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Optimising UAV photogrammetry for forest structure assessment and silvicultural planning
Thesis (PhDFor)--Stellenbosch University, 2025.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614069008105472 |
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| access_status_str | Open Access |
| author | Seifert, Erich |
| author2 | Drew, David |
| author_browse | Drew, David Seifert, Erich |
| author_facet | Drew, David Seifert, Erich |
| author_sort | Seifert, Erich |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhDFor)--Stellenbosch University, 2025. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132276 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:46:10.315Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132276 Optimising UAV photogrammetry for forest structure assessment and silvicultural planning Seifert, Erich Drew, David Van Aardt, Jan Stellenbosch University. Faculty of AgriSciences. Dept. of Forest and Wood Science. Photogrammetry -- Digital techniques Silvicultural systems -- Decision making Forest management -- Data processing Drone aircraft -- Remote sensing Unmanned Aerial Vehicles -- Mathematical models Forests and forestry -- Planning -- Evaluation UCTD Thesis (PhDFor)--Stellenbosch University, 2025. Seifert, E. 2025. Optimising UAV Photogrammetry for Forest Structure Assessment and Silvicultural Planning. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/ce711847-15b7-4930-a15f-76e1e4a37b33 ENGLISH ABSTRACT: This thesis explores the optimisation of UAV photogrammetry and related computational algorithms to enhance forest structure measurement and management practices. The research addresses key challenges in operational forestry, including the optimisation of UAV flight parameters, the development of efficient point cloud reconstruction methods, and the integration of remote sensing data into silvicultural decision-making processes. The first study investigates the impact of UAV flight parameters—altitude, overlap, and sensor resolution—on the quality and efficiency of Structure-from- Motion (SfM) reconstructions. Results demonstrate that low-altitude flights with high image overlaps produce the most detailed point clouds, providing practical guidelines for balancing data quality and operational efficiency in UAVbased forest assessments. These findings establish a robust foundation for deploying UAVs effectively in diverse forest conditions. The second study introduces the Optical Flow (OF) algorithm as an alternative to traditional SfM methods. The OF algorithm significantly reduces computational processing times (achieving over 98 % reductions compared to SfM without GPU acceleration) without compromising accuracy in key forest metrics such as tree height and crown dimensions. This advancement improves the scalability of UAV-based monitoring, making it feasible for large-scale or time-sensitive forestry applications. The third study examines the integration of UAV and TLS data to calculate competition indices and inform thinning decisions. This research highlights the potential of UAV-derived point clouds to assess stand density and individual tree competition, particularly in less dense stands where UAV data capture is more effective. The ability to derive precise thinning prescriptions demonstrates the practical utility of integrating remote sensing data into silvicultural planning and forest management. While these studies provide valuable insights, the research was conducted exclusively in even-aged stands dominated by Norway spruce (Picea abies Karst.) in Germany. As such, the findings may not be directly generalisable to uneven-aged or mixed-species forests, nor to regions with different environmental conditions. Future research should explore the applicability of these methods across a broader range of forest types, stand structures, and geographical locations. Additionally, investigating how these UAV-based workflows perform under varying climatic and operational constraints would further enhance their utility for forest management. This thesis underscores the importance of a fully integrated UAV-based workflow for forest monitoring and management. It demonstrates how optimised flight parameters improve data acquisition, how novel computational methods enable efficient and accurate extraction of key forest inventory metrics, and how the integration of remote sensing data supports silvicultural planning and forest management decisions. By streamlining this end-to-end process, the research lays the groundwork for more informed, data-driven approaches to sustainable forestry. These advancements provide valuable tools for monitoring forest structure, guiding silvicultural interventions, and supporting ecosystem resilience and resource sustainability in the face of ongoing environmental challenges. AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die optimalisering van UAV-fotogrammetrie en ver- wante rekenaaralgoritmes om meting van bosstruktuur en bestuurspraktyke te verbeter. Die navorsing spreek sleuteluitdagings in operasionele bosbou aan, insluitend die optimalisering van UAV-vlugparameters, die ontwikkeling van doeltreffende puntwolk-rekonstruksiemetodes, en die integrasie van afstands- waarnemingsdata in silvikulturele besluitnemingsprosesse. Die eerste studie ondersoek die impak van UAV-vlugparameters—hoogte, oorvleueling en sensorresolusie—op die kwaliteit en doeltreffendheid van Structure- from-Motion (SfM)-rekonstruksies. Resultate toon dat lae-vlughoogtes met hoë beeldoorvleueling die mees gedetailleerde puntwolke lewer, wat praktiese rig- lyne bied om ‘n balans tussen datakwaliteit en operasionele doeltreffendheid in UAV-gebaseerde bosbepalings te bewerkstellig. Hierdie bevindinge lê ‘n ste- wige grondslag vir die doeltreffende implementering van UAV’s in verskillende bosomstandighede. Die tweede studie stel die Optiese Vloei (OF)-algoritme voor as ‘n alterna- tief vir tradisionele SfM-metodes. Die OF-algoritme verminder rekenaarver- werkingstye aansienlik (met meer as 98 % in vergelyking met SfM sonder GPU- versnelling), sonder om akkuraatheid in sleutelbosmetrieke soos boomhoogte en kroonafmetings in te boet. Hierdie vooruitgang verbeter die skaalbaarheid van UAV-gebaseerde monitering, wat dit uitvoerbaar maak vir grootskaalse of tydsensitiewe bosbouaansoeke. Die derde studie ondersoek die integrasie van UAV- en TLS-data om kompetisie- indekse te bereken en uitdunbesluite te ondersteun. Hierdie navorsing beklem- toon die potensiaal van UAV-afgeleide puntwolke om standdigtheid en indivi- duele boomkompetisie te beoordeel, veral in minder digte stande waar UAV- datavaslegging meer effektief is. Die vermoë om presiese uitdunvoorskrifte af te lei, toon die praktiese nut van afstandswaarnemingsdata in silvikulturele be- planning en bosbestuur. Hoewel hierdie studies waardevolle insigte bied, is die navorsing uitsluitlik in eenvormige stande van Noorse spar (Picea abies Karst.) in Duitsland uit- gevoer. Daarom mag die bevindinge nie direk veralgemeen word na ongelyk- jarige of gemengde spesiebosse, of na streke met verskillende omgewingstoe- stande nie. Toekomstige navorsing behoort die toepaslikheid van hierdie me- todes oor ‘n wyer reeks bossoorte, standstrukture en geografiese liggings te ondersoek. Daarbenewens sal ‘n ontleding van hoe hierdie UAV-gebaseerde werkvloeie presteer onder verskillende klimaat- en operasionele beperkings verdere waarde toevoeg tot bosbestuur. Hierdie tesis beklemtoon die belangrikheid van ‘n ten volle geïntegreerde UAV-gebaseerde werkvloei vir bosmonitering en -bestuur. Dit toon hoe geopti- maliseerde vlugparameters data-insameling verbeter, hoe nuwe rekenaarme- todes doeltreffende en akkurate ekstraksie van sleutelbosinventarismetrieke moontlik maak, en hoe die integrasie van afstandswaarnemingsdata silvikul- turele beplanning en bestuursbesluite ondersteun. Deur hierdie end-tot-end- proses te stroomlyn, lê die navorsing die grondslag vir meer ingeligte, databe- stuurde benaderings tot volhoubare bosbou. Hierdie vooruitgang bied waarde- volle instrumente vir die monitering van bosstrukture, die rigting van silvikul- turele ingrypings en die ondersteuning van ekostelselweerstand en hulpbron- volhoubaarheid te midde van voortdurende omgewingsuitdagings. Doctoral 2025-06-02T10:28:59Z 2025-06-02T10:28:59Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132276 en Stellenbosch University xiv, 110 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Photogrammetry -- Digital techniques Silvicultural systems -- Decision making Forest management -- Data processing Drone aircraft -- Remote sensing Unmanned Aerial Vehicles -- Mathematical models Forests and forestry -- Planning -- Evaluation UCTD Seifert, Erich Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title | Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title_full | Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title_fullStr | Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title_full_unstemmed | Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title_short | Optimising UAV photogrammetry for forest structure assessment and silvicultural planning |
| title_sort | optimising uav photogrammetry for forest structure assessment and silvicultural planning |
| topic | Photogrammetry -- Digital techniques Silvicultural systems -- Decision making Forest management -- Data processing Drone aircraft -- Remote sensing Unmanned Aerial Vehicles -- Mathematical models Forests and forestry -- Planning -- Evaluation UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/132276 |
| work_keys_str_mv | AT seiferterich optimisinguavphotogrammetryforforeststructureassessmentandsilviculturalplanning |