classifyQA: Classify Landsat QA bands

Description Usage Arguments Details Value See Also Examples

View source: R/classifyQA.R

Description

extracts five classes from QA band: background, cloud, cirrus, snow and water.

Usage

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classifyQA(img, type = c("background", "cloud", "cirrus", "snow",
  "water"), confLayers = FALSE, sensor = "OLI",
  legacy = "collection1", ...)

Arguments

img

RasterLayer. Landsat 8 OLI QA band.

type

Character. Classes which should be returned. One or more of c("background", "cloud", "cirrus","snow", "water").

confLayers

Logical. Return one layer per class classified by confidence levels, i.e. cloud:low, cloud:med, cloud:high.

sensor

Sensor to encode. Options: c("OLI", "TIRS", "ETM+", "TM", "MSS").

legacy

Encoding systematic Options: c("collection1", "pre_collection"). Default is "collection1" for the Landsat Collection 1 8-bit quality designations. Use "pre_collection" for imagery downloaded before the Collection 1 quality designations were introduced

...

further arguments passed to writeRaster

Details

By default each class is queried for *high* confidence. See encodeQA for details. To return the different confidence levels per condition use confLayers=TRUE. This approach corresponds to the way LandsatLook Quality Images are produced by the USGS.

Value

Returns a RasterLayer with maximal five classes:

class value
background 1L
cloud 2L
cirrus 3L
snow 4L
water 5L

Values outside of these classes are returned as NA. If confLayers = TRUE then a RasterStack with one layer per condition (except 'background') is returned, whereby each layer contains the confidence level of the condition.

Confidence value
low 1L
med 2L
high 3L

See Also

encodeQA decodeQA

Examples

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library(raster)
qa <- raster(ncol = 100, nrow=100, val = sample(1:2^14,  10000))

## QA classes
qacs <- classifyQA(img = qa)
## Confidence levels
qacs_conf <- classifyQA(img = qa, confLayers = TRUE)

bleutner/RStoolbox documentation built on Nov. 12, 2019, 1:55 p.m.