Dual-pol Radar Vegetation Index (dprvi)#

polsartools.dprvi(infolder, window_size=1, outType='tif', cog_flag=False, cog_overviews=[2, 4, 8, 16], write_flag=True, max_workers=None, block_size=(512, 512), progress_callback=None)[source]

Compute dual-pol Radar Vegetation Index (DpRVI) from C2 matrix data.

This function processes dual-polarization SAR data to generate the DpRVI, which is useful for vegetation monitoring and biomass estimation. The processing is done in parallel blocks for improved performance.

Examples

>>> # Basic usage with default parameters
>>> dprvi("/path/to/c2_data")

>>> # Advanced usage with custom parameters
>>> dprvi(
...     infolder="/path/to/c2_data",
...     window_size=3,
...     outType="tif",
...     cog_flag=True,
...     block_size=(1024, 1024)
... )
Parameters:

  • infolder (str) – Path to the input folder containing C2 matrix files.

  • window_size (int, default=1) – Size of the spatial averaging window. Larger windows reduce speckle noise but decrease spatial resolution.

  • outType ({'tif', 'bin'}, default='tif') – Output file format: - ‘tif’: GeoTIFF format with georeferencing information - ‘bin’: Raw binary format

  • cog_flag (bool, default=False) – If True, creates a Cloud Optimized GeoTIFF (COG) with internal tiling and overviews for efficient web access.

  • cog_overviews (list[int], default=[2, 4, 8, 16]) – Overview levels for COG creation. Each number represents the decimation factor for that overview level.

  • write_flag (bool, default=True) – If True, writes results to disk. If False, only processes data in memory.

  • max_workers (int | None, default=None) – Maximum number of parallel processing workers. If None, uses CPU count - 1 workers.

  • block_size (tuple[int, int], default=(512, 512)) – Size of processing blocks (rows, cols) for parallel computation. Larger blocks use more memory but may be more efficient.

Returns:

Results are written to disk as either ‘dprvi.tif’ or ‘dprvi.bin’ in the input folder.

Return type:

None

The formulation of DpRVI is as follows:

\[\text{DpRVI} = 1- \text{DOP}_{dp}\Big(\frac{\lambda_1}{\lambda_1+\lambda_2}\Big)\]

where,

\[\text{DOP}_{dp} = \sqrt{1-\frac{4\times \text{det ([C2])}}{\text{(Trace [C2])}^2}}\]

\(\text{[C2]}\) is co-variance matrix, and \(\lambda_1, \lambda_2\) are the eigen values of \(\langle\mathbf{[C2]}\rangle\) matrix in descending order. Further details on DpRVI can be obtained from [[5]](#5)

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