Towards machine ecoregionalization of Earth’s landmass using pattern segmentation method

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.jag.2018.03.004.

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Authors

Jakub Nowosad , Tomasz Stepinski

Abstract

We present and evaluate a quantitative method for delineation of ecophysigraphic regions throughout the entire terrestrial landmass. The method uses the new pattern-based segmentation technique which attempts to emulate the qualitative, weight-of-evidence approach to a delineation of ecoregions in a computer code. An ecophysiographic region is characterized by homogeneous physiography defined by the cohesiveness of patterns of four variables: land cover, soils, landforms, and climatic patterns. It is expected that such a region is likely to be characterized by a single ecosystem. In this paper, we focus on the first-order approximation of the proposed method - delineation on the basis of the patterns of the land cover alone. We justify this approximation by the existence of significant spatial associations between various physiographic variables. Resulting ecophysiographic regionalization (ECOR) is shown to be more physiographically homogeneous than existing global ecoregionalizations (Terrestrial Ecoregions of the World (TEW) and Bailey’s Ecoregions of the Continents (BEC)). The presented quantitative method has an advantage of being transparent and objective. It can be verified, easily updated, modified and customized for specific applications. Each region in ECOR contains detailed, SQL-searchable information about physiographic patterns within it. It also has a computer-generated label. To give a sense of how ECOR compares to TEW and, in the U.S., to EPA Level III ecoregions, we contrast these different delineations using two specific sites as examples. We conclude that ECOR yields regionalization somewhat similar to EPA level III ecoregions, but for the entire world, and by automatic means.

DOI

https://doi.org/10.31223/osf.io/fsver

Subjects

Categorical Data Analysis, Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Statistics and Probability

Keywords

Segmentation, regionalization, Environmental variables, Global ecoregions, Pattern

Dates

Published: 2018-02-15 19:31

Last Updated: 2018-04-18 14:12

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License

CC BY Attribution 4.0 International

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