Publications by Ivan Lizarazo
Landsat image exploration with terra
1. Introduction In this notebook I illustrate how to find, download and explore satellite remote sensing data with R. I also show how to create color composites and explore spectral profiles. A lot of the code that follows is based on the notebook written by Aniruddha Ghosh and Robert J. Hijmans which you may find here. However, I expand here bot...
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A tutorial on pixel-based land cover classification using random forests
1. Introduction This notebook explores statistical learning techniques to conduct land cover classification from multispectral imagery. Novel machine learning techniques promise high predictive performance as they seems able to better represent nonlinear relationships or higher-order interactions between predictors than traditional linear models ...
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Accuracy assessment of land cover classification
1. Introduction This notebook illustrates how to assess thematic accuracy of land cover classification. It aims at helping Percepcion Remota students at UNAL to get started with remote sensing image analysis in R. I will use a classified image obtained in a previous notebook. The classification covers the area known as Montes de Maria, in the Co...
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My fifth notebook: elevation data
1. Why this notebook? This is an R Notebook created using R Studio on an old laptop. It illustrates several functionalities to obtain, process and visualize digital elevation models in R. It aims to help Geomatica Basica students at Universidad Nacional to get started with R geospatial capabilities. A few tips for writing your own notebook: Writ...
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CART-based land cover classification
1. Introduction This notebook illustrates how to conduct land cover classification from multispectral imagery using the terra library. It aims at helping Percepcion Remota students at UNAL to get started with remote sensing image analysis in R. I will use a spatial and spectral subset of a Landsat 8 scene collected in 2013. The subset is a seven-...
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Evaluaciones Agropecuarias Municipales
1. Introduction This is the third notebook which Geomatica Basica students have to write to get started with R & RStudio. It aims at learning how to use the dplyr package for data “editing”. Editing refers to choosing a subset of the variables and/or observations in a dataset, as well as filtering (selecting observations based on their variab...
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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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