RStoolbox: cloudShadowMask – R documentation

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cloudShadowMask

Cloud Shadow Masking for Flat Terrain

Description

Intended for interactive use, cloudShadowMask will ask the user to select a few corresponding cloud/cloudShadow pixels which will be used to estimate coordinates for a linear cloudmask shift.

Usage

cloudShadowMask(img, cm, nc = 5, shiftEstimate = NULL,
  preciseShift = NULL, quantile = 0.2, returnShift = FALSE)

Arguments

`img`	Raster* object containing the scene
`cm`	Raster* object. Cloud mask (typically the result of `cloudMask`)
`nc`	Integer. Number of control points. A few points (default) are fine because the final shift is estimated by coregisterImages.
`shiftEstimate`	NULL or numeric vector of length two (x,y). Estimated displacement of shadows in map units. If `NULL`, the user will be asked to select control points interactively.
`preciseShift`	NULL or numeric vector of length two (x,y). Use this if cloud/cloud-shadow displacement is already known, e.g. from a previous run of `cloudShadowMask`.
`quantile`	Numeric (between 0 and 1). Quantile threshold used for image co-registration. By default the 20% quantile of the total intensity (sum) of the image is used as potential shadow mask.
`returnShift`	Logical. Return a numeric vector containing the shift parameters. Useful if you estimate parameters on a subset of the image.

Details

This is a very simplistic approach to cloud shadow masking (simple shift of the cloud mask). It is not image based and accuracy will suffer from clouds at different altitudes. However, just as cloudMask this is a quick and easy to use tool for Landsat data if you're just working on a few scenes and don't have fMask or CDR data at hand. Although for some test scenes it does perform surprisingly well.

Value

Returns a RasterLayer with the cloud shadow mask (0 = shadow, NA = not-shadow).

Examples

library(raster)
library(ggplot2)
## Import Landsat example subset
data(lsat) 
## We have two tiny clouds in the east
ggRGB(lsat, stretch = "lin")

## Calculate cloud index
cldmsk    <- cloudMask(lsat, blue = 1, tir = 6)
ggR(cldmsk, 2, geom_raster = TRUE) 

## Define threshold (re-use the previously calculated index)
## Everything above the threshold is masked
## In addition we apply a region-growing around the core cloud pixels
cldmsk_final <- cloudMask(cldmsk, threshold = 0.1, buffer = 5) 

## Plot cloudmask 
ggRGB(lsat, stretch = "lin") +
   ggR(cldmsk_final[[1]], ggLayer = TRUE, forceCat = TRUE, geom_raster = TRUE) +
   scale_fill_manual(values = "red", na.value = NA)

#' ## Estimate cloud shadow displacement
## Interactively (click on cloud pixels and the corresponding shadow pixels)
## Not run:  shadow <- cloudShadowMask(lsat, cldmsk_final, nc = 2) 

## Non-interactively. Pre-defined shadow displacement estimate (shiftEstimate)
shadow <- cloudShadowMask(lsat, cldmsk_final, shiftEstimate = c(-16,-6))

## Plot
csmask <- raster::merge(cldmsk_final[[1]], shadow)
ggRGB(lsat, stretch = "lin") +
        ggR(csmask, ggLayer = TRUE, forceCat = TRUE, geom_raster = TRUE) +
        scale_fill_manual(values = c("blue", "yellow"), 
        labels = c("shadow", "cloud"), na.value = NA)

RStoolbox

Tools for Remote Sensing Data Analysis

v0.2.6

GPL (>= 3)

Authors

Benjamin Leutner [cre, aut], Ned Horning [aut], Jakob Schwalb-Willmann [aut], Robert J. Hijmans [ctb]

Initial release