Agustin Lobo

Recently Published

A comparison of HSI systems
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
Specim IQ test of grey standards
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
Specim IQ test with art pigments
Art pigments (control and heated to 800ÂșC) as imaged by hyperspectral camera Specim IQ
Specim IQ test with Lobaria amplissima (20180918)
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
Options to calculate Reflectance
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
Problems witth lmPerm::lmp() for regression
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
R Lab1
talleR ICTJA RLab1
to be removed
Overlay your raster layer on a background GM layer in R
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
Plotting vector maps overlaid on top of a raster background in R
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.
Introduction to rasterVis
rasterVis is an R package for the visualization of raster layers authored by Oscar Perpinan ( This introductory material that does not substitute the standard documentation provided with the package and the documents provided by Oscar Perpinan:
Finding out the CRS of the object returned by get_map()
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