xrspatial.multispectral.ebbi

xrspatial.multispectral.ebbi(red_agg: xarray.core.dataarray.DataArray, swir_agg: xarray.core.dataarray.DataArray, tir_agg: xarray.core.dataarray.DataArray, name='ebbi')[source]

Computes Enhanced Built-Up and Bareness Index (EBBI) which allows for easily distinguishing between built-up and bare land areas.

red_agg: xarray.DataArray

2D array of red band data. (Sentinel 2: Band 4)

swir_agg: xarray.DataArray

2D array of shortwave infrared band data. (Sentinel 2: Band 11)

tir_agg: xarray.DataArray

2D array of thermal infrared band data.

name: str, optional (default = “ebbi”)

Name of output DataArray.

Returns

  • xarray.DataArray – 2D array, of the same type as the input of calculated ebbi values. All other input attributes are preserved

  • Notes

  • ———-

  • Algorithm References

  • - RDRR.io, EBBI (Enhanced Built-Up and Bareness Index, https://rdrr.io/cran/LSRS/man/EBBI.html, Accessed Apr. 21, 2021. # noqa)

  • Examples

  • ———-

  • Imports

  • >>> import numpy as np

  • >>> import xarray as xr

  • >>> import xrspatial

  • Create Sample Band Data

  • >>> np.random.seed(1)

  • >>> red_agg = xr.DataArray(np.random.rand(4,4), dims = [“lat”, “lon”])

  • >>> height, width = red_agg.shape

  • >>> _lat = np.linspace(0, height - 1, height)

  • >>> _lon = np.linspace(0, width - 1, width)

  • >>> red_agg[“lat”] = _lat

  • >>> red_agg[“lon”] = _lon

  • >>> np.random.seed(5)

  • >>> swir_agg = xr.DataArray(np.random.rand(4,4), dims = [“lat”, “lon”])

  • >>> height, width = swir_agg.shape

  • >>> _lat = np.linspace(0, height - 1, height)

  • >>> _lon = np.linspace(0, width - 1, width)

  • >>> swir_agg[“lat”] = _lat

  • >>> swir_agg[“lon”] = _lon

  • >>> np.random.seed(6)

  • >>> tir_agg = xr.DataArray(np.random.rand(4,4), dims = [“lat”, “lon”])

  • >>> height, width = tir_agg.shape

  • >>> _lat = np.linspace(0, height - 1, height)

  • >>> _lon = np.linspace(0, width - 1, width)

  • >>> tir_agg[“lat”] = _lat

  • >>> tir_agg[“lon”] = _lon

  • >>> print(red_agg, swir_agg, tir_agg)

  • <xarray.DataArray (lat (4, lon: 4)>)

  • array([[4.17022005e-01, 7.20324493e-01, 1.14374817e-04, 3.02332573e-01], – [1.46755891e-01, 9.23385948e-02, 1.86260211e-01, 3.45560727e-01], [3.96767474e-01, 5.38816734e-01, 4.19194514e-01, 6.85219500e-01], [2.04452250e-01, 8.78117436e-01, 2.73875932e-02, 6.70467510e-01]])

  • Coordinates – * lat (lat) float64 0.0 1.0 2.0 3.0

    • lon (lon) float64 0.0 1.0 2.0 3.0

    <xarray.DataArray (lat: 4, lon: 4)>

  • array([[0.22199317, 0.87073231, 0.20671916, 0.91861091], – [0.48841119, 0.61174386, 0.76590786, 0.51841799], [0.2968005 , 0.18772123, 0.08074127, 0.7384403 ], [0.44130922, 0.15830987, 0.87993703, 0.27408646]])

  • Coordinates – * lat (lat) float64 0.0 1.0 2.0 3.0

    • lon (lon) float64 0.0 1.0 2.0 3.0

    <xarray.DataArray (lat: 4, lon: 4)>

  • array([[0.89286015, 0.33197981, 0.82122912, 0.04169663], – [0.10765668, 0.59505206, 0.52981736, 0.41880743], [0.33540785, 0.62251943, 0.43814143, 0.73588211], [0.51803641, 0.5788586 , 0.6453551 , 0.99022427]])

  • Coordinates –

    • lat (lat) float64 0.0 1.0 2.0 3.0

    • lon (lon) float64 0.0 1.0 2.0 3.0 Create EBBI DataArray

  • >>> data = xrspatial.multispectral.ebbi(red_agg, swir_agg, tir_agg)

  • >>> print(data)

  • <xarray.DataArray ‘ebbi’ (lat (4, lon: 4)>)

  • array([[-2.43983486, -2.58194492, 3.97432599, -0.42291921], – [-0.11444052, 0.96786363, 0.59269999, 0.42374096], [ 0.61379897, -0.23840436, -0.05598088, 0.95193251], [ 1.32393891, 0.41574839, 0.72484653, -0.80669034]])

  • Coordinates –

    • lat (lat) float64 0.0 1.0 2.0 3.0

    • lon (lon) float64 0.0 1.0 2.0 3.0

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