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Accuracy Assessment of Normalized Digital Surface Models from Aerial Images Regarding Tree Height Determination in Saxony, Germany

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Abstract

The data acquisition from normalized digital surface models (nDSM), based on regularly conducted aerial image surveys, represents an efficient opportunity for forest enterprises to ensure topicality as well as quality for tree height information. Two of such image data sets—L60 (60% along-track/30% across-track) and L80 (80/40%)—were each used to create an nDSM for a 23.5 km\(^{2}\) large study area in Saxony. The objective of the present study was to assess the accuracy of both nDSMs and to examine the influence of image overlap, tree type and terrain slope on the results of this accuracy assessment. Point clouds were initially extracted from the corresponding images using semi-global matching and were eventually used to create a digital surface model. An nDSM was produced by subtracting a digital terrain model to get absolute tree heights. The comparison between this nDSM and terrestrial measurements (n = 51) resulted in root mean square errors between 2.1 m and 2.2 m. Although opposing median values were found for the nDSMs based on L60 (−0.3 m) and L80 (0.5 m), no significance was found between the two models (Wilcoxon test, p = 0.16). However, large overlapping areas and lower terrain slopes have a positive effect on the completeness of the nDSM (L60: 92.9%, L80: 99.2%). Although the small number of measurements does not allow for more than the identification of a tendency, these results show the potential of image-based nDSMs for the regular update of tree height information in Saxony and beyond.

Zusammenfassung

Bewertung der Genauigkeit der Baumhöhenbestimmung in Sachsen auf der Basis von normierten Oberflächenmodellen. Die Datengewinnung aus normierten Digitalen Oberflächenmodellen (nDOM) auf Basis regelmäßig durchgeführter Luftbildaufnahmen stellt für Forstbetriebe eine effiziente Möglichkeit dar, Aktualität und Qualität der Baumhöheninformationen zu gewährleisten. Unter Verwendung zweier derartiger Luftbilddatensätze—L60 (60% Längsüberlappung/30% Querüberlappung) and L80 (80%/40%)—wurde jeweils ein nDOM für ein 23.5 km\(^{2}\) großes Untersuchungsgebiet in Sachsen generiert. Ziel der vorliegenden Studie war es, die Genauigkeit der beiden nDOMs zu untersuchen und den Einfluss der Bildüberlappung, der Baumart sowie der Geländeneigung auf das Resultat zu analysieren. Unter Verwendung des Semi-Global Matching wurden Punktwolken aus den entsprechenden Luftbildern extrahiert und diese zur Erzeugung eines Digitalen Oberflächenmodells genutzt. Durch Subtraktion eines Digitalen Geländemodells entstand jeweils ein die Baumhöhen wiedergebendes nDOM, dessen Genauigkeit durch den Vergleich mit terrestrischen Referenzmessungen (n = 51) untersucht wurde. Im Rahmen dieser Evaluation ergaben sich Root Mean Square Errors von 2.1 m bis 2.2 m. Obwohl sich für L60 (−0.3 m) und L80 (0.5 m) gegensätzliche Medianwerte ergaben, wurde keine Signifikanz zwischen den beiden Modellen festestellt (Wilcoxon-Test, p = 0.16). Allerdings haben große Überlappungsbereiche sowie eine geringe Hangneigung einen positiven Einfluss auf die Vollständigkeit des Höhenmodells (L60: 92.9%, L80: 99.2%). Obwohl die geringe Anzahl an Referenzmessungen nur Rückschlüsse auf Genauigkeitstrends zulässt, zeigen diese Resultate das Potential von Luftbild-basierten nDOMs für die regelmäßige Aktualisierung von Baumhöheninformationen in Sachsen und vergleichbaren Regionen.

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References

  • Balenović I, Seletković A, Pernar R, Jazbec A (2015) Estimation of the mean tree height of forest stands by photogrammetricc measurement using digital aerial images of high spatiall resolution. Ann For Res 58(1):125–143. doi:10.15287/afr.2015.300

    Google Scholar 

  • Baltsavias EP, Gruen A, Eisenbeiss H, Zhang L, Waser LT (2008) High-quality image matching and automated generation of 3D tree models. Int J Remote Sens 29(5):1243–1259. doi:10.1080/01431160701736513

  • Bühler Y, Marty M, Ginzler C (2012) High resolution DEM generation in high-alpine terrain using airborne remote sensing techniques. Trans GIS 16(5):635–647. doi:10.1111/j.1467-9671.2012.01331.x

  • Ginzler C, Hobi ML (2015) Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss National Forest Inventory. Remote Sens 7(4):4343–4370. doi:10.3390/rs70404343

    Article  Google Scholar 

  • Ginzler C, Hobi ML (2016) Das aktuelle Vegetationshöhenmodell der Schweiz: spezifische Anwendungen im Waldbereich. Schweiz Z Forst 167(3):128–135. doi:10.3188/szf.2016.0128

    Article  Google Scholar 

  • Hexagon Geospatial (2015) Imagine photogrammetry. A complete suite of photogrammetric production tools. Hexagon AB, Stockholm

    Google Scholar 

  • Hirschmüller H (2008) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341. doi:10.1109/TPAMI.2007.1166

    Article  Google Scholar 

  • Hirschmüller H, Bucher T (2010) Evaluation of digital surface models by semi-global matching. DPFG, Vienna

    Google Scholar 

  • Hobi ML, Ginzler C (2012) Accuracy assessment of digital surface models based on WorldView-2 and ADS80 stereo remote sensing data. Sensors 12(5):6347–6368. doi:10.3390/s120506347

    Article  Google Scholar 

  • Holopainen M, Vastaranta M, Hyyppä J (2014) Outlook for the next generation’s precision forestry in Finland. Forests 5(7):1682–1694. doi:10.3390/f5071682

    Article  Google Scholar 

  • Höhle J, Höhle M (2009) Accuracy assessment of digital elevation models by means of robust statistical methods. ISPRS J Photogramm Remote Sens 64(4):398–406. doi:10.1016/j.isprsjprs.2009.02.003

    Article  Google Scholar 

  • Järnstedt J, Pekkarinen A, Tuominen S, Ginzler C, Holopainen M, Viitala R (2012) Forest variable estimation using a high-resolution digital surface model. ISPRS J Photogramm Remote Sens 74:78–84. doi:10.1016/j.isprsjprs.2012.08.006

    Article  Google Scholar 

  • Nurminen K, Karjalainen M, Yu X, Hyyppä J, Honkavaara E (2013) Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS J Photogramm Remote Sens 83:104–115. doi:10.1016/j.isprsjprs.2013.06.005

    Article  Google Scholar 

  • R Core Team (2014) R: A language and environment for statistical computing. In: R Foundation for Statistical Computing. http://www.r-project.org. Accessed 09 Sep 2016

  • Rahman MM, Govindarajulu Z (2010) A modification of the test of Shapiro and Wilk for normality. J Appl Stat 24(2):219–236. doi:10.1080/02664769723828

    Article  Google Scholar 

  • Stepper C, Straub C, Pretzsch H (2015) Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe. Can J For Res 45(1):111–123. doi:10.1139/cjfr-2014-0297

    Article  Google Scholar 

  • Straub C, Stepper C (2016) Using digital aerial photogrammetry and the random forest approach to model forest inventory attributes in beech- and spruce-dominated Central European Forests. Photogramm Fernerkund Geoinf 3:109–123. doi:10.1127/pfg/2016/0292

    Article  Google Scholar 

  • Vastaranta M, Wulder MA, White JC, Pekkarinen A, Tuominen S, Ginzler C, Kankare V, Holopainen M, Hyppää J, Hyppää H (2013) Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update. Can J Remote Sens 39(5):382–395. doi:10.5589/m13-046

    Article  Google Scholar 

  • White JC, Wulder MA, Vastaranta M, Coops NC, Pitt D, Woods M (2013) The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4(3):518–536. doi:10.3390/f4030518

    Article  Google Scholar 

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Zimmermann, S., Hoffmann, K. Accuracy Assessment of Normalized Digital Surface Models from Aerial Images Regarding Tree Height Determination in Saxony, Germany. PFG 85, 257–263 (2017). https://doi.org/10.1007/s41064-017-0021-4

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