classifyQA: Classify Landsat QA bands

In RStoolbox: Remote Sensing Data Analysis

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",
  ...
)

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.

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

See Also

encodeQA decodeQA

Examples

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)

RStoolbox documentation built on May 29, 2024, 6:34 a.m.