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",
...
)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
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 |
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.