xrspatial.multispectral.gci¶
- xrspatial.multispectral.gci(nir_agg: xarray.core.dataarray.DataArray, green_agg: xarray.core.dataarray.DataArray, name='gci')[source]¶
Computes Green Chlorophyll Index. Used to estimate the content of leaf chorophyll and predict the physiological state of vegetation and plant health.
- Parameters
nir_agg (xr.DataArray) – 2D array of near-infrared band data.
green_agg (xr.DataArray) – 2D array of green band data.
name (str, default='gci') – Name of output DataArray.
- Returns
gci_agg – 2D array of gci values. All other input attributes are preserved.
- Return type
xarray.DataArray of the same type as inputs
References
Examples
import matplotlib.pyplot as plt from xrspatial.multispectral import gci from xrspatial.datasets import get_data # Open Example Data data = get_data('sentinel-2') nir = data['NIR'] green = data['Green'] # Generate GCI Aggregate Array gci_agg = gci(nir_agg = nir, green_agg = green) # Plot NIR Band nir.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("NIR Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot Green Band green.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("Green Band") plt.ylabel("latitude") plt.xlabel("longitude") # Plot GCI gci_agg.plot(cmap = 'Greys', aspect = 2, size = 4) plt.title("GCI") plt.ylabel("latitude") plt.xlabel("longitude")
>>> print(gci_agg[100:103, 100: 102]) <xarray.DataArray 'gci' (y: 3, x: 2)> array([[0.35625 , 0.33097345], [0.3420342 , 0.29551451], [0.34822242, 0.28270413]]) 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
