel structure at fine scales. Can J Remote Sens, 2016, 425460-472

[6] Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile V, Disney M, Armston J, Kaasalainen M. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Meth Ecol Evol, 2015, 6(2): 198-208

[7] Campos MB, Nunes MH, Shcherbacheva A, Valve V, Lintunen A, Kaitaniemi P, Junttila S, Yann S, Kulmala M, Kukko A, Hyyppä J, Wang YS, Puttonen E. Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series. Agric for Meteorol, 2024, 358 110253

[8] COST Action CA20118 (2025) Online questionnaire, questionnaire items and acknowledgement list created as part of the COST Action CA20118—three-dimensional forest ecosystem monitoring and better understanding by terrestrial-based technologies (3DForEcoTech). https://forms.gle/SYWcjZzVuBud4C8e6

[9] Cova GR, Prichard SJ, Rowell E, Drye B, Eagle P, Kennedy MC, Nemens DG. Evaluating close-range photogrammetry for 3D understory fuel characterization and biomass prediction in pine forests. Remote Sens, 2023, 15(19): 4837

[10] Doruska PF, Weih RC, Lane MD, Bragg DC (2005) Quantifying forest ground flora biomass using close-range remote sensing. In: McRoberts RE, Reams GA, Van Deusen PC, McWilliams WH, Cieszewski CJ (eds) Proceedings of the fourth annual forest inventory and analysis symposium; Gen. Tech. Rep. NC-252, pp 87–90. https://doi.org/10.2737/nc-gtr-252

[11] Ekstrand S. Close range forest defoliation effects of traffic emissions assessed using aerial photography. Sci Total Environ, 1994, 146: 149-155

[12] Esri (2020) ArcMap (Version 10.8). CA: Environmental Systems Research Institute

[13] Frey J. Evaluating close-range remote sensing techniques for the retention of biodiversity-related forest. Structures, 2019

[14] Frey J, Asbeck T, Bauhus J. Predicting tree-related microhabitats by multisensor close-range remote sensing structural parameters for the selection of retention elements. Remote Sens, 2020, 12(5 867

[15] Hrdina M, Surový P. Internal tree trunk decay detection using close-range remote sensing data and the PointNet deep learning method. Remote Sens, 2023, 1524 5712

[16] Jenn NC. Designing a questionnaire. Malays Fam Physician, 2006, 11): 32-35

[17] Jurjević L, Gašparović M, Liang XL, Balenović I. Assessment of close-range remote sensing methods for DTM estimation in a lowland deciduous forest. Remote Sens, 2021, 13(11 2063

[18] Koo M, Yang SW. Likert-type scale. Encyclopedia, 2025, 51 18

[19] Kükenbrink D, Marty M, Bösch R, Ginzler C (2021) Close-range remote sensing for national forest inventory applications: a comparison of terrestrial and airborne approaches. In: Proceedings of the SilviLaser conference, pp 148–150. https://doi.org/10.34726/wim.1861

[20] Kulicki M, Cabo C, Trzciński T, Będkowski J, Stereńczak K. Artificial intelligence and terrestrial point clouds for forest monitoring. Curr for Rep, 2024, 111 5

[21] Kuželka K, Surový P. Mathematically optimized trajectory for terrestrial close-range photogrammetric 3D reconstruction of forest stands. ISPRS J Photogramm Remote Sens, 2021, 178: 259-281

[22] Liang XL, Kukko A, Balenović I, Saarinen N, Junttila S, Kankare V, Holopainen M, Mokroš M, Surový P, Kaartinen H, Jurjević L, Honkavaara E, Näsi R, Liu JB, Hollaus M, Tian JJ, Yu XW, Pan J, Cai SS, Virtanen JP, Wang YS, Hyyppä J. Close-range remote sensing of forests: the state of the art, challenges, and opportunities for systems and data acquisitions. IEEE Geosci Remote Sens Mag, 2022, 103): 32-71

[23] Liang XL, Qi HW, Deng XJ, Chen JC, Cai SS, Zhang QJ, Wang YS, Kukko A, Hyyppä J. Forestsemantic: a dataset for semantic learning of forest from close-range sensing. Geo Spat Inf Sci, 2025, 28(1): 185-211

[24] Liu QL, Li CY, Meng P, Liu RW, Zhang JS, Gao J. Forest measurement method and data analysis of multi-baseline digital close-range photogrammetry system. Sci Silvae Sin, 2010, 46(2): 166-170(in Chinese)

[25] Luhmann T. Close range photogrammetry for industrial applications. ISPRS J Photogramm Remote Sens, 2010, 65(6): 558-569

[26] Madsen B (2020) Understanding grassland plant diversity dynamics using close-range remote sensing. Dissertation, Aarhus Universitet. https://pure.au.dk/portal/en/publications/understanding-grassland-plant-diversity-dynamics-using-close-rang

[27] McGlade J, Wallace L, Reinke K, Jones S. The potential of low-cost 3D imaging technologies for forestry applications: setting a research agenda for low-cost remote sensing inventory tasks. Forests, 2022, 13(2): 204

[28] Mikita T, Janata P, Surový P. Forest stand inventory based on combined aerial and terrestrial close-range photogrammetry. Forests, 2016, 78 165

[29] Mokros M (2017) First test of low-cost single-board computer based camera for close-range forest photogrammetry. In: SGEM international multidisciplinary scientific GeoConference EXPO proceedings. Stef92 technology. https://doi.org/10.5593/sgem2017h/33/s14.066

[30] Murtiyoso A, Cabo C, Singh A, Obaya DP, Cherlet W, Stoddart J, Fol CR, Beloiu Schwenke M, Rehush N, Stereńczak K, Calders K, Griess VC, Mokroš M. A review of software solutions to process ground-based point clouds in forest applications. Curr for Rep, 2024, 106401-419

[31] Nevalainen O, Honkavaara E, Hakala T, Kaasalainen S, Viljanen N, Rosnell T, Khoramshahi E, Näsi R (2016) Close-range environmental remote sensing with 3D hyperspectral technologies. In: Earth resources and environmental remote sensing/GIS applications VII. SPIE, Edinburgh, pp 1000503. https://doi.org/10.1117/12.2240936

[32] Niederheiser R, Winkler M, Di Cecco V, Erschbamer B, Fernández R, Geitner C, Hofbauer H, Kalaitzidis C, Klingraber B, Lamprecht A, Lorite J, Nicklas L, Nyktas P, Pauli H, Stanisci A, Steinbauer K, Theurillat JP, Vittoz P, Rutzinger M. Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain. Gisci Remote Sens, 2021, 58(1): 120-137

[33] Panagiotidis D, Abdollahnejad A. Accuracy assessment of total stem volume using close-range sensing: advances in precision forestry. Forests, 2021, 12(6): 717

[34] R Core Team (2024) R: a language and environment for statistical computing (R 4.3.2). R Foundation for Statistical Computing

[35] Rasib AW, Abd Hamid N-R, Mohd Yaacob ML, Abd Ghani Z, Idris NH, Alvin LMS, Hassan MI, Idris KM, Dollah R, Mohd Salleh A, Anjang Ahmad MB. Rubber-tree leaf diseases mapping using close-range remote sensing images. Int J Integr Eng, 2022, 1451-12

[36] Ryding J, Williams E, Smith MJ, Eichhorn MP. Assessing handheld mobile laser scanners for forest surveys. Remote Sensing, 2015, 7(1): 1095-1111

[37] Schwank M, Naderpour R, Mätzler C. Tau-omega”- and two-stream emission models used for passive L-band retrievals: application to close-range measurements over a forest. Remote Sens, 2018, 10(12 1868

[38] Shcherbacheva A, Campos MB, Wang YS, Liang XL, Kukko A, Hyyppä J, Junttila S, Lintunen A, Korpela I, Puttonen E. A study of annual tree-wise LiDAR intensity patterns of boreal species observed using a hyper-temporal laser scanning time series. Remote Sens Environ, 2024, 305 114083

[39] Tamaki Y, Konoshima M. Application of terrestrial close-range photogrammetry for estimating stem volume of tree species in subtropical forest in Okinawa, Japan. Formath, 2019, 18: n/a

[40] Vastaranta M, Holopainen M, Hyyppä J, Hyyppä H. Tree mapping and forest inventory using terrestrial laser scanning. Remote Sens, 2013, 53): 1198-1222

[41] Vastaranta M, Saarinen N, Yrttimaa T, Kankare V, Junttila S (2020) Monitoring forests in space and time using close-range sensing. Preprints. https://doi.org/10.20944/preprints202002.0300.v1

[42] Vepakomma U, Cormier D. Inventorying the forest: laser scanning vs close range photogrammetry on a UAV. Proc SilviLaser, 2015, 2015: 68-70

[43] Yrttimaa T, Matsuzaki S, Kankare V, Junttila S, Saarinen N, Kukko A, Hyyppä J, Miura J, Vastaranta M. Assessing forest traversability for autonomous mobile systems using close-range airborne laser scanning. Croat J for Eng (Online), 2024, 45(1): 169-182

[44] Zhu RN, Guo ZQ, Zhang XL. Forest 3d reconstruction and individual tree parameter extraction combining close-range photo enhancement and feature matching. Remote Sens, 2021, 139 1633

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Northeast Forestry University