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Development","sub_category":"Environmental Satellites","monthly_downloads":340,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# rsat\n\n\u003c!-- badges: start --\u003e\n\n[![CRAN version](https://www.r-pkg.org/badges/version/rsat)](https://CRAN.R-project.org/package=rsat)  \n[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/437_status.svg)](https://github.com/ropensci/software-review/issues/437)\n[![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)\n[![Codecov test coverage](https://codecov.io/gh/ropensci/rsat/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/rsat?branch=master)\n[![R build status](https://github.com/ropensci/rsat/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci/rsat/actions)\n[![Total downloads](https://cranlogs.r-pkg.org/badges/grand-total/rsat)](https://CRAN.R-project.org/package=rsat)\n\u003c!-- badges: end --\u003e\n\nThe goal of `rsat` is to help you handling time-series of satellite images from multiple platforms in a local, efficient, and standardized way. The package provides tools to;\n\n1.  Search (run `vignette(\"rsat1_search\", package = \"rsat\")`)\n2.  Download (run `vignette(\"rsat2_download\", package = \"rsat\")`)\n3.  Customize, and (run `vignette(\"rsat3_customize\", package = \"rsat\")`)\n4.  Process (run `vignette(\"rsat4_process\", package = \"rsat\")`)\n\nsatellite images from Landsat, MODIS, and Sentinel for a region and time of interest.\n\n## Installation\n\nYou can install the development version from [GitHub](https://github.com/) with:\n\n``` r\ninstall.packages(c('usethis', 'pkgdown', 'rcmdcheck', 'rversions', 'urlchecker'))\n\n# check and install devtools\nif(!require(\"devtools\")){\n   install.packages(\"devtools\")\n}\n# check and install rmarkdown\nif(!require(\"rmarkdown\")){\n  install.packages(\"rmarkdown\")\n}\n\ndevtools::install_github(\"spatialstatisticsupna/rsat\", build_vignettes=TRUE)\n```\n\n### Linux\n\nIn Linux, you need to install additional libraries before starting with `rsat`. Use the following commands for:\n\n-   **Debian/Ubuntu**\n\n\u003c!-- --\u003e\n\n    sudo apt install r-cran-rcpp gdal-bin libgdal-dev libproj-dev openssl libssl-dev xml2 libxml2-dev libmagick++-dev libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev libharfbuzz-dev libfribidi-dev\n\n-   **RedHat/Fedora**\n\n\u003c!-- --\u003e\n\n    sudo dnf install gdal gdal-devel proj-devel xml2 libxml2-devel libcurl-devel openssl-devel ImageMagick-c++-devel R-devel udunits2-devel sqlite-devel geos-devel pandoc harfbuzz-devel fribidi-devel freetype-devel libpng-devel libtiff-devel libjpeg-devel\n\n## Log-in profiles\n\nThe registration in the following online portals is required to get a full access to satellite images with `rsat`;\n-   [USGS](https://ers.cr.usgs.gov/register/) USGS is the sole science agency for the Department of the Interior of United States. Provide access to Modis Images. More information about USGS can be found [Here](https://www.usgs.gov/).\n-   [EarthData](https://urs.earthdata.nasa.gov): A repository of NASA's earth observation data-sets. More information about EarthData can be found [here](https://www.earthdata.nasa.gov/learn).\n-   [Dataspace](https://dataspace.copernicus.eu/), a web service giving access to Copernicus' scientific data hub. Please go [here](https://dataspace.copernicus.eu/) to find more details about the data hub.\n\nFor convenience, try to use the same username and password for all of them. To satisfy the criteria of all web services make sure that the username is $4$ characters long and includes a period, number or underscore. The password must be $12$ character long and should include characters with at least one capital letter, and numbers.\n\n## Example\n\nThis is a basic example which shows you how to compute the Normalized Difference Vegetation Index from a MODIS image captured on January 11th, 2020 in northern Spain (Navarre):\n\n``` r\nlibrary(rsat)\n\n# replace with your own \"username\" and \"password\"\nset_credentials(\"username\", \"password\")\n\n# region and time of interest: rtoi\nroi \u003c- ex.navarre\ntoi \u003c- as.Date(\"2020-01-11\")\nrtp \u003c- tempdir()\n\nset_database(file.path(tempdir(), \"DATABASE\"))\n\nnavarre \u003c- new_rtoi(\"Navarre\", roi, rtp)\n\n# search, acquire, customize, and process\nrsat_search(region = navarre, product = \"mod09ga\", dates = toi)\nrsat_download(navarre)\nrsat_mosaic(navarre, overwrite = TRUE)\n\nrsat_derive(navarre, \n            product = \"mod09ga\", \n            variable = \"NDVI\")\n\n# plot the results\nplot(navarre, \"view\" , \n      product = \"mod09ga\", \n      variable = \"NDVI\", \n      breaks = seq(0, 1, 0.1))\n      \nplot(navarre,\"dates\")\n```\nSee the vignettes for more examples:\n```\nbrowseVignettes(\"rsat\")\n```\n## Related similar packages\n\nR has become an outstanding tool for remote sensing image analysis.\nThere are several tools for the search and acquisition of satellite\nimages, however, rsat is the first package that standardizes all the\nprocedures in data acquisition to provide an unique workflow for any\nmultispectral satellite.\n\nCurrently there are several packages dedicated to remote sensing topic,\nbut they are usually ad-hoc packages for each satellite. Here is a list\nof some of the most popular R packages dedicated to satellite imagery:\n\n### Multi satellite packages\n\n-   [RGISTools](https://github.com/spatialstatisticsupna/RGISTools)\n\n-   [getSpatialData](https://github.com/16EAGLE/getSpatialData)\n\n-   [luna](https://github.com/rspatial/luna)\n\nThe closest package to `rsat` is RGISTools. `rsat` is the redefinition\nof the RGISTools package reprogrammed from scrach in the object-oriented\nprogramming paradigm. Many of the RGISTools code lines have been used to\ndevelop `rsat`, but these have been optimized and redundancies in the\ncode have been removed in order to facilitate its maintenance. In\naddition, `rsat` contains new features and R classes to make it more\nuser-friendly.\n\n`getSpatialData` is another package very similar to `rsat`. The package\nhas the same philosophy of having a single package for searching and\ndownloading satellite images. However, the development of `rsat` goes a\nbit further and in addition to search and download, the package helps\nyou to organize all the downloaded information in a structured database.\n`rsat` allows you to use the metadata of the images to see the direct\nrelation with your region of interest before downloading it. Also all\nimage processing standardization is not developed in `getSpatialData`.\n\nThe last package dedicated to image downloading is `luna`.Searching and\ndownloading images compared to rsat is a bit more complicated. It is\nonly able to search and download Modis and Landsat images, and does not\nhelp you in organizing the image products.\n\n### Single satellite packages\n\n-   [rLandsat](https://github.com/atlanhq/rLandsat)\n\n-   [getLandsat](https://github.com/ropensci-archive/getlandsat)\n\n-   [sen2r](https://github.com/ranghetti/sen2r)\n\n`rLandsat` makes it easy to search for Landsat8 product IDs, place an\norder on USGS-ESPA and download the data. `rsat` on the other hand is\nable to do the image search without knowing all the ids, just using a\npolygon of the region of interest, making the search process much\neasier.\n\n`getlandsat` provides access to Landsat 8 metadata and images hosted on\nAWS S3 at. The package only data for the users, and does not help in\nfurther use, as rsat does.\n\n`sen2r` is an R library which helps to download and preprocess\nSentinel-2 optical images. This is done through a GUI, something that\ncan be very interesting for users but limits the analysis of the\ninformation prior to downloading, which can be done with `rsat`.\n\n### Raster processing packages\n\n-   [landsat](https://cran.r-project.org/web/packages/landsat/index.html)\n\n-   [satellite](https://github.com/environmentalinformatics-marburg/satellite)\n\n-   [OpenImageR](https://github.com/mlampros/OpenImageR)\n\n-   [RSToolbox](https://github.com/bleutner/RStoolbox)\n\n-   [sits](https://github.com/e-sensing/sits)\n\n`rsat` helps you to search, download and pre-process the images, but\nonce these procedures are done it allows you to extract all the\nprocessed information into the most used raster classes in R (`raster`,\n`stars` or `spatRaster`). The image processing packages can be used for\nfurther analysis in these R classes.\n\n## Contributing\nWe accept contributions to improve the package. Before contributing, please follow these steps:\n\n-    Contributions should be thoroughly tested with testthat.\n-    Code style should attempt to follow the tidyverse style guide.\n-    Please attempt to describe what you want to do prior to contributing by submitting an issue.\n-    Please follow the typical github fork - pull-request workflow.\n-    Make sure you use roxygen and run Check before contributing. More on this front as the package matures.\n## Code of conduct\nPlease note that this package is released with a [Contributor\nCode of Conduct](https://ropensci.org/code-of-conduct/). \nBy\ncontributing to this project, you agree to abide by its terms.\n## Citation\n\n``` r\ncitation(\"rsat\")[1]\n```\n\nTo cite the package:\n\nU. Pérez-Goya, M. Montesino-SanMartin, A F Militino, M D Ugarte (2021). rsat: Dealing with Multiplatform Satellite Images from Landsat, MODIS, and Sentinel. R package version 0.1.16. \u003chttps://github.com/ropensci/rsat\u003e.\n\n## Acknowledgements\n\nThis work has been financed by projects MTM2017-82553-R (AEI/FEDER, UE) and PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.\n\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["air-pollution","routing","geocoding"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/191562","html_url":"https://ost.ecosyste.ms/projects/191562"}