Novel shape indices for vector landscape pattern analysis

Novel shape indices for vector landscape pattern analysis

The formation of an anisotropic landscape is influenced by natural and/or human 2 processes, which can then be inferred on the basis of geometric indices. In this 3 study, two minimal bounding rectangles in consideration of the principles of 4 mechanics (i.e. minimal width bounding (MWB) box and moment bounding 5 (MB) box) were introduced. Based on these boxes, four novel shape indices, 6 namel...

Vector analysis theory on landscape pattern (VATLP)

Landscape indices are popular for the quantification of landscape pattern. However, all landscape indices being used so far are scalar quantities, and measure patterns without considering sufficiently the pattern shape and the directionality together. Based on planar characteristics defined in mechanics such as centroid, moment of inertia, product of inertia and principal axes, vector analysis ...

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...

Preliminary Analysis on the Regionalization of Landscape Pattern Indices using Multivariate Cluster Analysis – MBPI Working Paper

Gap Shape Classification using Landscape Indices and Multivariate Statistics

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters ...