https://github.com/csaybar/forestoolboxrs

The PVts-β approach to forest disturbance monitoring is now available through the ForesToolboxRS package.
https://github.com/csaybar/forestoolboxrs

Last synced: about 1 month ago
JSON representation

The PVts-β approach to forest disturbance monitoring is now available through the ForesToolboxRS package.

• Host: GitHub
• URL: https://github.com/csaybar/forestoolboxrs
• Owner: csaybar
• License: other
• Created: 2019-09-08T00:54:00.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-31T22:10:44.000Z (about 5 years ago)
• Last Synced: 2024-10-12T19:10:58.070Z (3 months ago)
• Language: R
• Size: 29.4 MB
• Stars: 2
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  eval = FALSE,

  comment = "#>",

  out.width = "100%"

)

```

[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/csaybar/forestoolboxrs?branch=dev&svg=true)](https://ci.appveyor.com/project/csaybar/forestoolboxrs)

[![Travis build status](https://travis-ci.org/csaybar/ForesToolboxRS.svg?branch=master)](https://travis-ci.org/csaybar/ForesToolboxRS)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Codecov test coverage](https://codecov.io/gh/csaybar/ForesToolboxRS/branch/master/graph/badge.svg)](https://codecov.io/gh/csaybar/ForesToolboxRS?branch=dev)

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

## ForesToolboxRS

The PVts-β approach is implemented in R with the ForesToolboxRS package. This algorithm will allow to detect disturbances in the 

forests using all the available Landsat set. In fact, it can also be run with sensors such as MODIS (Moderate Resolution 

Imaging Spectroradiometer). In essence, the PVts-β approach is an algorithm that does not model the seasonal component 

in time series, instead it only requires calculating the mean and standard deviation of the time series itself to detect 

disturbances. Therefore, the PVts-β approach is a method: i) simple and intelligent that does not model seasonality, ii) 

has only one calibration parameter and iii) can be easily implemented by any standard user.

For any work you will submit, credits from this algorithm must be given to: 

[Tarazona, Y., Mantas, V.M., Pereira, A.J.S.C. (2018). Improving tropical deforestation detection through using photosynthetic 

vegetation time series – (PVts-β). Ecological Indicators, 94, 367–379.](https://doi.org/10.1016/j.ecolind.2018.07.012)

# Getting Started

## Install

```

library(devtools)

install_github("ytarazona/ForesToolboxRS")

```

## Some functions

```

library(ForesToolboxRS)

?MosaicFreeCloud

?smootH

?pvts

?fusionRS

```