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UAV-Based Digital Terrain Model Generation to Support Accurate Inventories in Mediterranean Forests

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Advances in Design Engineering III (INGEGRAF 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Digital Aerial Photogrammetry from Unmanned Aerial Vehicle (UAV-DAP) based on Structure from Motion Multiview Stereo (SfM-MVS) approach has demonstrated to be an efficient technique to meet the needs of highly accurate monitoring and forest sustainable management. In this work, a methodology based on UAV-DAP to generate high quality Digital Terrain Models (DTMs) in Mediterranean forest has been developed. UAV overlapping images were collected over eighteen Aleppo pine (Pinus halepensis Mill.) square plots of 100 m side (1 ha) located at Sierra de María-Los Vélez Natural Park (Almería, Spain). These plots were divided into 31 references subplots of 25 m side. The workflow devised to generate the corresponding DTMs consisted of: (i) SfM-based image aligning, (ii) point cloud generation, (iii) ground points filtering, (iv) outliers removal, and (v) DTM interpolation from previously labeled ground points. A very accurate reference DTM, derived from terrestrial laser scanning (TLS), was employed to accomplish the image-based DTM vertical accuracy assessment. The obtained results showed that the UAV image-based DTMs presented a reasonably low vertical bias of −9.38 cm, which meant that UAV-DAP DTM overestimated the z reference ground values provided by TLS. In addition, a low vertical random error of 4.68 cm (average value for all subplots in terms of standard deviation) was computed from the z-differences population. According to these results, UAV image-based DTM can be considered well suited to serve as ground reference to support Mediterranean forest inventories.

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References

  1. Forest-Europe (2020) State of Europe’s Forests 2020

    Google Scholar 

  2. Dong L, Zhang L, Li F (2014) A compatible system of biomass equations for three conifer species in Northeast. China For Ecol Manag 329:306–317. https://doi.org/10.1016/J.FORECO.2014.05.050

    Article  Google Scholar 

  3. McRoberts RE, Tomppo EO (2007) Remote sensing support for national forest inventories. Remote Sens Environ 110:412–419. https://doi.org/10.1016/J.RSE.2006.09.034

    Article  Google Scholar 

  4. White JC, Coops NC, Wulder MA, Vastaranta M, Hilker T, Tompalski P (2016) Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens 42:619–641. https://doi.org/10.1080/07038992.2016.1207484

    Article  Google Scholar 

  5. Gómez C, Alejandro P, Hermosilla T, Montes F, Pascual C, Ruiz LÁ, Álvarez-Taboada F, Tanase MA, Valbuena R (2019) Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. For Syst 28:2171–9292. https://doi.org/10.5424/FS/2019281-14221

    Article  Google Scholar 

  6. Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S, Zhu YH (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manag 128:109–120. https://doi.org/10.1016/S0378-1127(99)00278-9

    Article  Google Scholar 

  7. Næsset E, Gobakken T, Holmgren J, Hyyppä H, Hyyppä J, Maltamo M, Nilsson M, Olsson H, Persson Å, Söderman U (2004) Laser scanning of forest resources: the nordic experience. Scand J For Res 19:482–499. https://doi.org/10.1080/02827580410019553

    Article  Google Scholar 

  8. Rahlf J, Breidenbach J, Solberg S, Næsset E, Astrup R (2014) Comparison of four types of 3D data for timber volume estimation. Remote Sens Environ 155:325–333. https://doi.org/10.1016/J.RSE.2014.08.036

    Article  Google Scholar 

  9. Maltamo M, Næsset E, Vauhkonen J eds (2014) Forestry applications of airborne laser scanning. Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-017-8663-8

  10. Giannetti F, Chirici G, Gobakken T, Næsset E, Travaglini D, Puliti S (2018) A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data. Remote Sens Environ 213:195–205. https://doi.org/10.1016/J.RSE.2018.05.016

    Article  Google Scholar 

  11. Kumar L, Sinha P, Taylor S, Alqurashi AF (2015) Review of the use of remote sensing for biomass estimation to support renewable energy generation. J Appl Remote Sens 9:097696. https://doi.org/10.1117/1.JRS.9.097696

    Article  Google Scholar 

  12. Lin Y-C, Liu J, Fei S, Habib A (2021) Leaf-off and leaf-on UAV LiDAR surveys for single-tree inventory in forest plantations. Drones. 5:115. https://doi.org/10.3390/drones5040115

    Article  Google Scholar 

  13. Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53:193–203. https://doi.org/10.1016/S0924-2716(98)00009-4

    Article  Google Scholar 

  14. Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, Maltamo M (2008) Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29:1339–1366. https://doi.org/10.1080/01431160701736489

    Article  Google Scholar 

  15. Holopainen M, Vastaranta M, Hyyppä J (2014) Outlook for the next generation’s precision forestry in Finland. For 5:1682–1694. https://doi.org/10.3390/F5071682

  16. Potapov P, Li X, Hernandez-Serna A, Tyukavina A, Hansen MC, Kommareddy A, Pickens A, Turubanova S, Tang H, Silva CE, Armston J, Dubayah R, Blair JB, Hofton M (2021) Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens Environ 253:112165. https://doi.org/10.1016/J.RSE.2020.112165

    Article  Google Scholar 

  17. Aguilar FJ, Nemmaoui A, Aguilar MA, Peñalver A (2019) Fusion of terrestrial laser scanning and RPAS image based point cloud in mediterranean forest inventories. Dyna Ing e Ind 94:131–136. https://doi.org/10.6036/8892

    Article  Google Scholar 

  18. Giannetti F, Puletti N, Puliti S, Travaglini D, Chirici G (2020) Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests. Ecol Indic 117:106513. https://doi.org/10.1016/J.ECOLIND.2020.106513

    Article  Google Scholar 

  19. Otero V, Van De Kerchove R, Satyanarayana B, Martínez-Espinosa C, Fisol MA, Bin I, Bin 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 Manage 411:35–45. https://doi.org/10.1016/J.FORECO.2017.12.049

  20. Jayathunga S, Owari T, Tsuyuki S (2018) Evaluating the performance of photogrammetric products using fixed-wing UAV imagery over a mixed conifer–broadleaf forest: comparison with airborne laser scanning. Remote Sens 10:187. https://doi.org/10.3390/RS10020187

    Article  Google Scholar 

  21. Kachamba DJ, Ørka HO, Gobakken T, Eid T, Mwase W (2016) Biomass estimation using 3D data from unmanned aerial vehicle imagery in a tropical woodland. Remote Sens 8:968. https://doi.org/10.3390/RS8110968

    Article  Google Scholar 

  22. Roşca S, Suomalainen J, Bartholomeus H, Herold M (2018) Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests. Interface Focus 8:20170038. https://doi.org/10.1098/RSFS.2017.0038

    Article  Google Scholar 

  23. Aguilar FJ, Rivas JR, Nemmaoui A, Peñalver A, Aguilar MA (2019) UAV-based digital terrain model generation under leaf-off conditions to support teak plantations inventories in tropical dry forests. a case of the coastal region of Ecuador. Sensors 19:1934. https://doi.org/10.3390/s19081934

  24. Iglhaut J, Cabo C, Puliti S, Piermattei L, O’Connor J, Rosette J (2019) Structure from motion photogrammetry in forestry: a review. Curr For Rep 5:155–168. https://doi.org/10.1007/s40725-019-00094-3

    Article  Google Scholar 

  25. Dandois JP, Ellis EC (2013) High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens Environ 136:259–276. https://doi.org/10.1016/J.RSE.2013.04.005

    Article  Google Scholar 

  26. Puliti S, Dash JP, Watt MS, Breidenbach J, Pearse GD (2020) A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties. For An Int J For Res 93:150–162. https://doi.org/10.1093/FORESTRY/CPZ057

    Article  Google Scholar 

  27. Wallace L, Lucieer A, Malenovský Z, Turner D, Vopěnka P, Wallace L, Lucieer A, Malenovský Z, Turner D, Vopěnka 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. https://doi.org/10.3390/f7030062

    Article  Google Scholar 

  28. Puliti S, Ørka HO, Gobakken T, Næsset E (2015) Inventory of small forest areas using an unmanned aerial system. Remote Sens 7:9632–9654. https://doi.org/10.3390/RS70809632

    Article  Google Scholar 

  29. 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 49:1348. https://doi.org/10.14214/SF.1348

  30. 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. https://doi.org/10.3390/f4040922

    Article  Google Scholar 

  31. Ota T, Ogawa M, Shimizu K, Kajisa T, Mizoue N, Yoshida S, Takao G, Hirata Y, Furuya N, Sano T, Sokh H, Ma V, Ito E, Toriyama J, Monda Y, Saito H, Kiyono Y, Chann S, Ket N (2015) Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest. Forests 6:3882–3898. https://doi.org/10.3390/F6113882

    Article  Google Scholar 

  32. Chirici G, McRoberts RE, Fattorini L, Mura M, Marchetti M (2016) Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework. Remote Sens Environ 174:1–9. https://doi.org/10.1016/J.RSE.2015.11.010

    Article  Google Scholar 

  33. Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci 33:110–117. https://doi.org/10.1016/j.isprsjprs.2005.10.005

  34. Felicísimo AM (1994) Parametric statistical method for error detection in digital elevation models. ISPRS J Photogramm Remote Sens 49:29–33. https://doi.org/10.1016/0924-2716(94)90044-2

    Article  Google Scholar 

  35. Aguilar FJ, Aguilar MA, Blanco JLJL, Nemmaoui A, García Lorca AMA (2016) Analysis and validation of grid dem generation based on gaussian markov random field. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLI-B2:277–284. https://doi.org/10.5194/isprs-archives-XLI-B2-277

  36. Muir J, Goodwin N, Armston J, Phinn S, Scarth P, Muir J, Goodwin N, Armston J, Phinn S, Scarth P (2017) An accuracy assessment of derived digital elevation models from terrestrial laser scanning in a sub-tropical forested environment. Remote Sens 9:843. https://doi.org/10.3390/rs9080843

    Article  Google Scholar 

  37. Trochta J, Krůček M, Vrška T, Král K (2017) 3D forest: an application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0176871

    Article  Google Scholar 

  38. Aguilar MA, Saldaña M del M, Aguilar FJ (2014) Generation and quality assessment of stereo-extracted DSM from geoeye-1 and worldview-2 imagery. IEEE Trans Geosci Remote Sens 52:1259–1271. https://doi.org/10.1109/TGRS.2013.2249521

  39. Ministerio de Fomento de España: Plan Nacional de Ortofotografía Aérea, http://pnoa.ign.es/especificaciones-tecnicas. Last accessed 19 June 2017

  40. Aguilar FJ, Mills JP, Delgado J, Aguilar MA, Negreiros JG, Pérez JL (2010) Modelling vertical error in LiDAR-derived digital elevation models. ISPRS J Photogramm Remote Sens 65:103–110. https://doi.org/10.1016/J.ISPRSJPRS.2009.09.003

    Article  Google Scholar 

  41. Goodwin NR, Coops NC, Culvenor DS (2006) Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens Environ 103:140–152. https://doi.org/10.1016/J.RSE.2006.03.003

    Article  Google Scholar 

  42. Su JG, Bork E (2006) Influence of vegetation, slope, and lidar sampling angle on DEM accuracy. Photogramm Eng Remote Sens 72:1265–1274. https://doi.org/10.7939/R3571821W

    Article  Google Scholar 

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Acknowledgements

This study was funded by the following projects: (1) “Enabling interdisciplinary collaboration to foster Mediterranean forest sustainable management and socio-economic valuation (ECO2-FOREST)” (“Proyecto Retos Junta de Andalucía, Spain. Grant number P18-RT- 2327”). (2) “Intervention strategies for an integrated and sustainable management of the Mediter- ranean forest based on an interdisciplinary analysis and its economic assessment” (“Programa Operativo FEDER Andalucía 2014-2020, Spain. Grant number UAL2020-SEJ-D1931”). “The au- thors wish to thank the support of the Territorial Delegation in Almeria of the Ministry of Agri- culture, Fisheries and Sustainable Development of Andalusia. Special thanks are due to Jaime de Lara, Director-Conservator of the Natural Park of Sierra de María-Los Vélez. Finally, this work takes part of the general research lines promoted by the Agrifood Campus of International Excel- lence ceiA3, Spain (http://www.ceia3.es/)”.

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Authors and Affiliations

  1. Department of Engineering and Research Centre CIAIMBITAL, University of Almería, Carretera de Sacramento s/n, La Cañada de San Urbano, 04120, Almería, Spain

    Abderrahim Nemmaoui, Fernando J. Aguilar & Manuel A. Aguilar

Authors
  1. Abderrahim Nemmaoui
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  2. Fernando J. Aguilar
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  3. Manuel A. Aguilar
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Correspondence to Abderrahim Nemmaoui .

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Editors and Affiliations

  1. Departamento de Estructuras, Construcción y Expresión Gráfica, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain

    Francisco Cavas-Martínez

  2. Departamento de Expresión Gráfica, Diseño y Proyectos, Universidad de Málaga, Málaga, Spain

    Manuel D. Marín Granados

  3. Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza, Zaragoza, Spain

    Ramón Mirálbes Buil

  4. Departamento de Expresión Gráfica, Diseño y Proyectos, Universidad de Málaga, Málaga, Spain

    Oscar D. de-Cózar-Macías

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Nemmaoui, A., Aguilar, F.J., Aguilar, M.A. (2023). UAV-Based Digital Terrain Model Generation to Support Accurate Inventories in Mediterranean Forests. In: Cavas-Martínez, F., Marín Granados, M.D., Mirálbes Buil, R., de-Cózar-Macías, O.D. (eds) Advances in Design Engineering III. INGEGRAF 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-20325-1_45

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  • DOI: https://doi.org/10.1007/978-3-031-20325-1_45

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