Recent Advances in UAV-Based Structure-from-Motion Photogrammetry for Aboveground Biomass and Carbon Storage Estimations in Forestry

  • Chapter
  • First Online:
Concepts and Applications of Remote Sensing in Forestry

Abstract

Due to economical, ecological, and social changes over the last decade, managers and researchers in nature-based disciplines intend to use both traditional and innovative remote sensing (RS) technologies for best management practices. RS, which has grown interest in forestry, offers rapid and reliable assessment tool to monitoring and observing. Several successful studies in literature have indicated that the RS use in forestry is light the way of the most effective evaluating various forest ecosystems. Recent develo** unmanned aerial platforms play as a low-cost and inexperienced user-based multi-image processing by using computer vision techniques for forestry studies. Forests, where are essential natural resources for the future, are the pool of biomass and carbon storage, and they need periodical monitoring to sustain. A sustainable management of carbon balance and biomass in mountainous forests includes exhausting effort in field-based studies. However, the RS as a tool for study on biomass and carbon storage can be received as the most effective prediction and nondestructive method in combination with structure-from-motion techniques. Considering recent opportunities in data science and unmanned aerial vehicles (UAVs), RS and photogrammetry in forestry have still played an indispensable role in the evolution of forests. This chapter aims to review the recent advanced knowledge on the progress of the use of UAV technologies in accordance with advanced photogrammetry-related applications in the quantification of forest aboveground biomass and carbon storage. A comprehensive literature search has been performed on the use of UAV-based SfM photogrammetry for UAV-based forest biomass and carbon storage studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Akgul M, Yurtseven H, Gulci S, Akay AE (2018) Evaluation of UAV-and GNSS-based DEMs for earthwork volume. Arab J Sci Eng 43(4):1893–1909

    Article  Google Scholar 

  • Alonzo M, Andersen H-E, Morton DC, Cook BD (2018) Quantifying boreal forest structure and composition using UAV Structure from Motion. Forests 9(3):119

    Article  Google Scholar 

  • Andersson F (1971) Methods and preliminary results of estimation of biomass and primary production in south Swedish mixed deciduous woodland. In: Du-Vigneaud P (ed) Productivity of forest ecosystems. UNESCO, Paris, pp 281–288

    Google Scholar 

  • Banu TP, Borlea GF, Banu C (2016) The use of drones in forestry. J Environ Sci Eng B 5:557–562

    Google Scholar 

  • Brown S (1997) Estimating biomass and biomass change of tropical forests. Forest Resources Assessment Publication. FAO Forestry Papers 134:55 pp. Rome

    Google Scholar 

  • Bugday E (2018) Capabilities of using UAVs in forest road construction activities. Eur J Forest Eng 4(2):56–62

    Article  Google Scholar 

  • Castellanos-Galindo GA, Casella E, Tavera H, Zapata Padilla LA, Simard M (2021) Structural characteristics of the tallest mangrove forests of the American continent: a comparison of ground-based, drone and radar measurements. Front For Glob Change 4:732468

    Article  Google Scholar 

  • Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure J-P, Nelson BW, Ogawa H, Puig H, Riera B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145(1):87–99

    Article  CAS  Google Scholar 

  • Chave J, Réjou-Méchain M, Burquez A, Chidumayo E (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol 20:3177–3190

    Article  Google Scholar 

  • Dainelli R, Toscano P, Di Gennaro SF, Matese A (2021) Recent advances in unmanned aerial vehicle forest remote sensing—a systematic review. Part I: a general framework. Forests 12:327

    Article  Google Scholar 

  • Dandois JP, Ellis EC (2010) Remote sensing of vegetation structure using computer vision. Remote Sens 2:1157–1176

    Article  Google Scholar 

  • Dandois J, Olano M, Ellis E (2015) Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sens 7:13895–13920

    Article  Google Scholar 

  • Dittmann S, Thiessen E, Hartung E (2017) Applicability of different non-invasive methods for tree mass estimation: a review. For Ecol Manag 398:208–215

    Article  Google Scholar 

  • Eker R, Alkan E, Aydın A (2020) A comparative analysis of UAV-RTK and UAV-PPK methods in map** different surface types. Eur J Forest Eng 7(1):12–25

    Google Scholar 

  • Fernandes MR, Aguiar FC, Martins MJ, Rico N, Ferreira MT, Correia AC (2020) Carbon stock estimations in a Mediterranean riparian forest: a case study combining field data and UAV imagery. Forests 11(4):376

    Article  Google Scholar 

  • González-Jorge H, Martínez-Sánchez J, Bueno M, Arias AP (2017) Unmanned aerial systems for civil applications: a review. Drones 1(1):2

    Article  Google Scholar 

  • Grybas H, Congalton RG (2021) A Comparison of multi-temporal RGB and multispectral UAS imagery for tree species classification in heterogeneous New Hampshire Forests. Remote Sens 13(13):2631

    Article  Google Scholar 

  • Guerra-Hernández J, Díaz-Varela RA, Ávarez-González JG, Rodríguez-González PM (2021) Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests. For Ecosyst 8(1):1–21

    Article  Google Scholar 

  • Guimarães N, Pádua L, Marques P, Silva N, Peres E, Sousa JJ (2020) Forestry remote sensing from unmanned aerial vehicles: a review focusing on the data, processing and potentialities. Remote Sens 12(6):1046

    Article  Google Scholar 

  • Gülci S (2019) The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: an analysis of data production in pure coniferous young forest stands. Environ Monit Assess 191(8):495. https://doi.org/10.1007/s10661-019-7628-4

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gupta SG, Ghonge MM, Jawandhiya PM (2013) Review of unmanned aircraft system (UAS). Int J Adv Res Comput Sci Eng Inf Technol 2(4):1646–1658

    Google Scholar 

  • Hentz ÂMK, Silva CA, Dalla Corte AP, Netto SP, Strager MP, Klauberg C (2018) Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection. For Syst 27(2):17

    Google Scholar 

  • Hermosilla T, Ruiz LA, Kazakova AN, Coops NC, Moskal LM (2014) Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. Int J Wildl Fire 23:224–233

    Article  Google Scholar 

  • Iglhaut J, Cabo C, Puliti S, Piermattei L, O’Connor J, Rosette J (2019) Structure from motion photogrammetry in forestry: a review. Curr Forest Rep 5(3):155–168

    Article  Google Scholar 

  • Jayathunga S, Owari T, Tsuyuki S, Hirata Y (2020) Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests. Int J Remote Sens 41:53–73

    Article  Google Scholar 

  • Kameyama S, Sugiura K (2020) Estimating tree height and volume using unmanned aerial vehicle photography and SfM technology, with verification of result accuracy. Drones 4(2):19

    Article  Google Scholar 

  • Keane JF, Carr SS (2013) A brief history of early unmanned aircraft. Johns Hopkins APL Technical Digest 32(3):558–571

    Google Scholar 

  • Lingner S, Thiessen E, Müller K, Hartung E (2018) Dry biomass estimation of hedge banks: allometric equation vs. structure from motion via unmanned aerial vehicle. J For Sci 64:149–156

    Article  Google Scholar 

  • Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P (2013) A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial; system imagery. Forests 4:922–944

    Article  Google Scholar 

  • Michez A, Piégay H, Lisein J, Claessens H, Lejeune P (2016) Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ Monit Assess 188:146

    Article  Google Scholar 

  • Miraki M, Sohrabi H, Fatehi P, Kneubuehler M (2021) Detection of mistletoe infected trees using UAV high spatial resolution images. J Plant Dis Prot 128:1679–1689

    Article  Google Scholar 

  • Mohan M, Leite RV, Broadbent EN, Jaafar WSWM, Srinivasan S, Bajaj S et al (2021) Individual tree detection using UAV-lidar and UAV-SfM data: a tutorial for beginners. Open Geosci 13(1):1028–1039

    Article  Google Scholar 

  • Nasiri V, Darvishsefat AA, Arefi H, Pierrot-Deseilligny M, Namiranian M, Le Bris A (2021) 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 51(7):962–971

    Article  CAS  Google Scholar 

  • Návar J (2009a) Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For Ecol Manag 257:427–434

    Article  Google Scholar 

  • Návar J (2009b) Biomass component equations for Latin American species and groups of species. Ann For Sci 66:208–216

    Article  Google Scholar 

  • Newcome LR (2004) Unmanned aviation: a brief history of unmanned aerial vehicles. Reston, VA, American Institute of Aeronautics and Astronautics, Inc., pp 45–50

    Book  Google Scholar 

  • Nex F, Remondino F (2014) UAV for 3D map** applications: a review. Appl Geomatics 6:1–15

    Article  Google Scholar 

  • Otero V, Van De Kerchove R, Satyanarayana B, Martinez-Espinosa C, Bin Fisol MA, Bin Ibrahim MR, Sulong I, Mohd-Lokman H, Lucas R, Dahdouh-Guebas F (2018) Managing mangrove forests from the sky: forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. For Ecol Manag 411:35–45

    Article  Google Scholar 

  • Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm Eng Remote Sens 81(4):281–329

    Article  Google Scholar 

  • Perruchoud DO, Fischlin A (1995) The response of the carbon cycle in undisturbed forest ecosystems to climate change: a review of plant-soil models. J Biogeograph 22:759–274

    Article  Google Scholar 

  • Puliti S, Ene LT, Gobakken T, Næsset E (2017) Use of partial-coverage uav data in sampling for large scale forest inventories. Remote Sens Environ 194:115–126

    Article  Google Scholar 

  • Puliti S, Talbot B, Astrup R (2018a) Tree-stump detection, segmentation, classification, and measurement using Unmanned aerial vehicle (UAV) imagery. Forests 9:102

    Article  Google Scholar 

  • Puliti S, Saarela S, Gobakken T, Ståhl G, Næsset E (2018b) Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference. Remote Sens Environ 204:485–497

    Article  Google Scholar 

  • Puliti S, Solberg S, Granhus A (2019) Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration. Remote Sens 11(3):233

    Article  Google Scholar 

  • Remondino F, Spera MG, Nocerino E, Menna F, Nex F (2014) State of the art in high density image matching. Photogramm Rec 29:144–166

    Article  Google Scholar 

  • Richardson J, Bjorheden R, Hakkila P, Lowe AT, Smith CT (2002) Bioenergy from sustainable forestry: guiding principles and practice. Kluwer Academic Publishers, Dordrecht, p 344

    Book  Google Scholar 

  • Sankey T, Donager J, McVay J, Sankey JB (2017) UAV Lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens Environ 195:30–43

    Article  Google Scholar 

  • Silva CA, Crookston NL, Hudak AT, Vierling LA (2015) Package ‘rLiDAR’: LiDAR data processing and visualization. Available in CRAN repository. https://cran.r-project.org/web/packages/rLiDAR/rLiDAR.pdf. Accessed 15 Dec 2021

  • Smith MW, Carrivick JL, Quincey DJ (2015) Structure from motion photogrammetry in physical geography. Prog Phys Geogr 40:247–275

    Article  Google Scholar 

  • Stone C, Webster M, Osborn J, Iqbal I (2016) Alternatives to LiDAR-derived canopy height models for softwood plantations: a review and example using photogrammetry. Aust For 79:271–282

    Article  Google Scholar 

  • Swayze NC, Tinkham WT, Vogeler JC, Hudak AT (2021) Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density. Remote Sens Environ 263:112540

    Article  Google Scholar 

  • Tang L, Shao G (2015) Drone remote sensing for forestry research and practices. J For Res 26:791–797

    Article  Google Scholar 

  • Tiwari AK, Singh JS (1984) Map** forest biomass in India through aerial photographs and nondestructive field sampling. Appl Geogr 4:151–165

    Article  Google Scholar 

  • Torresan C, Berton A, Carotenuto F, Di Gennaro SF, Gioli B, Matese A, Miglietta F, Vagnoli C, Zaldei A, Wallace L (2017) Forestry applications of UAVs in Europe: a review. Int J Remote Sens 38(8–10):2427–2447

    Article  Google Scholar 

  • Tuominen S, Balazs A, Saari H, Pölönen I, Sarkeala J, Viitala R (2015) Unmanned aerial system imagery and photogrammetric canopy height data in area-based estimation of forest variables. Silva Fenn 49(5):1348

    Article  Google Scholar 

  • Van Leeuwen M, Nieuwenhuis M (2010) Retrieval of forest structural parameters using LiDAR remote sensing. Eur J For Res 129:749–770

    Article  Google Scholar 

  • Wallace L, Lucieer A, Watson C, Turner D (2012) Development of a UAV-LiDAR system with application to forest inventory. Remote Sens 4:1519

    Article  Google Scholar 

  • Wallace L, Lucieer A, Malenovský Z, Turner D, Vopˇenka P (2016) Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 7:62

    Article  Google Scholar 

  • Wang M, Lin J (2020) Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV). Can J For Res 50(10):1012–1024

    Article  Google Scholar 

  • Woellner R, Wagner TC (2019) Saving species, time and money: application of unmanned aerial vehicles (UAVs) for monitoring of an endangered alpine river specialist in a small nature reserve. Biol Conserv 233:162–175

    Article  Google Scholar 

  • Yavaşli DD (2012) Recent approaches in aboveground biomass estimation methods. Aegean Geogra J 21(1):39–49

    Google Scholar 

  • Yurtseven H, Akgul M, Coban S, Gulci S (2019) Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques. Measurement 145:651–664

    Article  Google Scholar 

Download references

Authors’ Contributions

All authors performed the same effort in this study. All authors wrote, read, reviewed, and decided to submit as chapters of a book.

Conflict of Interest

No conflict of interest

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sercan Gülci .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gülci, S., Akay, A.E., Aricak, B., Sariyildiz, T. (2022). Recent Advances in UAV-Based Structure-from-Motion Photogrammetry for Aboveground Biomass and Carbon Storage Estimations in Forestry. In: Suratman, M.N. (eds) Concepts and Applications of Remote Sensing in Forestry . Springer, Singapore. https://doi.org/10.1007/978-981-19-4200-6_20

Download citation

Publish with us

Policies and ethics

Navigation