sgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdm
| Autor: | Marcel Schwieder, Pedro J. Leitão, Cornelius Senf |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Clustering high-dimensional data generalized dissimilarity modelling 010504 meteorology & atmospheric sciences Computer science hyperspectral remote sensing Geography Planning and Development lcsh:G1-922 High dimensional Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences Earth and Planetary Sciences (miscellaneous) ddc:550 Monitoring methods Computers in Earth Sciences 0105 earth and related environmental sciences Cerrado trees business.industry R package Hyperspectral imaging sparse canonical component analysis 550 Geowissenschaften Tree (data structure) high-dimensional data Community composition Spatial ecology Artificial intelligence Data mining business computer community turnover lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 6, Iss 1, p 23 (2017) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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