Publications by Ivan Lizarazo
Kriging made simple
1. Introduction This R Markdown notebook illustrates kriging interpolation. The code was written by Barry Boessenkool. Thank you Barry! 2. Toy interpolation A single script to understand interpolation: if(!requireNamespace("pacman", quietly=TRUE)) install.packages("pacman") pacman::p_load(geoR, berryFunctions) # installs packages if needed # ber...
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How to project big raster files?
1. Introduction Reprojecting big raster files is a common task needed for GIS analysis. It may be challenging to conduct it by using the projectRaster functionality provided by the raster library: it can take several hours. In this notebook, I explore another option to overcome the challenge: the gdalUtils library which provides wrapper functions...
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Classification accuracy assessment
1. Introduction This is an R Markdown notebook to evaluate how your classification results reflect the real-world. Using a toy dataset, we explain how to compute common metrics that are often used in accuracy assessment. Most code has been taken from a blog written by Said Bleik, Shaheen Gauher, Data Scientists at Microsoft, which you may visit h...
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Joining attributes to spatial features
1. Introduction This is the four notebook which Geomatica Basica students have to write to get started with R & RStudio. It aims at learning how to join non-spatial attributes to geospatial data. We will illustrate the topic by joining a spatial feature representing municipalities in Boyacá (i.e. a shapefile provided by DANE, 2017) and a non-sp...
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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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