xrspatial.multispectral.nbr2

xrspatial.multispectral.nbr2(swir1_agg: xarray.core.dataarray.DataArray, swir2_agg: xarray.core.dataarray.DataArray, name='nbr2')

Computes Normalized Burn Ratio 2 “NBR2 modifies the Normalized Burn Ratio (NBR) to highlight water sensitivity in vegetation and may be useful in post-fire recovery studies.” 1

Parameters

• swir1_agg (xr.DataArray) – 2D array of near-infrared band data. shortwave infrared band (Sentinel 2: Band 11) (Landsat 4-7: Band 5) (Landsat 8: Band 6)

• swir2_agg (xr.DataArray) – 2D array of shortwave infrared band data. (Landsat 4-7: Band 6) (Landsat 8: Band 7)

• name (str default='nbr2') – Name of output DataArray.

Returns

nbr2_agg – 2D array of nbr2 values. All other input attributes are preserved.

Return type

xr.DataArray of same type as inputs.

Notes

1

https://www.usgs.gov/land-resources/nli/landsat/landsat-normalized-burn-ratio-2 # noqa

Examples

>>> from xrspatial.datasets import get_data
>>> data = get_data('sentinel-2')  # Open Example Data
>>> swir1 = data['SWIR1']
>>> swir2 = data['SWIR2']
>>> from xrspatial.multispectral import nbr2
>>> # Generate NBR2 Aggregate Array
>>> nbr2_agg = nbr2(swir1_agg=swir1, swir2_agg=swir2)
>>> swir1.plot(aspect=2, size=4)
>>> swir2.plot(aspect=2, size=4)
>>> nbr2_agg.plot(aspect=2, size=4)

(png, hires.png, pdf)

(png, hires.png, pdf)

(png, hires.png, pdf)

>>> y1, x1, y2, x2 = 100, 100, 103, 104
>>> print(swir1[y1:y2, x1:x2].data)
[[2092. 2242. 2333. 2382.]
 [2017. 2150. 2303. 2344.]
 [2124. 2244. 2367. 2452.]]
>>> print(swir2[y1:y2, x1:x2].data)
[[1866. 1962. 2086. 2112.]
 [1811. 1900. 2012. 2041.]
 [1838. 1956. 2067. 2109.]]
>>> print(nbr2_agg[y1:y2, x1:x2].data)
[[0.05709954 0.06660324 0.055895   0.06008011]
 [0.053814   0.0617284  0.06743917 0.0690992 ]
 [0.07218576 0.06857143 0.067659   0.07520281]]