Remote Sensing Applications and Decision Support

Multivariate classification of landscape metrics in multispectral digital images

[+] Author Affiliations
Jorge Lira, Sara Morales

Universidad Nacional Autónoma de México, Instituto de Geofísica, 04510, D.F., Av. Universidad 3000, Delegación Coyoacán, Mexico

J. Appl. Remote Sens. 10(2), 026039 (Jun 28, 2016). doi:10.1117/1.JRS.10.026039
History: Received December 7, 2015; Accepted June 3, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Jorge Lira and Sara Morales
"Multivariate classification of landscape metrics in multispectral digital images", J. Appl. Remote Sens. 10(2), 026039 (Jun 28, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026039


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