Band math#
Vegetation indices#
Enhanced Vegetation Index (EVI)#
In [1]: import geowombat as gw
In [2]: from geowombat.data import rgbn
Calculate a vegetation index, returning an xarray.DataArray.
In [3]: with gw.open(rgbn) as ds:
...: print(ds)
...: evi = ds.gw.evi(sensor='rgbn', scale_factor=0.0001)
...: print(evi)
...:
<xarray.DataArray (band: 4, y: 403, x: 515)> Size: 830kB
dask.array<open_rasterio-b2d13b66c2943e829fbd41033004fd37<this-array>, shape=(4, 403, 515), dtype=uint8, chunksize=(4, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* band (band) int64 32B 1 2 3 4
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
Attributes: (12/14)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan, nan, nan, nan)
_FillValue: nan
... ...
AREA_OR_POINT: Area
DataType: Generic
filename: /home/docs/checkouts/readthedocs.org/user_builds/geo...
resampling: nearest
_data_are_separate: 0
_data_are_stacked: 0
<xarray.DataArray (band: 1, y: 403, x: 515)> Size: 2MB
dask.array<where, shape=(1, 403, 515), dtype=float64, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* band (band) <U3 12B 'evi'
Attributes: (12/18)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan,)
_FillValue: nan
... ...
_data_are_separate: 0
_data_are_stacked: 0
pre-scaling: 0.0001
sensor: rgbn
vi: evi
drange: (0, 1)
Use the configuration context to set parameters.
In [4]: with gw.config.update(sensor='rgbn', scale_factor=0.0001):
...: with gw.open(rgbn) as ds:
...: evi = ds.gw.evi()
...: print(evi)
...:
<xarray.DataArray (band: 1, y: 403, x: 515)> Size: 2MB
dask.array<where, shape=(1, 403, 515), dtype=float64, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* band (band) <U3 12B 'evi'
Attributes: (12/18)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan,)
_FillValue: nan
... ...
resampling: nearest
_data_are_separate: 0
_data_are_stacked: 0
pre-scaling: 0.0001
vi: evi
drange: (0, 1)
Two-band Enhanced Vegetation Index (EVI2)#
In [5]: with gw.config.update(sensor='rgbn', scale_factor=0.0001):
...: with gw.open(rgbn) as ds:
...: evi2 = ds.gw.evi2()
...: print(evi2)
...:
<xarray.DataArray (band: 1, y: 403, x: 515)> Size: 2MB
dask.array<where, shape=(1, 403, 515), dtype=float64, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* band (band) <U4 16B 'evi2'
Attributes: (12/18)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan,)
_FillValue: nan
... ...
resampling: nearest
_data_are_separate: 0
_data_are_stacked: 0
pre-scaling: 0.0001
vi: evi2
drange: (0, 1)
Normalized difference indices#
Use the generic xarray.DataArray.gw.norm_diff() function with any two-band combination.
In [6]: with gw.config.update(sensor='rgbn'):
...: with gw.open(rgbn) as ds:
...: d = ds.gw.norm_diff('red', 'nir')
...: print(d)
...:
<xarray.DataArray (band: 1, y: 403, x: 515)> Size: 2MB
dask.array<where, shape=(1, 403, 515), dtype=float64, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* band (band) <U9 36B 'norm-diff'
Attributes: (12/18)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan,)
_FillValue: nan
... ...
resampling: nearest
_data_are_separate: 0
_data_are_stacked: 0
pre-scaling: 1.0
vi: norm-diff
drange: (-1, 1)
Tasseled cap transformations#
In [7]: with gw.config.update(sensor='qb', scale_factor=0.0001):
...: with gw.open(rgbn) as ds:
...: tcap = ds.gw.tasseled_cap()
...: print(tcap)
...:
<xarray.DataArray (band: 3, y: 403, x: 515)> Size: 5MB
dask.array<transpose, shape=(3, 403, 515), dtype=float64, chunksize=(3, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* x (x) float64 4kB 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
* y (y) float64 3kB 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* band (band) <U10 120B 'brightness' 'greenness' 'wetness'
Attributes: (12/15)
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: 32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan, nan, nan, nan)
_FillValue: nan
... ...
DataType: Generic
sensor: Quickbird with 4 (visible + NIR) bands
filename: /home/docs/checkouts/readthedocs.org/user_builds/geo...
resampling: nearest
_data_are_separate: 0
_data_are_stacked: 0
Additional useful indices are available, such as the normalized burned ratio (NBR) and woody index (WI). For a full list of indices, see the API docs