Publications by Ivan Lizarazo
Satellite image contrast enhancement with R
1. Introduction In this notebook I explore functionalities provided by the imager package developed by Simon Barthelmé as well as the imagerExtra package developed by Shota Ochi. I applied here these packages’ functions to improve remote sensing image visualization. In a earlier notebook, we saw that the terra package provide a few functions t...
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Image statistics of a Landsat 8 image
1. Introduction This notebook illustrates how to calculate uniband and multiband image statistics using the raster library. I will use a spatial and spectral subset of a Landsat 8 scene collected in 2013. The subset is a seven-band image which covers the area known as Montes de Maria, in the Colombian Caribbean region. I explained in a previous n...
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Getting climate data from R
1. Why this notebook? This is an R Notebook written to illustrate how to obtain, process and visualize climate data in R. It aims to help Geomatica Basica students at Universidad Nacional to get started with R geospatial capabilities. Note that, here, I focus on explaining the technical procedure rather than on interpreting or analyzing the outpu...
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Healthcare accessibility and population
The Accessibility to Healthcare 2019 dataset enumerates land-based travel time (in minutes) to the nearest hospital or clinic for all areas between 85 degrees north and 60 degrees south for a nominal year 2019. It also includes “walking-only” travel time, using non-motorized means of transportation only. Major data collection efforts underway...
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Water ocurrence change intensity
The JRC Water Ocurrence dataset contains maps of the location and temporal distribution of surface water from 1984 to 2020 and provides statistics on the extent and change of those water surfaces. For more information see the associated journal article: Jean-Francois Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping of...
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Spatial Interpolation revisited
1. Why this notebook? This is an update for a previous R Notebook written to illustrate how to conduct spatial interpolation in R which may be found here. This update aims to help Geomatica Basica students at Universidad Nacional to fix a problem originated from the shift from PROJ4 to PROJ6 in the recent versions of the spatial packages of R (an...
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Looking again at spatial interpolation
1. Introduction This notebook illustrates spatial interpolation using data obtained in a field campaign over a potato crop farm. A lot of code is borrowed from Introduction to Spatial Data Programming with R by Michael Dorman (2021). Its main purpose is to guide data processing tasks for the HERMES project: Agro-Geoinformática: Detección tempra...
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Exploring random forest based regression of soil moisture
1. Introduction This notebook illustrates regression of soil moisture from multispectral & thermal imagery obtained in a field campaign over a potato crop farm. Its main purpose is to guide data processing tasks for the HERMES project: Agro-Geoinformática: Detección temprana in situ de enfermedades a nivel foliar empleando imágenes espectrales...
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Crop production dynamics
1.Introduction This is the third R Markdown Notebook for the Geomatica Basica 2022 course. It illustrates how to obtain multi-year stats for a given group of crops in any department. We will use as main data source the Evaluaciones Agropecuarias Municipales (EVA), a 2007-2018 agricultural data set provided by the Ministerio de Agricultura y Desar...
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How to make thematic maps - V2
1.Introduction This is the second R Markdown Notebook for the Geomatica Basica 2022 course. It illustrates how to make thematic maps showing the municipal share of the two most important groups of crops for a given department. We will use as main source the csv files saved in the EVA notebook, as well as a shapefile of municipalities obtained in ...
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