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Landscape Ecology

, Volume 33, Issue 1, pp 9–18 | Cite as

A practical method for creating a digital topographic surface for ecological plots using ground-based measurements

  • Jessie C. BuettelEmail author
  • Stefania Ondei
  • Barry W. Brook
Short Communication

Abstract

Context

Digital elevation models (DEMs) are widely used in landscape ecology to link topographic features with biotic and abiotic factors. However, to date, high-resolution, affordable, and easy to process elevation data are not available for many regions.

Objectives

Here we propose a field-based method for efficiently and inexpensively collecting or analysing already existing slope data. We compare the field approach to two commonly used remote sensing techniques to test the similarly of the DEMs using different methods.

Methods

To provide an ecological example of the method, we selected four 1-ha forest plots and compared the DEM generated by using our field method with those derived from: (i) coarse (~ 30 m pixel) data from the Shuttle Radar Topography Mission and (ii) high-resolution (~ 1 m) data from Light Detection and Ranging devices (LiDAR).

Results

Field- and LiDAR-based DEMs showed strong concordance in two of the four sites. The sites where field-based and LiDAR DEMs substantially differed, suffered from relatively few LiDAR sampling points. Diagnostic tests suggested that the field–LiDAR discrepancy was due to dense over-storey vegetation, which reduced LiDAR’s accuracy due to a failure to penetrate the forest canopy adequately in some areas.

Conclusions

Our method has the advantage of being quick and cheap to collect yet able to produce small-scale (plot-scale) DEMs of high quality. By using the R-code we have provided, ecologists will be able to use slope data (collected using any means) to generate a DEM without the need of specific skills in spatial sciences.

Keywords

Digital elevation models DEM Topography Plots Algorithm Remote sensing Slope LiDAR SRTM Forests 

Notes

Funding

This study was supported by Australian Research Council Laureate Fellowship under award number FL160100101. Jessie C. Buettel and Barry W. Brook were supported by Centre of Excellence for Australian Biodiversity and Heritage under award number CE170100015.

Supplementary material

10980_2017_598_MOESM1_ESM.txt (5 kb)
Supplementary material 1 (TXT 5 kb)
10980_2017_598_MOESM2_ESM.txt (12 kb)
Supplementary material 2 (TXT 11 kb)
10980_2017_598_MOESM3_ESM.docx (524 kb)
Supplementary material 3 (DOCX 524 kb)
10980_2017_598_MOESM4_ESM.pdf (3.5 mb)
Supplementary material 4 (PDF 3609 kb)

References

  1. Bader MY, Ruijten JJA (2008) A topography-based model of forest cover at the alpine tree line in the tropical Andes. J Biogeogr 35(4):711–723CrossRefGoogle Scholar
  2. Bosse M, Zlot R, Flick P (2012) Zebedee: design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Trans Robot 28(5):1104–1119CrossRefGoogle Scholar
  3. Brasington J, Langham J, Rumsby B (2003) Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport. Geomorphology 53(3–4):299–316CrossRefGoogle Scholar
  4. Buettel JC, Ondei S, Brook BW (2017) Look down to see what’s up: a systematic overview of treefall dynamics in forests. Forests 8(4):123CrossRefGoogle Scholar
  5. Chang K-t, Tsai B-w (1991) The effect of DEM resolution on slope and aspect mapping. Cartogr Geogr Inf Syst 18(1):69–77Google Scholar
  6. Coops NC, Wulder MA, Culvenor DS, St-Onge B (2004) Comparison of forest attributes extracted from fine spatial resolution multispectral and LiDAR data. Can J Remote Sens 30(6):855–866CrossRefGoogle Scholar
  7. Dixon B, Earls J (2009) Resample or not?! Effects of resolution of DEMs in watershed modeling. Hydrol Process 23:1714–1724CrossRefGoogle Scholar
  8. Ehrenfeld JG (1995) Microtopography and vegetation in Atlantic white cedar swamps: the effects of natural disturbances. Can J Bot 73(3):474–484CrossRefGoogle Scholar
  9. Erdogan S (2009) A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surf Process Landf 34(3):366–376CrossRefGoogle Scholar
  10. Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The Shuttle Radar Topography Mission. Rev Geophys.  https://doi.org/10.1029/2005RG000183 Google Scholar
  11. Fisher PF, Tate NJ (2006) Causes and consequences of error in digital elevation models. Prog Phys Geogr 30(4):467–489CrossRefGoogle Scholar
  12. Franklin SE (2001) Remote sensing for sustainable forest management. CRC Press, Boca RatonCrossRefGoogle Scholar
  13. Gao J (1997) Resolution and accuracy of terrain representation by grid DEMs at a micro-scale. Int J Geogr Inf Sci 11(2):199–212CrossRefGoogle Scholar
  14. Gong J, Li Z, Zhu Q, Sui H, Zhou Y (2000) Effects of various factors on the accuracy of DEMs: an intensive experimental investigation. Photogramm Eng Remote Sens 66(9):1113–1117Google Scholar
  15. Guo Q, Li W, Yu H, Alvarez O (2010) Effects of topographic variability and LiDAR sampling density on several DEM interpolation methods. Photogramm Eng Remote Sens 76(6):701–712CrossRefGoogle Scholar
  16. Hodgson ME, Bresnahan P (2004) Accuracy of airborne LiDAR-derived elevation. Photogramm Eng Remote Sens 70(3):331–339CrossRefGoogle Scholar
  17. Hu J (1995) Methods of generating surfaces in environmental GIS applications. In: 1995 ESRI user conference proceedingsGoogle Scholar
  18. James TD, Murray T, Barrand NE, Barr SL (2006) Extracting photogrammetric ground control from LiDAR DEMs for change detection. Photogramm Rec 21(116):312–328CrossRefGoogle Scholar
  19. Lassueur T, Joost S, Randin CF (2006) Very high resolution digital elevation models: do they improve models of plant species distribution? Ecol Model 198(1–2):139–153CrossRefGoogle Scholar
  20. Linn RR, Winterkamp JL, Weise DR, Edminster C (2010) A numerical study of slope and fuel structure effects on coupled wildfire behaviour. Int J Wildland Fire 19(2):179–201CrossRefGoogle Scholar
  21. Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr 32(1):31–49CrossRefGoogle Scholar
  22. Liu X, Zhang Z, Peterson J, Chandra S (2007) LiDAR-derived high quality ground control information and DEM for image orthorectification. GeoInformatica 11(1):37–53CrossRefGoogle Scholar
  23. Mitchard ETA, Saatchi SS, White LJT, Abernethy KA, Jeffery KJ, Lewis SL, Collins M, Lefsky MA, Leal ME, Woodhouse IH, Meir P (2012) Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences 9:179–191Google Scholar
  24. Moser K, Ahn C, Noe G (2007) Characterization of microtopography and its influence on vegetation patterns in created wetlands. Wetlands 27(4):1081–1097CrossRefGoogle Scholar
  25. O’Loughlin FE, Paiva RCD, Durand M, Alsdorf DE, Bates PD (2016) A multi-sensor approach towards a global vegetation corrected SRTM DEM product. Remote Sens Environ 182:49–59CrossRefGoogle Scholar
  26. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  27. Schumann G, Matgen P, Cutler MEJ, Black A, Hoffmann L, Pfister L (2008) Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS J Photogramm Remote Sens 63(3):283–296CrossRefGoogle Scholar
  28. Seibert J, Stendahl J, Sorensen R (2007) Topographical influences on soil properties in boreal forests. Geoderma 141(1–2):139–148CrossRefGoogle Scholar
  29. Su J, Bork E (2006) Influence of vegetation, slope, and LiDAR sampling angle on DEM accuracy. Photogramm Eng Remote Sens 72(11):1265–1274CrossRefGoogle Scholar
  30. Trumbore S, Brando P, Hartmann H (2015) Forest health and global change. Science 349(6250):814–818CrossRefPubMedGoogle Scholar
  31. Warren SD, Hohmann MG, Auerswald K, Mitasova H (2004) An evaluation of methods to determine slope using digital elevation data. CATENA 58(3):215–233CrossRefGoogle Scholar
  32. Wood SW, Murphy BP, Bowman DMJS (2011) Firescape ecology: how topography determines the contrasting distribution of fire and rain forest in the south-west of the Tasmanian Wilderness World Heritage Area. J Biogeogr 38(9):1807–1820CrossRefGoogle Scholar
  33. Wood SW, Prior LD, Stephens HC, Bowman DM (2015) Macroecology of Australian tall eucalypt forests: baseline data from a continental-scale permanent plot network. PLoS ONE 10(9):e0137811CrossRefPubMedPubMedCentralGoogle Scholar
  34. Yin Z-Y, Wang X (1999) A cross-scale comparison of drainage basin characteristics derived from digital elevation models. Earth Surf Process Landf 24(6):557–562CrossRefGoogle Scholar
  35. Zellweger F, Morsdorf F, Purves RS, Braunisch V, Bollmann K (2014) Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment. Biodivers Conserv 23(2):289–307CrossRefGoogle Scholar
  36. Zhang X, Drake NA, Wainwright J, Mulligan M (1999) Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution. Earth Surf Process Landf 24(9):763–779CrossRefGoogle Scholar
  37. Ziadat FM (2007) Effect of contour intervals and grid cell size on the accuracy of DEMs and slope derivatives. Trans GIS 11(1):67–81CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Biological Sciences and ARC Centre of Excellence for Australian Biodiversity and HeritageUniversity of TasmaniaHobartAustralia

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