As it reads.

An R notebook illustrating map making code for the MP164 course.

This notebook illustrates several geometric functions provided by the *simple features for R* package.

How to read and subset xlsx data.

This notebook illustrates spatial interpolation using IDW and Ordinary Kriging techniques.

A notebook illustrating how to use the gdalUtils library and the WebDAV protocol to get soil raster data from a given geographic area.

This notebook illustrates several functionalities for obtaining morphometric variables from digital elevation models.

This notebook illustrates how to conduct spatial interpolation of point data using the stars, geostat and automap libraries.

A simple notebook for GB2022 students.

This notebook illustrates analysis of multi-year agricultural stats.

This notebook illustrates how to make thematic maps showing production for two important crops in a department.

It describes how to make thematic maps showing major crops in a given department.

It descibes how to select crops with largest production from the 2020 EVA dataset.

A notebook to guide image analysis tasks in the HERMES project.

Testing the gstat & stars alliance.

This update aims to fix a problem originated from the shift from PROJ4 to PROJ6 in the recent versions of the rgdal. In this notebook I use the WKT format instead of the PROJ4 to get the CRS.

Another proof-of-concept.

A proof-of-concept.

This notebook illustrates how to obtain and visualize global climate data in R.

This notebook illustrates land cover classification from multispectral imagery using the package ranger which is a fast implementation of random forests.

This notebook illustrates how to assess thematic accuracy of pixel-based land cover classification using different metrics.

This R Notebook illustrates several functionalities to obtain, process and visualize digital elevation models in R.

This notebook illustrates how to conduct land cover classification from multispectral imagery using the terra library.

This is the fourth notebook that GB students need to write to get started with R geospatial capabilities.

This is the third notebook that Geomática Básica students have to write to get started with R & RStudio.

This notebook illustrates how to calculate uni-band and multi-band statistics of remote sensing images using the raster library.

This notebook illustrates how to improve image visualization of remote sensing images (when traditional contrast stretch techniques are not good enough).

This notebook illustrates how to read, visualize and explore Landsat satellite images using the terra package.

This notebook illustrates kriging interpolation using code written by Barry Boessenkool.

This notebook illustrates how to read, subset and process Sentinel-2 data in R.

This is an R Markdown Notebook which illustrates how to access SoilGrids using the webDAV protocol.

Author: Francesco Pirotti <francesco.pirotti@unipd.it> #Summer Webinar Series — Sept. 29, 2020

This notebook illustrates how to use the elevatr package for getting raster elevation data.

This notebook illustrates how to simplify simple features geometries in order to produce lightweight files, and thus, be able to publish leaflet maps in RPubs.

This is a workaround to "publish" maps when uploading the html to RPubs is not possible.

This notebook creates a leaflet map from a table of points

A quick exploration of FRK.

This notebook illustrates spatial interpolation using precipitation data as example.

A notebook to explore land cover change using the ESA CCI Land Cover Product

A notebook to explore land cover change using the MODIS Land Cover Product

Illustration of functionalities provided by the R library smapr.

This notebook illustrates functionalities provided by the cartography package

An R notebook to illustrate geomatic concepts useful in agronomy.

This is an illustrative R notebook aimed to help GB students at UNAL with their home activities during this social distancing's time.

This is an R Notebook based on the tutorial Remote Sensing Image Analysis with R written by Aniruddha Ghosh and Robert J. Hijmans. It covers the exploration section.

This notebook explores principal component analysis of textural metrics

This notebook explores texture metrics.

This notebook uses a 1993-2015 time series with the undernourished population percentage by country and population data for producing cartograms showing number of people severely affected by food insecurity.

Illustration of a meaningful set of convex hull, alpha hull and buffer operations.

A proof-of_concept on women and gender inequality

A guide for mapping development indicators using World Bank data.

There are several options to read, transform, and display point and track data stored in GPS Exchange (GPX) format using R. This notebook shows one of them.

Here you find detailed instructions to write your own notebook.

A synthesis based on rspatial.org

As said.

Esta nota explica cómo acceder y consultar la base de datos global armonizada de suelos (HWSD) [3] usando R, el programa estadístico de software libre [7].

This document evaluates accuracy of soil moisture estimated from remotely sensed data. A SMAP Level 3 dataset is used as reference.

This document describes how to search and download NASA SMAP data.

Proof of concept to estimate surface soil moisture from topographic and multispectral índices.

A brief overview of the nasadata package functionalities

This is a simple test of a few geospatial functionalities