Sgdm: An R package for performing sparse generalized dissimilarity modelling with tools for gdm

Pedro J. Leitão, Marcel Schwieder, Cornelius Senf

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package for performing sparse generalized dissimilarity modelling (SGDM). The package includes some general tools that add functionality to both generalized dissimilarity modelling and sparse generalized dissimilarity modelling. It also includes an exemplary dataset that allows for the application of SGDM for mapping the spatial patterns of tree communities in a region of natural vegetation in the Brazilian Cerrado.

Original languageEnglish
Article number23
JournalISPRS International Journal of Geo-Information
Volume6
Issue number1
DOIs
StatePublished - Jan 2017
Externally publishedYes

Keywords

  • Cerrado trees
  • Community turnover
  • Generalized dissimilarity modelling
  • High-dimensional data
  • Hyperspectral remote sensing
  • R package
  • Sparse canonical component analysis

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