RGISTools-package: `RGISTools': Handling Multiplatform Satellite Images

Description

This package enables you downloading, customizing, and processing time series of satellite images from Landsat, MODIS and Sentinel in a standardized way. Some functions download and convert automatically the platform-specific file formats into GTiff, so they can be loaded in `R'. The customization functions support tile mosaicking, cropping, cloud masking and deriving new variables of interest, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), etc. Tile mosaicking is required when the region of interest extends over several tiles, so they can be combined into a single image. Cropping involves removing the pixels outside the region of interest, making any analysis more computationally and memory efficient. Cloud masking eliminates cloud reflectance that would otherwise be erroneously attributed to land surface features. Cloud removal and (measurement or processing) errors trigger data gaps and outliers, decreasing the quality and quantity of measurements. Hence, the package includes a set of function for filling and smoothing the satellite imagery. The combination of functions in `RGISTools' results in a stack of satellite images ready-to-use. Due to the wide variety of procedures and sources of information being handled in `RGISTools', the functions are divided into 7 categories, which are identified by the first 3 characters of the function names;

mod identifies MODIS Terra and Aqua satellite functions.
sen identifies Sentinel functions.
ls7 identifies Landsat-7 functions.
ls8 identifies Landsat-8 functions.
ls identifies both Landsat-7 and Landsat-8 functions.
gen identifies function for being used in any of the three platforms.
var identifies function for deriving variables in any of the three platforms.

Arguments

I. Landsat functions

The Landsat program is currently releasing imagery captured by two satellites; the Landsat-7 and Lansat-8. Both satellites are treated separately in coding terms due to discrepancies in their spectral coverage and data formats. To download Landsat imagery with the following functions, a USGS's `EarthExplorer' account is required. Please, register here.

Landsat-7

`ls7LoadMetadata`	Loads the Landsat-7 metadata file
`ls7Search`	Searches a time series of Landsat-7 images
`lsPreview`	Previews Landsat satellite images
`lsDownSearch`	Downloads a time series of Landsat images
`lsCloudMask`	Creates clouds masks for Landsat images
`lsMosaic`	Mosaics Landsat images
`ls7FolderToVar`	Computes new variables from Landsat-7 multispectral images
`genSaveTSRData`	Saves a time series of images
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Landsat-8

`ls8LoadMetadata`	Loads the Landsat-8 metadata file
`ls8Search`	Searches a time series of Landsat-8 images
`lsPreview`	Previews Landsat satellite images
`lsDownSearch`	Downloads a time series of Landsat images
`lsCloudMask`	Creates cloud masks for Landsat images
`lsMosaic`	Mosaics Landsat images
`ls8FolderToVar`	Computes new variables from Landsat-8 multispectral images
`genSaveTSRData`	Saves a time series of images
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II. MODIS functions

Functions in `RGISTools' download all land products from Terra and Aqua satellites, but the processing focuses on the multispectral images. Be aware that an `EarthData' account is required to use NASA's web service so, please, register here.

`modSearch`	Searches a time series of MODIS images
`modPreview`	Previews MODIS satellite images
`modDownSearch`	Downloads a time series of MODIS images
`modMosaic`	Mosaics MODIS images from the land products
`modFolderToVar`	Computes new variables from MODIS multispectral images
`modCloudMask`	Creates cloud masks for MODIS images
`genSaveTSRData`	Saves a time series of images
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III. Sentinel functions

Sentinel archives provide a wide variety of products based on a 5-satellite constellation. The functions to download Sentinel images can cope with any product available in ESA's `SciHub' web service. However, image processing is focused on Sentinel-2 multispectal images. `SciHub' credentials are required to download Sentinel imagery and can be obtained here.

`senSearch`	Searches a time series of Sentinel images
`senPreview`	Previews Sentinel images
`senDownSearch`	Downloads a time series of Sentinel images
`senMosaic`	Mosaics Sentinel-2 images
`senCloudMask`	Creates cloud masks for Sentinel-2 images
`senFolderToVar`	Computes new variables from Sentinel-2 multispectral images
`genSaveTSRData`	Saves a time series of images
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IV. Important general functions

In addition to functions above, the package provides some general functions for a better data handling:

`genCompositions`	Creates image compositions from a time series of satellite images
`genSmoothingIMA`	Fills the gaps and smooths outliers in a time series of satellite images
`genSmoothingCovIMA`	Fills the gaps and smooths outliers in a time series of satellite images using covariates
`genPlotGIS`	Plots satellite images with a proper GIS format
`genGetDates`	Gets the capturing date of an image from the name of a `RasterLayer`
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V. Remote sensing variables

New variables can be derived from multispectral images. The most common variables in the scientific literature are pre-programmed in `RGISTools'. They can be identified by the prefix "var".

`varEVI`	Calculates the enhanced vegetation index (EVI)
`varMSAVI2`	Calculates the modified soil-adjusted vegetation index (MSAVI2)
`varNBR`	Calculates the normalized burn ratio (NBR)
`varNBR2`	Calculates the normalized burn ratio 2 (NBR2)
`varNDMI`	Calculates the normalized difference moisture index (NDMI)
`varNDVI`	Calculates the normalized difference vegetation index (NDVI)
`varNDWI`	Calculates the normalized difference water index (NDWI)
`varRGB`	Generates a Red-Green-Blue (RGB) image
`varSAVI`	Calculates the soil-adjusted vegetation index (SAVI)
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Details

Below, there is a list of the most important functions grouped by platform, and listed in operational order. These functions include searching, previewing, downloading, mosaicking, deriving new variables, compositing, cloud masking and filling/smoothing satellite imagery.

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