Comparing dissimilarity values to visual inspection of couples of most similar (NN) spectra

Reflectance spectra of the Standard Reference target under different lab illuminators

Radiance and Reflectance spectra under different lab illuminators

This script extracts and compares reflectance spectra of a standard reflectance target (Labsphere WCS-MC-020) from VISNIR hyperspectral images acquired with three VISNIR hyperespectral imaging systems in HSILab premises: * Cubert Firefleye S185 SE * Specim IQ * Specim FX10

Reflectance spectra of 2 grey standards from a VISNIR hyperspectral image acquired with a Specim IQ system in HSILab premises on 20221109: * Specim IQ white reference card. * Labsphere 50% reflectance standard * Cubert grey reflectance standard

Art pigments (control and heated to 800ºC) as imaged by hyperspectral camera Specim IQ

Reflectance spectra (400 - 100 nm) of hydrated and dry samples of Lobaria amplissima extracted from a hyperspectral image (Specim IQ)

Comparison of EMs to spectral libraries (UGS+Ecostress) (2000-2460 nm) Color: spectral libraries Black: TFM Grey: Bedini (height is approx.)

Comparison of EMs to spectral libraries (UGS+Ecostress) Color: spectral libraries Black: TFM Grey: Bedini (height is approx.)

Specim IQ test 20180918_DIPV23

This script compares 4 options to calculate reflectance from radiance images acquired by hyperspectral scanning systems, in particular, Specim FX17 and FX10 mounted on desktop-size Lumo Scanner stands

LDA severuty all classes by segments P8

LDA severity by plots with M in P8

Some problems with lmp described

Simple Markdown syntax for formatting your R scripts into WEB pages using knitr::spin()

Most Basic use of knitr::spin()

Comparisson of Copernicus Time Series of NDVI V1 and V2 in Valderejo Test Site (Val2013_V1V2comp_log.R)

Exercise R Lab 2.1: Pollutants in soils of the Kola Peninsula

Length of Day An example of computing with dates in R

talleR ICTJA RLab1

to be removed

GM tiles can be downloaded as R objects using either ggmap:get_map() or dismo:gmap(). We prefer dismo:gmap() because, unlike ggmap:get_map(), it returns a raster layer from package raster including complete CRS information

Variables that describe local displacement, such as wind fields, can be very effectively represented with function rasterVis:vectorplot() by Oscar Perpinan. We will use a climatological wind field dowloaded from the NOAA Blended Sea Winds

Objects returned by dismo:gmap() are raster layers of package *raster* with the correct CRS information ("Google PseudoMercator" epsg:3857) that can be used as background for displaying other geographic layers in R provided their CRS are consistent. We show here the different alternatives.

rasterVis is an R package for the visualization of raster layers authored by Oscar Perpinan (http://oscarperpinan.github.io/) This introductory material that does not substitute the standard documentation provided with the package and the documents provided by Oscar Perpinan:

The CRS of objects returned by get_map() is not documented. Here we use an empirical approach to confirm that, as it is usual in tools interacting with GM, the map is in so called "Google PseudoMercator" while coordinates are provided in "Longitude, Latitude" (datum WGS84 in both cases) and show how to produce correct plots of overlaid maps