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legacy

darts_preprocessing.legacy

PLANET scene based preprocessing.

logger module-attribute 

logger = logging.getLogger(
    __name__.replace("darts_", "darts.")
)

calculate_ndvi 

calculate_ndvi(optical: xarray.Dataset) -> xarray.Dataset

Calculate NDVI from an xarray Dataset containing spectral bands.

This function will clip the NIR and Red bands to the range [0, 1] before calculating NDVI to avoid potential numerical instabilities from negative reflections.

Parameters:

  • optical (xarray.Dataset) –

    The xarray Dataset containing the spectral bands.

Returns:

  • xarray.Dataset –

    xr.DataArray: A new DataArray containing the calculated NDVI values.

Notes

NDVI (Normalized Difference Vegetation Index) is calculated using the formula: NDVI = (NIR - Red) / (NIR + Red)

Source code in darts-preprocessing/src/darts_preprocessing/engineering/indices.py 
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@stopwatch("Calculating NDVI", printer=logger.debug)
def calculate_ndvi(optical: xr.Dataset) -> xr.Dataset:
    """Calculate NDVI from an xarray Dataset containing spectral bands.

    This function will clip the NIR and Red bands to the range [0, 1] before calculating NDVI to avoid
    potential numerical instabilities from negative reflections.

    Args:
        optical (xr.Dataset): The xarray Dataset containing the spectral bands.

    Returns:
        xr.DataArray: A new DataArray containing the calculated NDVI values.

    Notes:
        NDVI (Normalized Difference Vegetation Index) is calculated using the formula:
            NDVI = (NIR - Red) / (NIR + Red)

    """
    nir = optical["nir"].clip(0, 1)
    r = optical["red"].clip(0, 1)
    ndvi = (nir - r) / (nir + r)
    ndvi = ndvi.clip(-1, 1)
    ndvi = ndvi.assign_attrs({"long_name": "NDVI"}).rename("ndvi")
    return ndvi

calculate_slope 

calculate_slope(
    arcticdem_ds: xarray.Dataset,
) -> xarray.Dataset

Calculate the slope of the terrain surface from an ArcticDEM Dataset.

Parameters:

  • arcticdem_ds (xarray.Dataset) –

    The ArcticDEM Dataset containing the 'dem' variable.

Returns:

  • xarray.Dataset –

    xr.Dataset: The input Dataset with the calculated slope added as a new variable 'slope'.

Source code in darts-preprocessing/src/darts_preprocessing/engineering/arcticdem.py 
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@stopwatch("Calculating slope", printer=logger.debug)
def calculate_slope(arcticdem_ds: xr.Dataset) -> xr.Dataset:
    """Calculate the slope of the terrain surface from an ArcticDEM Dataset.

    Args:
        arcticdem_ds (xr.Dataset): The ArcticDEM Dataset containing the 'dem' variable.

    Returns:
        xr.Dataset: The input Dataset with the calculated slope added as a new variable 'slope'.

    """
    slope_deg = slope(arcticdem_ds.dem)
    slope_deg.attrs = {
        "long_name": "Slope",
        "units": "degrees",
        "description": "The slope of the terrain surface in degrees.",
        "source": "ArcticDEM",
    }
    arcticdem_ds["slope"] = slope_deg.compute()
    return arcticdem_ds

calculate_topographic_position_index 

calculate_topographic_position_index(
    arcticdem_ds: xarray.Dataset,
    outer_radius: int,
    inner_radius: int,
) -> xarray.Dataset

Calculate the Topographic Position Index (TPI) from an ArcticDEM Dataset.

Parameters:

  • arcticdem_ds (xarray.Dataset) –

    The ArcticDEM Dataset containing the 'dem' variable.

  • outer_radius (int) –

    The outer radius of the annulus kernel in m.

  • inner_radius (int) –

    The inner radius of the annulus kernel in m.

Returns:

  • xarray.Dataset –

    xr.Dataset: The input Dataset with the calculated TPI added as a new variable 'tpi'.

Source code in darts-preprocessing/src/darts_preprocessing/engineering/arcticdem.py 
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@stopwatch.f("Calculating TPI", printer=logger.debug, print_kwargs=["outer_radius", "inner_radius"])
def calculate_topographic_position_index(arcticdem_ds: xr.Dataset, outer_radius: int, inner_radius: int) -> xr.Dataset:
    """Calculate the Topographic Position Index (TPI) from an ArcticDEM Dataset.

    Args:
        arcticdem_ds (xr.Dataset): The ArcticDEM Dataset containing the 'dem' variable.
        outer_radius (int, optional): The outer radius of the annulus kernel in m.
        inner_radius (int, optional): The inner radius of the annulus kernel in m.

    Returns:
        xr.Dataset: The input Dataset with the calculated TPI added as a new variable 'tpi'.

    """
    cellsize_x, cellsize_y = convolution.calc_cellsize(arcticdem_ds.dem)  # Should be equal to the resolution of the DEM
    # Use an annulus kernel if inner_radius is greater than 0
    outer_radius: Distance = Distance.parse(outer_radius, cellsize_x)
    if inner_radius > 0:
        inner_radius: Distance = Distance.parse(inner_radius, cellsize_x)
        kernel = convolution.annulus_kernel(cellsize_x, cellsize_y, outer_radius.meter, inner_radius.meter)
        attr_cell_description = (
            f"within a ring at a distance of {inner_radius}-{outer_radius} cells away from the focal cell."
        )
        logger.debug(
            f"Calculating Topographic Position Index with annulus kernel of {inner_radius}-{outer_radius} cells."
        )
    else:
        kernel = convolution.circle_kernel(cellsize_x, cellsize_y, outer_radius.meter)
        attr_cell_description = f"within a circle at a distance of {outer_radius} cells away from the focal cell."
        logger.debug(f"Calculating Topographic Position Index with circle kernel of {outer_radius} cells.")

    # Change dtype of kernel to float32 since we don't need the precision and f32 is faster
    kernel = kernel.astype("float32")

    if has_cuda_and_cupy() and arcticdem_ds.cupy.is_cupy:
        kernel = cp.asarray(kernel)

    tpi = arcticdem_ds.dem - convolution.convolution_2d(arcticdem_ds.dem, kernel) / kernel.sum()
    tpi.attrs = {
        "long_name": "Topographic Position Index",
        "units": "m",
        "description": "The difference between the elevation of a cell and the mean elevation of the surrounding"
        f"cells {attr_cell_description}",
        "source": "ArcticDEM",
    }

    arcticdem_ds["tpi"] = tpi.compute()

    return arcticdem_ds

preprocess_legacy_arcticdem_fast 

preprocess_legacy_arcticdem_fast(
    ds_arcticdem: xarray.Dataset,
    tpi_outer_radius: int,
    tpi_inner_radius: int,
) -> xarray.Dataset

Preprocess the ArcticDEM data with legacy (DARTS v1) preprocessing steps.

Parameters:

  • ds_arcticdem (xarray.Dataset) –

    The ArcticDEM dataset.

  • tpi_outer_radius (int) –

    The outer radius of the annulus kernel for the tpi calculation in number of cells.

  • tpi_inner_radius (int) –

    The inner radius of the annulus kernel for the tpi calculation in number of cells.

Returns:

  • xarray.Dataset –

    xr.Dataset: The preprocessed ArcticDEM dataset.

Source code in darts-preprocessing/src/darts_preprocessing/legacy.py 
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@stopwatch.f("Preprocessing arcticdem", printer=logger.debug, print_kwargs=["tpi_outer_radius", "tpi_inner_radius"])
def preprocess_legacy_arcticdem_fast(
    ds_arcticdem: xr.Dataset,
    tpi_outer_radius: int,
    tpi_inner_radius: int,
) -> xr.Dataset:
    """Preprocess the ArcticDEM data with legacy (DARTS v1) preprocessing steps.

    Args:
        ds_arcticdem (xr.Dataset): The ArcticDEM dataset.
        tpi_outer_radius (int): The outer radius of the annulus kernel for the tpi calculation in number of cells.
        tpi_inner_radius (int): The inner radius of the annulus kernel for the tpi calculation in number of cells.

    Returns:
        xr.Dataset: The preprocessed ArcticDEM dataset.

    """
    ds_arcticdem = calculate_topographic_position_index(ds_arcticdem, tpi_outer_radius, tpi_inner_radius)
    ds_arcticdem = calculate_slope(ds_arcticdem)

    return ds_arcticdem

preprocess_legacy_fast 

preprocess_legacy_fast(
    ds_optical: xarray.Dataset,
    ds_arcticdem: xarray.Dataset,
    ds_tcvis: xarray.Dataset,
    tpi_outer_radius: int = 100,
    tpi_inner_radius: int = 0,
    device: typing.Literal["cuda", "cpu"]
    | int = darts_utils.cuda.DEFAULT_DEVICE,
) -> xarray.Dataset

Preprocess optical data with legacy (DARTS v1) preprocessing steps, but with new data concepts.

The processing steps are: - Calculate NDVI - Calculate slope and relative elevation from ArcticDEM - Merge everything into a single ds.

The main difference to preprocess_legacy is the new data concept of the arcticdem. Instead of using already preprocessed arcticdem data which are loaded from a VRT, this step expects the raw arcticdem data and calculates slope and relative elevation on the fly.

Parameters:

  • ds_optical (xarray.Dataset) –

    The Planet scene optical data or Sentinel 2 scene optical dataset including data_masks.

  • ds_arcticdem (xarray.Dataset) –

    The ArcticDEM dataset.

  • ds_tcvis (xarray.Dataset) –

    The TCVIS dataset.

  • tpi_outer_radius (int, default: 100 ) –

    The outer radius of the annulus kernel for the tpi calculation in m. Defaults to 100m.

  • tpi_inner_radius (int, default: 0 ) –

    The inner radius of the annulus kernel for the tpi calculation in m. Defaults to 0.

  • device (typing.Literal['cuda', 'cpu'] | int, default: darts_utils.cuda.DEFAULT_DEVICE ) –

    The device to run the tpi and slope calculations on. If "cuda" take the first device (0), if int take the specified device. Defaults to "cuda" if cuda is available, else "cpu".

Returns:

Source code in darts-preprocessing/src/darts_preprocessing/legacy.py 
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@stopwatch("Preprocessing", printer=logger.debug)
def preprocess_legacy_fast(
    ds_optical: xr.Dataset,
    ds_arcticdem: xr.Dataset,
    ds_tcvis: xr.Dataset,
    tpi_outer_radius: int = 100,
    tpi_inner_radius: int = 0,
    device: Literal["cuda", "cpu"] | int = DEFAULT_DEVICE,
) -> xr.Dataset:
    """Preprocess optical data with legacy (DARTS v1) preprocessing steps, but with new data concepts.

    The processing steps are:
    - Calculate NDVI
    - Calculate slope and relative elevation from ArcticDEM
    - Merge everything into a single ds.

    The main difference to preprocess_legacy is the new data concept of the arcticdem.
    Instead of using already preprocessed arcticdem data which are loaded from a VRT, this step expects the raw
    arcticdem data and calculates slope and relative elevation on the fly.

    Args:
        ds_optical (xr.Dataset): The Planet scene optical data or Sentinel 2 scene optical dataset including data_masks.
        ds_arcticdem (xr.Dataset): The ArcticDEM dataset.
        ds_tcvis (xr.Dataset): The TCVIS dataset.
        tpi_outer_radius (int, optional): The outer radius of the annulus kernel for the tpi calculation
            in m. Defaults to 100m.
        tpi_inner_radius (int, optional): The inner radius of the annulus kernel for the tpi calculation
            in m. Defaults to 0.
        device (Literal["cuda", "cpu"] | int, optional): The device to run the tpi and slope calculations on.
            If "cuda" take the first device (0), if int take the specified device.
            Defaults to "cuda" if cuda is available, else "cpu".

    Returns:
        xr.Dataset: The preprocessed dataset.

    """
    # Move to GPU for faster calculations
    ds_optical = move_to_device(ds_optical, device)
    # Calculate NDVI
    ds_optical["ndvi"] = calculate_ndvi(ds_optical)
    ds_optical = move_to_host(ds_optical)

    # Reproject TCVIS to optical data
    with stopwatch("Reprojecting TCVIS", printer=logger.debug):
        ds_tcvis = ds_tcvis.odc.reproject(ds_optical.odc.geobox, resampling="cubic")

    ds_optical["tc_brightness"] = ds_tcvis.tc_brightness
    ds_optical["tc_greenness"] = ds_tcvis.tc_greenness
    ds_optical["tc_wetness"] = ds_tcvis.tc_wetness

    # Calculate TPI and slope from ArcticDEM
    with stopwatch("Reprojecting ArcticDEM", printer=logger.debug):
        ds_arcticdem = ds_arcticdem.odc.reproject(ds_optical.odc.geobox.buffered(tpi_outer_radius), resampling="cubic")
    # Move to same device as optical
    ds_arcticdem = move_to_device(ds_arcticdem, device)
    ds_arcticdem = preprocess_legacy_arcticdem_fast(ds_arcticdem, tpi_outer_radius, tpi_inner_radius)
    ds_arcticdem = move_to_host(ds_arcticdem)

    ds_arcticdem = ds_arcticdem.odc.crop(ds_optical.odc.geobox.extent)
    # For some reason, we need to reindex, because the reproject + crop of the arcticdem sometimes results
    # in floating point errors. These error are at the order of 1e-10, hence, way below millimeter precision.
    ds_arcticdem = ds_arcticdem.reindex_like(ds_optical)

    ds_optical["dem"] = ds_arcticdem.dem
    ds_optical["relative_elevation"] = ds_arcticdem.tpi
    ds_optical["slope"] = ds_arcticdem.slope
    ds_optical["arcticdem_data_mask"] = ds_arcticdem.arcticdem_data_mask

    return ds_optical