The transiogram as a graphic metric for characterizing the spatial patterns of landscapes
- 119 Downloads
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.
To explore the capability of the transiogram for measuring spatial patterns of categorical landscape maps and compare it with existing landscape metrics.
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 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.
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).
KeywordsTransiogram Landscape metrics Transition probability Spatial pattern Graphic metric Visual information
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.
- Eastman J (2012) IDRISI selva. Clark University, Worcester, MAGoogle Scholar
- McGarigal K (2002) Landscape pattern metrics. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmetrics. Wiley, Chichester, pp 1135–1142Google Scholar
- McGarigal K, Cushman S, Neel M, Ene E (2002a) FRAGSTATS: spatial pattern analysis program for categorical maps. University of Massachusetts, AmherstGoogle Scholar
- 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
- 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