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---
title: "About"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
**Modeling defoliation as a proxy for tree health: Comparison of feature-selection methods across multiple feature sets derived from hyperspectral data**
# Authors
**Patrick Schratz** (patrick.schratz@gmail.com) [](http://orcid.org/0000-0003-0748-6624)
Jannes Muenchow [](http://orcid.org/0000-0001-7834-4717)
Eugenia Iturritxa [](https://orcid.org/0000-0002-0577-3315)
José Cortés [](http://orcid.org/0000-0003-2567-8689)
Bernd Bischl [](http://orcid.org/0000-0001-6002-6980)
Alexander Brenning [](http://orcid.org/0000-0001-6640-679X)
# Contents
## Paper
This repository contains the research compendium of our work on comparing algorithms across multiple feature sets and filtering methods (including ensemble filter methods).
- Using machine-learning algorithms to model defoliation of _Pinus Radiata_ trees.
- keywords
- hyperspectral imagery
- forest health monitoring
- machine learning
- feature selection
- feature effects
- model comparison
- filter
- imaging spectroscopy
- Compare filtering methods (ensemble filter methods) across various algorithms and datasets
- Predict defoliation to all available plots (24) and the whole Basque Country (at 200 m resolution)
The following directories belong to this project
- `code/01-download.R`
- `code/02-hyperspectral-processing.R`
- `code/04-data-processing.R`
- `code/05-modeling/`
- `code/06-benchmark-matrix/`
- `code/07-reports/`
## Other Content
In addition, this repo contains the workflow for an analysis related to the [LIFE 14 ENV/ES/000179 LIFE HEALTHY FOREST](http://www.lifehealthyforest.com/) project: Predicting defoliation at trees for the Basque Country (for the years 2017 and 2018) using Sentinel-2 data.
Target `defoliation_maps_wfr` builds are targets necessary for the final results [report](https://2019-feature-selection.pjs-web.de/report-defoliation.html).
# How to use
## Read the code, access the data
See the [`code`](https://github.com/pat-s/paper_hyperspectral/tree/master/analysis) directory on GitHub for the source code that generated the figures and statistical results contained in the manuscript.
See the [`data`](https://github.com/pat-s/paper_hyperspectral/tree/master/analysis/data) directory for instructions how to access the raw data discussed in the manuscript.
## Install the R package
This repository is organized as an R package, providing functions and raw data to reproduce and extend the analysis reported in the publication.
Note that this package has been written explicitly for this project and may not be suitable for more general use.
This project is setup with a [drake workflow](https://github.com/ropensci/drake), ensuring reproducibility.
Intermediate targets/objects will be stored in a hidden `.drake` directory.
The R library of this project is managed by [renv](https://rstudio.github.io/renv/index.html).
This makes sure that the exact same package versions are used when recreating the project.
When calling `renv::restore()`, all required packages will be installed with their specific version.
Please note that this project was built with R version 3.6.1 on a CentOS 7 operating system.
Some packages from this project **are not compatible with R versions prior version 3.6.0.**
To clone the project, a working installation of `git` is required.
Open a terminal in the directory of your choice and execute:
```sh
git clone https://github.com/pat-s/2019-feature-selection.git
```
Then start R in this directory and run
```{r README-1, eval = FALSE}
renv::restore()
r_make()
```
# Notes and resources
* The organisation of this compendium is based on the work of [Carl Boettiger](http://www.carlboettiger.info/) and [Ben Marwick](https://github.com/benmarwick).