Regionalization of landscape characteristics to map hydrologic variables

Regionalization of landscape pattern indices using multivariate cluster analysis.

Regionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape pa...

Regionalization of the conterminous U.S. into hierarchical landscape pattern types

We present a pattern-based regionalization of the conterminous U.S. – a partition of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of land surface into a number of landscape pattern types (LPTs). First, the entire NLCD is ...

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each c...

Regionalization of Hydrologic Response in the Great Lakes Basin: Considerations of Temporal Variability

Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications...