Skip to main content
Log in

Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

Lacunarity analysis is frequently used in multiscale and spatial pattern studies. However, the explanation for the lacunarity analysis results is limited mainly at a qualitative description level. In other words, this approach can be used to judge whether the spatial pattern of the objective is regular, random or aggregated in space. The lacunarity analysis, however, cannot afford many quantitative information. Therefore, this study proposed the lacunarity variation index (LVI) to reflect the rates of variation of lacunarity with the resolution. In comparison with lacunarity analysis, the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data. The experiment showed that different slope types in the Loess Plateau display aggregated patterns, and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data. This study can improve the spatial pattern analysis and scale detecting methods, as well as provide a new method for landscape and vegetation community pattern analyses. Lacunarity analysis is frequently used in multiscale and spatial pattern studies. However, the explanation for the lacunarity analysis results is limited mainly at a qualitative description level. In other words, this approach can be used to judge whether the spatial pattern of the objective is regular, random or aggregated in space. The lacunarity analysis, however, cannot afford many quantitative information. Therefore, this study proposed the lacunarity variation index (LVI) to reflect the rates of variation of lacunarity with the resolution. In comparison with lacunarity analysis, the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data. The experiment showed that different slope types in the Loess Plateau display aggregated patterns, and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data. This study can improve the spatial pattern analysis and scale detecting methods, as well as provide a new method for landscape and vegetation community pattern analyses.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allain C, Cloitre M (1991). Diffraction on Fractals. Scaling Phenomena in Disordered Systems. New York: Springer

    Google Scholar 

  • Anderson C D, Rosenberg M S (2016). Spatial pattern analysis. In: David Gibson ed, Oxford Bibliographies in Ecology. New York: Oxford University Press

    Google Scholar 

  • Cao Y Z (1983). Slope features and soil erosion on the loess region. Geogr Res, 2(3): 19–28 (in Chinese)

    Google Scholar 

  • Dale M R T, Mah M (1998). The use of wavelets for spatial pattern analysis in ecology. J Veg Sci, 9(6): 805–814

    Article  Google Scholar 

  • Dale M R T (2000). Lacunarity analysis of spatial pattern: a comparison. Landsc Ecol, 15(5): 467–478

    Article  Google Scholar 

  • Dale M R T, Gibson D J (2002). Spatial pattern analysis in plant ecology. Q Rev Biol, 15(1): 195–196

    Google Scholar 

  • Dale M R T, Dixon P, Fortin M J, Legendre P, Myers D E, Rosenberg M S (2002). Conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25(5): 558–577

    Article  Google Scholar 

  • Dikau R, Brabb E E, Mark R M. (1991). Landform classification of New Mexico by computer. Open-File Report.

  • Dougherty G, Henebry G M (2002). Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis. Med Eng Phys, 24(2): 129–138

    Article  Google Scholar 

  • Drăguţ L, Blaschke T (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3–4): 330–344

    Article  Google Scholar 

  • Falcon-Lang H J (2003). Late carboniferous tropical dryland vegetation in an alluvial-plain setting, joggins, Nova Scotia, Canada. Palaios, 18(3): 197–211

    Article  Google Scholar 

  • Gao Y, Alexander E C Jr, Barnes R J (2005). Karst database implementation in minnesota: analysis of sinkhole distribution. Environmental Geology, 47(8): 1083–1098

    Article  Google Scholar 

  • Gefen Y, Meir Y, Mandelbrot B B, Aharony A (1983). Geometric implementation of hypercubic lattices with noninteger dimensionality by use of low lacunarity fractal lattices. Phys Rev Lett, 50(3): 145–148

    Article  Google Scholar 

  • Gefen Y, Aharony A, Mandelbrot B B. (1984). Phase transitions on fractals. iii. infinitely ramified lattices. Journal of Physics A General Physics, 17(6): 1277–1289

    Article  Google Scholar 

  • Greig-Smith P (1979). Pattern in vegetation. J Ecol, 67(3): 755–779

    Article  Google Scholar 

  • Hammond E H (2005). Analysis of properties in land form geography: an application to broad-scale land form mapping. Ann Assoc Am Geogr, 54(1): 11–19

    Article  Google Scholar 

  • Heltshe J F, Ritchey T A (1984). Spatial pattern detection using quadrat samples. Biometrics, 40(4): 877–885

    Article  Google Scholar 

  • Heupel M R, Simpfendorfer C A (2005). Quantitative analysis of aggregation behavior in juvenile blacktip sharks. Marine Biology (Berlin), 147(5): 1239–1249

    Article  Google Scholar 

  • Huang X L, Ding H, Na J M, Tang G A (2017). Theories and methods of space-for-time substitution in geomorphology. Acta Geogr Sin, 72(1): 94–104

    Google Scholar 

  • Keersmaecker M L D, Frankhauser P, Thomas I (2003). Using fractal dimensions for characterizing intra-urban diversity: the example of Brussels. Geogr Anal, 35(4): 310–328

    Article  Google Scholar 

  • Kristensen L, Olsen J, Weiner J, Griepentrog H W. (2006). Describing the spatial pattern of crop plants with special reference to crop-weed competition studies. Field Crops Research, 96(2–3): 0–215

    Google Scholar 

  • Larsen D R, Bliss L C (1998). An analysis of structure of tree seedling populations on a lahar. Landsc Ecol, 13(5): 307–323

    Article  Google Scholar 

  • Li F Y (2010). DEM and image-based loess slope segmentation. In: 3rd International Congress on Image and Signal Processing

  • Li S J, Xiong L Y, Tang G A, Strobl J (2020). Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery. Geomorphology, 354: 107045

    Article  Google Scholar 

  • Lin B, Yang Z R (1986). A suggested lacunarity expression for sierpinski carpets. J Phys Math Gen, 19(2): 49–52

    Article  Google Scholar 

  • Mandelbrot B B, Wheeler J A (1983). The fractal geometry of nature. Am J Phys, 51(3): 286–287

    Article  Google Scholar 

  • McGarigal K (2016). Concepts of scale. In: Landscape ecology course notes. Amherst: Massachusetts University

    Google Scholar 

  • McIntyre N E, Wiens J A (2000). A novel use of the lacunarity index to discern landscape function. Landsc Ecol, 15(4): 313–321

    Article  Google Scholar 

  • Oliver M A, Webster R (1986). Semi-variograms for modelling the spatial pattern of landform and soil properties. Earth Surf Process Landf, 11(5): 491–504

    Article  Google Scholar 

  • Perry J N, Liebhold A M, Rosenberg M S, Dungan J, Miriti M, Jakomulska A, Citron-Pousty S (2002). Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography, 25(5): 578–600

    Article  Google Scholar 

  • Plante M, Lowell K, Potvin F, Boots B, Fortin M J (2004). Studying deer habitat on Anticosti Island, Quebec: relating animal occurrences and forest map information. Ecol Modell, 174(4): 387–399

    Article  Google Scholar 

  • Plotnick R E (1996). The ecological play and the geological theater. Palaios, 11(3): 207–208

    Article  Google Scholar 

  • Reiji K, Naru T (2014). Climate of the Loess Plateau. In: Restoration and Development of the Degraded Loess Plateau, China, 22–33

  • Sadahiro Y (2005). Spatiotemporal analysis of the distribution of urban facilities in terms of accessibility. Pap Reg Sci, 84(1): 61

    Article  Google Scholar 

  • Saunders S C, Chen J, Drummer T D, Gustafson E J, Brosofske K D (2005). Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses. Ecol Complex, 2(1): 87–105

    Article  Google Scholar 

  • Shaw D C, Chen J, Freeman E A, Braun D M (2005). Spatial and population characteristics of dwarf mistletoe infected trees in an old-growth douglas-fir/western hemlock forest. Revue Canadienne De Recherche Forestière, 35(4): 990–1001

    Article  Google Scholar 

  • Tang G A, Li F Y, Liu X J, Long Y, Yang X (2008). Research on the slope spectrum of the Loess Plateau. Science in China Series E: Technological Sciences, 51(S1): 175–185

    Article  Google Scholar 

  • Tarolli P, Sofia G (2016). Human topographic signatures and derived geomorphic processes across landscapes. Geomorphology, 255: 140–161

    Article  Google Scholar 

  • Urban D L, O’Neill R V, Shugart H H Jr (1987). A hierarchical perspective can help scientists understand spatial patterns. Bioscience, 1987(37): 119–127

    Article  Google Scholar 

  • Williams E A, Wentz E A (2008). Pattern analysis based on type, orientation, size, and shape. Geogr Anal, 40(2): 97–122

    Article  Google Scholar 

  • With K A, King A W (1999). Dispersal success on fractal landscapes: a consequence of lacunarity thresholds. Landsc Ecol, 14(1): 73–82

    Article  Google Scholar 

  • Wu H, Li Z L (2009). Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors (Basel), 9(3): 1768–1793

    Article  Google Scholar 

  • Wu J, Jones K B, Li H, Loucks O L (2006). Scaling and Uncertainty Analysis in Ecology II Concepts of Scale and Scaling. Amsterdam: Springer

    Book  Google Scholar 

  • Zhao M W, Li F Y, Tang G A (2012). Optimal scale selection for DEM based slope segmentation in the Loess Plateau. Int J Geosci, 1(3): 37–43

    Article  Google Scholar 

  • Zhou Y, Tang G A, Yang X, Xiao C C, Zhang Y, Luo M L (2010). Positive and negative terrains on northern Shaanxi Loess Plateau. J Geogr Sci, 20(1): 64–76

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful for the financial support from the National Natural Science Foundation of China (Grant Nos. 41930102, 41571383, 41771415, 41801321, and 41701450). We sincerely appreciate the editor’s encouragement. The constructive criticisms and suggestions from anonymous reviewers are also gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fayuan Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, Z., Li, F., Zhao, M. et al. Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China. Front. Earth Sci. 15, 94–105 (2021). https://doi.org/10.1007/s11707-020-0830-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-020-0830-4

Keywords

Navigation