NAME
i.cva - Performs Change Vector Analysis (CVA) in two dimensions.
KEYWORDS
imagery,
transformation,
CVA,
change vector analysis
SYNOPSIS
i.cva
i.cva --help
i.cva xaraster=name xbraster=name yaraster=name ybraster=name output=basename [custom_threshold=float] [stat_threshold=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
- --overwrite
- Allow output files to overwrite existing files
- --help
- Print usage summary
- --verbose
- Verbose module output
- --quiet
- Quiet module output
- --ui
- Force launching GUI dialog
Parameters:
- xaraster=name [required]
- Name of the first raster for X axis
- xbraster=name [required]
- Name of the the second raster for X axis
- yaraster=name [required]
- Name of the first raster for Y axis
- ybraster=name [required]
- Name of the second raster for Y axis
- output=basename [required]
- Name for output basename raster maps (angle and magnitude)
- Name for output basename raster map(s)
- custom_threshold=float
- Use a custom threshold
- stat_threshold=float
- Use a statystical parameter for the threshold (mean + N * standard deviation)
i.cva calculates Change Vector Analysis (CVA) for two input
variables. CVA is a remote sensing technique used for change detection
analysis. As input for CVA, two maps for each date must be given: in general,
on X axis an indicator of overall reflectance and on Y axis an indicator of
vegetation conditions. A common choice for the indicators is Albedo and NDVI
(Normalized Difference Vegetation Index) or the Brightness and Greenness
features features of the Tasselled Cap (TC) transform.
For each pixel of the original image, CVA gives in output a map of the angle
and a map of the magnitude of the vector of the change between two dates.
Read Malila et al. for a complete explanation of the technique. This module
might require a first transformation of the data to Top Of Atmosphere
Reflectance (TOAR); if the TC transform are chosen as indicators, the TC
transform should be then performed as well before running CVA.
Four parameters are required in input:
- xaraster: first date map for X axis,
- xbraster: second date map for X axis,
- yaraster: first date map for Y axis,
- ybraster: second date map for Y axis.
The following maps can be generated in output:
- basename_angle: map of the angles of the change vector between
the two dates;
- basename_angle_class: map of the angles, classified by the four
quadrants (0-90, 90-180, ...);
- basename_magnitude: map of the magnitudes of the change vector
between the two dates;
- basename_change: final map of the change
The change detection map is created using the classified angle map and applying
a threshold to the magnitude: the change is given by the pixels that have
values higher than the threshold, divided in four categories depending on
the quadrant they belong to.
The threshold can be chosen manually (custom value, given by personal
criteria) or using statistical criteria. In this case the mean of the magnitude
values is used and the user can choose the multiples of N standard
deviation to sum to the mean (threshold = mean + N * standard deviation).
One could consider of running the module at first without assigning a
threshold, in order to have an idea of the range of the magnitude and to
choose an appropriate custom threshold (for univariate statistical parameters
run r.univar). In this case i.cva gives
in output only three maps: the angle, angle classified and magnitude maps.
Calculation of CVA maps from North Carolina Landsat 5 TM and 7 ETM scenes,
using lsat5_1987 and lsat7_2002.
The Tasselled cap maps are calculated for TOAR data.
# compute tasscap of 1987 scene
g.region raster=lsat5_1987_10 -p
i.tasscap sensor=landsat5_tm \
input=lsat5_1987_10,lsat5_1987_20,lsat5_1987_30,lsat5_1987_40,lsat5_1987_50,lsat5_1987_70 \
output=lsat5_1987_tasscap
# compute tasscap of 2002 scene
g.region raster=lsat7_2002_10 -p
i.tasscap sensor=landsat7_etm \
input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70 \
output=lsat7_2002_tasscap
# compute CVA
i.cva xaraster=lsat5_1987_tasscap.1 xbraster=lsat7_2002_tasscap.1 \
yaraster=lsat5_1987_tasscap.2 ybraster=lsat7_2002_tasscap.2 \
output=CVA_87_02 stat_threshold=1
Calculating DeltaX and DeltaY
Writing angle map CVA_87_02_angle
Writing magnitude map CVA_87_02_magnitude
Mean of magnitude values is: 0.091335330260002
Standard deviation of magnitude values is: 0.0671211630131731
Writing change detection map CVA_87_02_change
Threshold is 0.158456493273
Results:
CVA angle map 1
|
CVA classified angle map
|
CVA magnitude map
|
CVA change map
|
Optionally, labels can be added to the four quadrants of the change
map (after Zanchetta et al., 2016):
# assign legend
cat i_cva_legend_rules.csv
1:moisture reduction
2:chlorophyll increase
3:moisture increase
4:bare soil increase
r.category map=CVA_87_02_change separator=":" rules=i_cva_legend_rules.csv
# assign colors
cat i_cva_color_rules.csv
1 217:255:0
2 10:214:10
3 75:173:255
4 139:105:20
r.colors map=CVA_87_02_change rules=i_cva_color_rules.csv
- Malila W A, Lafayette W. Change Vector Analysis (1980): An Approach
for Detecting Forest Changes with Landsat. LARS Symp., pp. 326-335
(PDF)
- Zanchetta, A., Bitelli, G. & Karnieli, A. (2016): Monitoring desertification
by remote sensing using the Tasselled Cap transform for long-term
change detection. Nat Hazards, 83(Suppl 1):223-237.
(DOI)
i.albedo,
i.vi,
i.aster.toar,
i.landsat.toar,
r.univar
Anna Zanchetta
SOURCE CODE
Available at:
i.cva source code
(history)
Latest change: Monday Jan 30 19:52:26 2023 in commit: cac8d9d848299297977d1315b7e90cc3f7698730
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GRASS GIS 8.4.1dev Reference Manual