xrspatial.multispectral.nbr¶
- xrspatial.multispectral.nbr(nir_agg: xarray.core.dataarray.DataArray, swir2_agg: xarray.core.dataarray.DataArray, name='nbr')[source]¶
Computes Normalized Burn Ratio. Used to identify burned areas and provide a measure of burn severity.
- Parameters
nir_agg (xr.DataArray) – 2D array of near-infrared band.
swir_agg (xr.DataArray) – 2D array of shortwave infrared band. (Landsat 4-7: Band 6) (Landsat 8: Band 7)
name (str, default='nbr') – Name of output DataArray.
- Returns
nbr_agg – 2D array of nbr values. All other input attributes are preserved.
- Return type
xr.DataArray of the same type as inputs
References
Examples
import matplotlib.pyplot as plt from xrspatial.multispectral import nbr from xrspatial.datasets import get_data # Open Example Data data = get_data('sentinel-2') nir = data['NIR'] swir2 = data['SWIR2'] # Generate NBR Aggregate Array nbr_agg = nbr(nir_agg = nir, swir2_agg = swir2) # Plot NIR Band nir.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("NIR Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot SWIR2 Band swir2.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("SWIR2 Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot NBR nbr_agg.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("NBR") plt.ylabel("latitude") plt.xlabel("longitude")
print(nbr_agg[100:103, 100: 102]) <xarray.DataArray 'nbr' (y: 3, x: 2)> array([[-0.10251108, -0.1321408 ], [-0.09691096, -0.12659354], [-0.10823033, -0.14486392]]) Coordinates: * x (x) float64 6.01e+05 6.01e+05 * y (y) float64 4.699e+06 4.699e+06 4.699e+06 band int32 ... Attributes: (12/13) transform: [ 1.00000e+01 0.00000e+00 6.00000e+05 0.0000... # noqa crs: +init=epsg:32719 res: [10. 10.] is_tiled: 1 nodatavals: nan scales: 1.0 ... ... instrument: Sentinel-2 Band: 07 Name: NIR Bandwidth (µm): 115 Nominal Wavelength (µm): 0.842 Resolution (m): 10
