xrspatial.multispectral.ndmi¶
- xrspatial.multispectral.ndmi(nir_agg: xarray.core.dataarray.DataArray, swir1_agg: xarray.core.dataarray.DataArray, name='ndmi')[source]¶
Computes Normalized Difference Moisture Index. Used to determine vegetation water content.
- Parameters
nir_agg (xr.DataArray) – 2D array of near-infrared band data. (Landsat 4-7: Band 4) (Landsat 8: Band 5)
swir1_agg (xr.DataArray) – 2D array of shortwave infrared band. (Landsat 4-7: Band 5) (Landsat 8: Band 6)
name (str, default='ndmi') – Name of output DataArray.
- Returns
ndmi_agg – 2D array of ndmi values. All other input attributes are preserved.
- Return type
xr.DataArray of same type as inputs
References
Examples
import matplotlib.pyplot as plt from xrspatial.multispectral import ndmi from xrspatial.datasets import get_data # Open Example Data data = get_data('sentinel-2') nir = data['NIR'] swir1 = data['SWIR1'] # Generate NDMI Aggregate Array ndmi_agg = ndmi(nir_agg = nir, swir1_agg = swir1) # Plot NIR Band nir.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("NIR Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot SWIR1 Band swir1.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("SWIR1 Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot NDMI ndmi_agg.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("NDMI") plt.ylabel("latitude") plt.xlabel("longitude")
>> print(ndmi_agg[100:103, 100: 102]) <xarray.DataArray 'ndmi' (y: 3, x: 2)> array([[-0.15868181, -0.19701014], [-0.14994299, -0.18686172], [-0.17901749, -0.21133603]]) 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
