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The transiogram as a graphic metric for characterizing the spatial patterns of landscapes

  • Ruiting ZhaiEmail author
  • Weidong Li
  • Chuanrong Zhang
  • Weixing Zhang
  • Wenjie Wang
Research Article
  • 119 Downloads

Abstract

Context

Landscape metrics play an important role in measurement, analysis, and interpretation of spatial patterns of landscapes. There are a variety of different landscape metrics widely used in landscape ecology. However, existing landscape metrics are mostly non-graphic and single-value indices, which may not be sufficient to describe the complex spatial correlation and interclass relationships of various landscapes. As a transition probability diagram over the lag distance, the transiogram, which emerged in recent years, essentially provides a new graphic metric for measuring and visualizing the auto and cross correlations of landscape categories.

Objectives

To explore the capability of the transiogram for measuring spatial patterns of categorical landscape maps and compare it with existing landscape metrics.

Methods

Sixteen commonly-used landscape metrics and transiograms (including auto- and cross-transiograms) were estimated and compared for land cover/use classes in four areas with different landscapes.

Results

Results show that (1) these transiograms can provide visual information about the proportions, aggregation levels, interclass adjacencies, and intra-class/interclass correlation ranges of landscape classes; (2) sills and auto-correlation ranges of transiograms are correlated with the values of some landscape metrics; and (3) the peak height ratios of idealized transiograms can effectively represent the juxtaposition strength of neighboring class pairs.

Conclusions

The transiogram can be an effective graphic metric for characterizing the auto-correlation of single classes (through auto-transiograms) and the complex interclass relationships, such as interdependency and juxtaposition, between different landscape classes (through cross-transiograms).

Keywords

Transiogram Landscape metrics Transition probability Spatial pattern Graphic metric Visual information 

Notes

Acknowledgments

This research is partially supported by USA NSF grant No. 1414108. Authors have the sole responsibility to all of the viewpoints presented in the paper.

Supplementary material

10980_2018_760_MOESM1_ESM.docx (3.6 mb)
Supplementary material 1 (DOCX 3690 kb)

References

  1. Bailey D, Billeter R, Aviron S, Schweiger O, Herzog F (2007) The influence of thematic resolution on metric selection for biodiversity monitoring in agricultural landscapes. Landsc Ecol 22(3):461–473CrossRefGoogle Scholar
  2. Carle SF, Fogg GE (1996) Transition probability-based indicator geostatistics. Math Geol 28(4):453–476CrossRefGoogle Scholar
  3. Carle SF, Fogg GE (1997) Modeling spatial variability with one and multidimensional continuous-lag Markov chains. Math Geol 29(7):891–918CrossRefGoogle Scholar
  4. Eastman J (2012) IDRISI selva. Clark University, Worcester, MAGoogle Scholar
  5. Fan C, Myint S (2014) A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landsc Urban Plan 121:117–128CrossRefGoogle Scholar
  6. Frank S, Fürst C, Koschke L, Witt A, Makeschin F (2013) Assessment of landscape aesthetics—validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecol Ind 32:222–231CrossRefGoogle Scholar
  7. Hargis CD, Bissonette J, Turner DL (1999) The influence of forest fragmentation and landscape pattern on American martens. J Appl Ecol 36(1):157–172CrossRefGoogle Scholar
  8. Kong F, Yin H, Nakagoshi N, James P (2012) Simulating urban growth processes incorporating a potential model with spatial metrics. Ecol Ind 20:82–91CrossRefGoogle Scholar
  9. Kupfer JA (2012) Landscape ecology and biogeography: rethinking landscape metrics in a post-FRAGSTATS landscape. Prog Phys Geogr 36(3):400–420CrossRefGoogle Scholar
  10. Lee S-W, Lee M-B, Lee Y-G, Won M-S, Kim J-J, Hong S-K (2009) Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea. For Ecol Manag 258(7):1594–1604CrossRefGoogle Scholar
  11. Li H, Wu J (2004) Use and misuse of landscape indices. Landsc Ecol 19(4):389–399CrossRefGoogle Scholar
  12. Li W (2007a) Transiograms for characterizing spatial variability of soil classes. Soil Sci Soc Am J 71(3):881–893CrossRefGoogle Scholar
  13. Li W (2007b) Markov chain random fields for estimation of categorical variables. Math Geol 39(3):321–335CrossRefGoogle Scholar
  14. Li W, Zhang C, Dey DK (2012) Modeling experimental cross-transiograms of neighboring landscape categories with the gamma distribution. Int J Geogr Inform Sci 26(4):599–620CrossRefGoogle Scholar
  15. Li W, Zhang C, Willig MR, Dey DK, Wang G, You L (2015b) Bayesian Markov chain random field cosimulation for improving land cover classification accuracy. Math Geosci 47(2):123–148CrossRefGoogle Scholar
  16. Li X, He HS, Bu R, Wen Q, Chang Y, Hu Y, Li Y (2005) The adequacy of different landscape metrics for various landscape patterns. Pattern Recogn 38(12):2626–2638CrossRefGoogle Scholar
  17. Li X, Mander Ü (2009) Future options in landscape ecology: development and research. Prog Phys Geogr 33(1):31–48CrossRefGoogle Scholar
  18. Li Y, Li Y, Qureshi S, Kappas M, Hubacek K (2015a) On the relationship between landscape ecological patterns and water quality across gradient zones of rapid urbanization in coastal China. Ecol Model 318:100–108CrossRefGoogle Scholar
  19. Liu M, Hu Y-M, Li C-L (2017) Landscape metrics for three-dimensional urban building pattern recognition. Appl Geogr 87:66–72CrossRefGoogle Scholar
  20. Luo J (1996) Transition probability approach to statistical analysis of spatial qualitative variables in geology. In: Förster A, Merriam DF (eds) Geologic modeling and mapping. Plenum Press, New York, pp 281–299CrossRefGoogle Scholar
  21. McGarigal K (2002) Landscape pattern metrics. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmetrics. Wiley, Chichester, pp 1135–1142Google Scholar
  22. McGarigal K, Cushman S, Neel M, Ene E (2002a) FRAGSTATS: spatial pattern analysis program for categorical maps. University of Massachusetts, AmherstGoogle Scholar
  23. McGarigal K, Ene E, Holmes C (2002) FRAGSTATS (version 3): FRAGSTATS Metrics. University of Massachusetts-Produced Program. http://www.umass.edu/landeco/research/fragstats/documents/fragstatsdocuments.html
  24. Midha N, Mathur P (2010) Assessment of forest fragmentation in the conservation priority Dudhwa landscape, India using FRAGSTATS computed class level metrics. J Indian Soc Remote Sens 38(3):487–500CrossRefGoogle Scholar
  25. Rempel R, Carr A, Elkie P (2008) Patch analyst for ArcGIS®. Centre for Northern Forest Ecosystem Research, Ontario Ministry of Natural Resources. Lakehead University, Thunder BayGoogle Scholar
  26. Riitters KH, Oneill R, Hunsaker C, Wickham JD, Yankee D, Timmins S, Jones K, Jackson B (1995) A factor analysis of landscape pattern and structure metrics. Landscape Ecol 10(1):23–39CrossRefGoogle Scholar
  27. Ritzi RW (2000) Behavior of indicator variograms and transition probabilities in relation to the variance in lengths of hydrofacies. Water Resour Res 36(11):3375–3381CrossRefGoogle Scholar
  28. Schwarzacher W (1969) The use of Markov chains in the study of sedimentary cycles. J Int Assoc Math Geol 1(1):17–39CrossRefGoogle Scholar
  29. Shao G, Wu J (2008) On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecol 23(5):505–511CrossRefGoogle Scholar
  30. Shen Z, Hou X, Li W, Aini G, Chen L, Gong Y (2015) Impact of landscape pattern at multiple spatial scales on water quality: a case study in a typical urbanised watershed in China. Ecol Ind 48:417–427CrossRefGoogle Scholar
  31. Sofia G, Marinello F, Tarolli P (2014) A new landscape metric for the identification of terraced sites: the slope local length of auto-correlation (SLLAC). ISPRS J Photogramm Remote Sens 96:123–133CrossRefGoogle Scholar
  32. Soille P, Vogt P (2009) Morphological segmentation of binary patterns. Pattern Recogn Lett 30(4):456–459CrossRefGoogle Scholar
  33. Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecol 3(3–4):153–162CrossRefGoogle Scholar
  34. Vogt P, Ferrari JR, Lookingbill TR, Gardner RH, Riitters KH, Ostapowicz K (2009) Mapping functional connectivity. Ecol Ind 9(1):64–71CrossRefGoogle Scholar
  35. Vogt P, Riitters K (2017) GuidosToolbox: universal digital image object analysis. Eur J Remote Sens 50(1):352–361CrossRefGoogle Scholar
  36. Vogt P, Riitters KH, Estreguil C, Kozak J, Wade TG, Wickham JD (2007) Mapping spatial patterns with morphological image processing. Landscape Ecol 22(2):171–177CrossRefGoogle Scholar
  37. Wang X, Cumming SG (2011) Measuring landscape configuration with normalized metrics. Landscape Ecol 26(5):723–736CrossRefGoogle Scholar
  38. Wickham J, Rhtters K (1995) Sensitivity of landscape metrics to pixel size. Int J Remote Sens 16(18):3585–3594CrossRefGoogle Scholar
  39. Yu J, Li W, Zhang C (2019) A framework of experimental transiogram modelling for Markov chain geostatistical simulation of landscape categories. Comput Environ Urban Syst 73:16–26CrossRefGoogle Scholar
  40. Zhang W, Li W, Zhang C, Hanink D, Liu Y, Zhai R (2018) Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model. Landscape Urban Plan 177:114–127CrossRefGoogle Scholar
  41. Zhang W, Li W, Zhang C, Ouimet WB (2017) Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors. Int J Remote Sens 38(12):3704–3734CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Geography & Center for Environmental Science and EngineeringUniversity of ConnecticutStorrsUSA
  2. 2.Connecticut Transportation Safety Research CenterUniversity of ConnecticutStorrsUSA

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