classifyQA: Classify Landsat QA bands

Description

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

Usage

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

Value

Returns a SpatRaster 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

Arguments

img: SpatRaster. 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.

Examples

Run this code

library(terra)
qa <- rast(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)