Authors@R: person("Ruben", "Remelgado", role = c("aut", "cre"), email="ruben.remelgado@uni-wuerzburg.de")
Description: In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
Imports: raster, sp, caret, rgdal, ggplot2, grDevices, rsMove,
Suggests: knitr, rmarkdown, kableExtra, imager

Imports: raster, sp, caret, ggplot2, grDevices, spatialEco, rgeos
Suggests: knitr, rmarkdown, kableExtra, imager, RStoolbox,

Imports: raster, sp, caret, ggplot2, grDevices, spatialEco, rgeos,
Suggests: knitr, rmarkdown, kableExtra, imager, randomForest, rgdal,

Imports: raster, sp, caret, ggplot2, grDevices, geosphere, vegan,

Description: FIELDS is a collection of programs for curve and function
fitting with an emphasis on spatial data. The major methods implemented
include cubic and thin plate splines, universal Kriging and Kriging for
large data sets. The main feature is that any covariance function
implemented in R can be used for spatial prediction.

Description: Fields is a collection of programs for curve and function
fitting with an emphasis on spatial data and spatial statistics. The
major methods implemented include cubic and thin plate splines, universal
Kriging and Kriging for large data sets. One main feature is that any
covariance function implemented in R can be used for spatial prediction.