Community Ecology

, Volume 4, Issue 2, pp 205–217 | Cite as

Synoptic environmental indicators as image analogs for landscape analysis

  • W. L. Myers
  • G. P. PatilEmail author
  • C. Taillie
  • D. C. Walrath


Spatially synoptic multivariate image data implicitly embody information on landscape pattern, for which analytical techniques of explicit pattern extraction are evolving. In parallel, a multiplicity of environmental indicators’ is being generated in the arena of geographic information systems. Landscape ecological analysis offers substantial opportunity for configuring these indicators synoptically as cells over spatial extents and for stacking them into complementary sets of image-structured multiple environmental indicators whereby the values of the indicators become intensity analogs of brightness for spectral bands. As environmental signal analogs of multiband images, these data become available to image portrayal in both graytone and quasi-color renditions to reveal joint properties of pattern for visual interpretation. Likewise, many of the conventional image analysis operations can be conceived more broadly to allow their application in the indicator context. This includes combinatorial approaches such as calculation of an NDVI equivalent from indicator intensities. Similarly, supervised and unsupervised analyses can have meaningful application in the context of multiple environmental indicators. Furthermore, newer techniques of pattern-based image segmentation can also be applied. Application to habitat modeling for vertebrates from Gap Analysis shows the effectiveness of the approach.


Change detection Clustering algorithms Environmental indicators Geographic information systems Image analysis Landscape ecology Remote sensing Species habitat Species richness 



Digital Elevation Model


Geographic Information System


Normalized Difference Vegetation Index


Palette Homogeneity Among Segmentation Elements


Progressively Segmenting Images


Regional Habitat Importance Index


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Prepared with partial support from NASA Biospheric Sciences Branch, Goddard Space Flight Center, U.S. EPA Star Grant for Atlantic Slope Consortium Cooperative Agreement, and the NSF Digital Government Program, Division of Experimental and Integrative Activities, Directorate for Computer and Information Science and Engineering. The contents have not been subjected to Agency review and therefore do not necessarily reflect the views of the Agencies and no official endorsement should be inferred.


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© Akadémiai Kiadó, Budapest 2003

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • W. L. Myers
    • 1
  • G. P. Patil
    • 2
    Email author
  • C. Taillie
    • 2
  • D. C. Walrath
    • 3
  1. 1.School of Forest Resources and Penn State Institutes of EnvironmentThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Center for Statistical Ecology and Environmental Statistics, Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Penn State Institutes of EnvironmentThe Pennsylvania State UniversityUniversity ParkUSA

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