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Journal of Applied Remote Sensing • new

Multivariate classification of landscape metrics in multispectral digital images
Author(s): Jorge Lira; Sara Morales
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Paper Abstract

The use of landscape metrics to characterize the morphological behavior of a landscape has been extensive in the last few years. It is recognized that a single metric is insufficient to characterize a landscape. Such metrics are used individually to derive the morphological aspect of a landscape. No joint use of various metrics has been reported. Therefore, we considered the joint use of landscape metrics in a multivariate classification. We derived the value of a number of landscape metrics of patches from several case studies. A multivariate classification was applied using a hierarchical clustering algorithm. The multivariate classification was carried out using the least correlated landscape metrics. To consider the multivariate classification, a normalization of metrics range was used. The results provided the morphological structure of patches grouped into four or five classes. The classes depicted a morphological structure of patches that ranged from simple to very complex. An index was proposed to quantify the morphological structure of a class-patch. Such an index was defined as the average of the landscape metrics for a class-patch. The distance among the class-patch was given by means of the Jeffries–Matusita distance.

Paper Details

Date Published: 28 June 2016
PDF: 19 pages
J. Appl. Remote Sens. 10(2) 026039 doi: 10.1117/1.JRS.10.026039
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Jorge Lira, Univ. Nacional Autónoma de México (Mexico)
Sara Morales, Univ. Nacional Autónoma de México (Mexico)


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