From 54ccef3daca6623be35902c53164f87243a9217c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 28 Sep 2023 18:54:39 +0000 Subject: [PATCH] Deploy dev from d60f8e0 --- dev/.buildinfo | 2 +- dev/_images/composites_2_1.png | Bin 0 -> 211807 bytes dev/_images/composites_3_0.png | Bin 643076 -> 347734 bytes dev/_images/composites_4_0.png | Bin 628980 -> 618314 bytes dev/_images/composites_5_0.png | Bin 0 -> 604220 bytes dev/_images/composites_6_0.png | Bin 0 -> 643321 bytes dev/_images/equalize-histogram_2_0.png | Bin 0 -> 349709 bytes dev/_images/equalize-histogram_3_0.png | Bin 0 -> 438276 bytes dev/_images/equalize-histogram_4_0.png | Bin 0 -> 456830 bytes dev/_images/index_0_0.png | Bin 0 -> 301708 bytes dev/_images/index_0_1.png | Bin 301286 -> 0 bytes dev/_images/indices_10_0.png | Bin 0 -> 27713 bytes dev/_images/indices_14_0.png | Bin 0 -> 691320 bytes dev/_images/indices_1_1.png | Bin 0 -> 688298 bytes dev/_images/indices_2_0.png | Bin 592985 -> 0 bytes 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      2. - Citing + Citing xlandsat
      3. @@ -221,10 +271,20 @@

        xlandsat v0.

        - Links + Community

        - Links + Community

      4. - Citing + Citing xlandsat
      5. @@ -221,10 +271,20 @@

        xlandsat v0.

        - Links + Community

      6. - Citing + Citing xlandsat
      7. @@ -221,10 +271,20 @@

        xlandsat v0.

        - Links + Community

      8. - Citing + Citing xlandsat
      9. @@ -221,10 +271,20 @@

        xlandsat v0.

        - Links + Community

      10. - Citing + Citing xlandsat
      11. @@ -224,10 +274,20 @@

        xlandsat v0.

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    @@ -503,7 +563,7 @@

    xlandsat.composite

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 28, 2023.

    diff --git a/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html b/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html index f6ea7d0..1ecb240 100644 --- a/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html +++ b/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html @@ -8,7 +8,7 @@ - xlandsat.datasets.fetch_brumadinho_after | xlandsat v0.0.post36 + xlandsat.datasets.fetch_brumadinho_after | xlandsat v0.0.post67+gd60f8e0 @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@

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  • +
  • + + xlandsat.equalize_histogram + +
  • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,11 +229,26 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

  • +
  • + + xlandsat.datasets.fetch_manaus + +
  • +
  • + + xlandsat.datasets.fetch_momotombo + +
  • +
  • + + xlandsat.datasets.fetch_roraima + +
  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

    This release contains contributions from:

    @@ -612,7 +672,7 @@

    Version 0.0.1

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 28, 2023.

    diff --git a/dev/citing.html b/dev/citing.html index 93d9628..f64397c 100644 --- a/dev/citing.html +++ b/dev/citing.html @@ -8,7 +8,7 @@ - Citing | xlandsat v0.0.post36 + Citing xlandsat | xlandsat v0.0.post67+gd60f8e0 @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@

  • +
  • + + xlandsat.equalize_histogram + +
  • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,11 +229,26 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

  • +
  • + + xlandsat.datasets.fetch_manaus + +
  • +
  • + + xlandsat.datasets.fetch_momotombo + +
  • +
  • + + xlandsat.datasets.fetch_roraima + +
  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -221,10 +271,20 @@

    xlandsat v0.

    - Links + Community

    +

    E

    + + +
    +

    F

    - + +
    + +

    I

    + +
    @@ -487,7 +571,7 @@

    X

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 28, 2023.

    diff --git a/dev/index.html b/dev/index.html index 2c4c8be..a8786b8 100644 --- a/dev/index.html +++ b/dev/index.html @@ -8,7 +8,7 @@ - xlandsat v0.0.post36 + xlandsat v0.0.post67+gd60f8e0 @@ -83,9 +83,14 @@

  • +
  • + + xlandsat.equalize_histogram + +
  • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -193,11 +228,26 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

  • +
  • + + xlandsat.datasets.fetch_manaus + +
  • +
  • + + xlandsat.datasets.fetch_momotombo + +
  • +
  • + + xlandsat.datasets.fetch_roraima + +
  • - Citing + Citing xlandsat
  • @@ -223,10 +273,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +275,20 @@

    xlandsat v0.

    - Links + Community

    -

    Now lets plot it:

    +

    Now we can do the same for the after scene:

    -
    fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    -ndvi_change.plot(ax=ax, vmin=-1, vmax=1, cmap="PuOr")
    -ax.set_aspect("equal")
    -plt.show()
    +
    ndvi_after = (after.nir - after.red) / (after.nir + after.red)
    +ndvi_after
     
    -_images/indices_4_0.png -
    -
    -

    There’s some noise in the cloudy areas of both scenes in the northeast but -otherwise this plots highlights the area affected by flooding from the dam -collapse in bright red at the center.

    +
    + + + + + + + + + + + + + + +
    <xarray.DataArray (northing: 300, easting: 400)>
    +array([[0.5073, 0.4705, 0.4907, ..., 0.515 , 0.4946, 0.4565],
    +       [0.4277, 0.478 , 0.4817, ..., 0.4949, 0.5166, 0.5474],
    +       [0.37  , 0.4304, 0.441 , ..., 0.522 , 0.5786, 0.6226],
    +       ...,
    +       [0.57  , 0.594 , 0.595 , ..., 0.328 , 0.462 , 0.54  ],
    +       [0.5845, 0.5796, 0.583 , ..., 0.4995, 0.566 , 0.572 ],
    +       [0.61  , 0.5664, 0.5938, ..., 0.609 , 0.5903, 0.605 ]],
    +      dtype=float16)
    +Coordinates:
    +  * easting   (easting) float64 5.844e+05 5.844e+05 ... 5.963e+05 5.964e+05
    +  * northing  (northing) float64 -2.232e+06 -2.232e+06 ... -2.223e+06 -2.223e+06
    +
    +

    And add some metadata for xarray to find when making plots:

    +
    +
    +
    for ndvi in [ndvi_before, ndvi_after]:
    +    ndvi.attrs["long_name"] = "normalized difference vegetation index"
    +    ndvi.attrs["units"] = "dimensionless"
    +ndvi_before.attrs["title"] = "NDVI before"
    +ndvi_after.attrs["title"] = "NDVI after"
    +
    +
    +
    +
    +
    +
    +

    Now we can make pseudo-color plots of the NDVI from before and after the +disaster:

    +
    +
    +
    fig, axes = plt.subplots(2, 1, figsize=(10, 12), layout="tight")
    +for ax, ndvi in zip(axes, [ndvi_before, ndvi_after]):
    +    # Limit the scale to [-1, +1] so the plots are easier to compare
    +    ndvi.plot(ax=ax, vmin=-1, vmax=1, cmap="RdBu_r")
    +    ax.set_title(ndvi.attrs["title"])
    +    ax.set_aspect("equal")
    +plt.show()
    +
    +
    +
    +
    +_images/indices_5_0.png +
    +
    + +
    +

    Tracking differences#

    +

    An advantage of having our data in xarray.DataArray format is that we +can perform coordinate-aware calculations. This means that taking the +difference between our two arrays will take into account the coordinates of +each pixel and only perform the operation where the coordinates align.

    +

    We can calculate the change in NDVI from one scene to the other by taking the +difference:

    +
    +
    +
    ndvi_change = ndvi_before - ndvi_after
    +
    +# Add som metadata for xarray
    +ndvi_change.name = "ndvi_change"
    +ndvi_change.attrs["long_name"] = "NDVI change"
    +ndvi_change.attrs["title"] = (
    +    f"NDVI change between {before.attrs['date_acquired']} and "
    +    f"{after.attrs['date_acquired']}"
    +)
    +ndvi_change
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.DataArray 'ndvi_change' (northing: 300, easting: 370)>
    +array([[ 0.05908 ,  0.06323 ,  0.0542  , ...,  0.004395, -0.009766,
    +         0.07324 ],
    +       [ 0.0498  ,  0.07764 ,  0.07495 , ...,  0.0957  ,  0.012695,
    +         0.003906],
    +       [-0.010254,  0.11743 ,  0.0747  , ...,  0.03125 ,  0.01807 ,
    +         0.04004 ],
    +       ...,
    +       [-0.000977,  0.01123 ,  0.000977, ...,  0.00928 ,  0.01367 ,
    +         0.00708 ],
    +       [ 0.00879 ,  0.02344 ,  0.01318 , ...,  0.006836,  0.00586 ,
    +         0.001221],
    +       [-0.01221 ,  0.02637 ,  0.006836, ...,  0.01343 ,  0.01221 ,
    +         0.0105  ]], dtype=float16)
    +Coordinates:
    +  * easting   (easting) float64 5.844e+05 5.844e+05 ... 5.954e+05 5.955e+05
    +  * northing  (northing) float64 -2.232e+06 -2.232e+06 ... -2.223e+06 -2.223e+06
    +Attributes:
    +    long_name:  NDVI change
    +    title:      NDVI change between 2019-01-14 and 2019-01-30
    +
    +
    +

    Did you notice?

    +

    The keen-eyed among you may have noticed that the number of points along +the "easting" dimension has decreased. This is because xarray +only makes the calculations for pixels where the two scenes coincide. In +this case, there was an East-West shift between scenes but our calculations +take that into account.

    +
    +

    Now lets plot the difference:

    +
    +
    +
    fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    +ndvi_change.plot(ax=ax, vmin=-1, vmax=1, cmap="PuOr")
    +ax.set_aspect("equal")
    +ax.set_title(ndvi_change.attrs["title"])
    +plt.show()
    +
    +
    +
    +
    +_images/indices_7_0.png +
    +
    +

    There’s some noise in the cloudy areas of both scenes in the northeast but +otherwise this plots highlights the area affected by flooding from the dam +collapse in purple at the center.

    +
    +
    +

    Estimating area#

    +

    One things we can do with indices and their differences in time is calculated +area estimates. If we know that the region of interest has index values +within a given value range, the area can be calculated by counting the number +of pixels within that range (a pixel in Landsat 8/9 scenes is 30 x 30 = 900 m²).

    +

    First, let’s slice our NDVI difference to just the flooded area to avoid the +effect of the clouds in North. We’ll use the xarray.DataArray.sel +method to slice using the UTM coordinates of the scene:

    +
    +
    +
    flood = ndvi_change.sel(
    +    easting=slice(587000, 594000),
    +    northing=slice(-2230000, -2225000),
    +)
    +
    +fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    +flood.plot(ax=ax, vmin=-1, vmax=1, cmap="PuOr")
    +ax.set_aspect("equal")
    +plt.show()
    +
    +
    +
    +
    +_images/indices_8_0.png +
    +
    +

    Now we can create a mask of the flood area by selecting pixels that have a high +NDVI difference. Using a > comparison (or any other logical operator in +Python), we can create a boolean (True or False) +xarray.DataArray as our mask:

    +
    +
    +
    # Threshold value determined by trial-and-error
    +flood_mask = flood > 0.3
    +
    +# Add some metadata for xarray
    +flood_mask.attrs["long_name"] = "flood mask"
    +
    +flood_mask
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.DataArray 'ndvi_change' (northing: 167, easting: 234)>
    +array([[False, False, False, ..., False, False, False],
    +       [False, False, False, ..., False, False, False],
    +       [False, False, False, ..., False, False, False],
    +       ...,
    +       [False, False, False, ..., False, False, False],
    +       [False, False, False, ..., False, False, False],
    +       [False, False, False, ..., False, False, False]])
    +Coordinates:
    +  * easting   (easting) float64 5.87e+05 5.87e+05 ... 5.94e+05 5.94e+05
    +  * northing  (northing) float64 -2.23e+06 -2.23e+06 ... -2.225e+06 -2.225e+06
    +Attributes:
    +    long_name:  flood mask
    +
    +

    Plotting boolean arrays will use 1 to represent True and 0 to represent +False:

    +
    +
    +
    fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    +flood_mask.plot(ax=ax, cmap="gray")
    +ax.set_aspect("equal")
    +ax.set_title("Flood mask")
    +plt.show()
    +
    +
    +
    +
    +_images/indices_10_0.png +
    +
    +
    +

    See also

    +

    Notice that our mask isn’t perfect. There are little bloobs classified as +flood pixels that are clearly outside the flood region. For more +sophisticated analysis, see the image segmentation methods in +scikit-image.

    +
    +

    Counting the number of True values is as easy as adding all of the boolean +values (remember that True corresponds to 1 and False to 0), which +we’ll do with xarray.DataArray.sum:

    +
    +
    +
    flood_pixels = flood_mask.sum().values
    +print(flood_pixels)
    +
    +
    +
    +
    +
    2095
    +
    +
    +
    +
    +
    +

    Note

    +

    We use .values above because sum returns an +xarray.DataArray with a single value instead of the actual number. +This is usually not a problem but it looks ugly when printed, so we grab +the number with .values.

    +
    +

    Finally, the flood area is the number of pixels multiplied by the area of each +pixel (30 x 30 m²):

    +
    +
    +
    flood_area = flood_pixels * 30**2
    +
    +print(f"Flooded area is approximately {flood_area:.0f} m²")
    +
    +
    +
    +
    +
    Flooded area is approximately 1885500 m²
    +
    +
    +
    +
    +

    Values in m² are difficult to imagine so a good way to communicate these +numbers is to put them into real-life context. In this case, we can use the +football pitches as a unit +that many people will understand:

    +
    +
    +
    flood_area_pitches = flood_area / 7140
    +
    +print(f"Flooded area is approximately {flood_area_pitches:.0f} football pitches")
    +
    +
    +
    +
    +
    Flooded area is approximately 264 football pitches
    +
    +
    +
    +
    +
    +

    Warning

    +

    This is a very rough estimate! The final value will vary greatly if you +change the threshold used to generate the mask (try it yourself). +For a more thorough analysis of the disaster using remote-sensing data, see +Silva Rotta et al. (2020).

    +
    +
    +
    +

    Other indices#

    +

    Calculating other indices will follow a very similar strategy to NDVI since +most of them only involve arithmetic operations on different bands. +As an example, let’s calculate and plot the +Modified Soil Adjusted Vegetation Index (MSAVI) +for our two scenes:

    +
    +
    +
    import numpy as np
    +
    +# This time, use a loop and put them in a list to avoid repeated code
    +msavi_collection = []
    +for scene in [before, after]:
    +    msavi = (
    +        (
    +            2 * scene.nir + 1 - np.sqrt(
    +                (2 * scene.nir + 1) * 2 - 8 * (scene.nir - scene.red)
    +            )
    +        ) / 2
    +    )
    +    msavi.name = "msavi"
    +    msavi.attrs["long_name"] = "modified soil adjusted vegetation index"
    +    msavi.attrs["units"] = "dimensionless"
    +    msavi.attrs["title"] = scene.attrs["title"]
    +    msavi_collection.append(msavi)
    +
    +# Plotting is mostly the same
    +fig, axes = plt.subplots(2, 1, figsize=(10, 12), layout="tight")
    +for ax, msavi in zip(axes, msavi_collection):
    +    msavi.plot(ax=ax, vmin=-0.5, vmax=0.5, cmap="RdBu_r")
    +    ax.set_title(msavi.attrs["title"])
    +    ax.set_aspect("equal")
    +plt.show()
    +
    +
    +
    +
    +_images/indices_14_0.png +
    +
    +

    With this same logic, you could calculate NBR and dNBR, other variants of +NDVI, NDSI, etc.

    @@ -1823,7 +3353,7 @@

    NDVI#<

    previous

    -

    Composites

    +

    Making composites

    @@ -1841,7 +3371,7 @@

    NDVI#<

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 28, 2023.

    diff --git a/dev/install.html b/dev/install.html index 6779862..14ddd83 100644 --- a/dev/install.html +++ b/dev/install.html @@ -8,7 +8,7 @@ - Installing | xlandsat v0.0.post36 + Installing | xlandsat v0.0.post67+gd60f8e0 @@ -49,7 +49,7 @@ - + @@ -84,9 +84,14 @@

  • +
  • + + xlandsat.equalize_histogram + +
  • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,11 +229,26 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

  • +
  • + + xlandsat.datasets.fetch_manaus + +
  • +
  • + + xlandsat.datasets.fetch_momotombo + +
  • +
  • + + xlandsat.datasets.fetch_roraima + +
  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

  • - Citing + Citing xlandsat
  • @@ -224,10 +274,20 @@

    xlandsat v0.

    - Links + Community

    - Links + Community

    • easting
      PandasIndex
      PandasIndex(Index([487020.0, 487050.0, 487080.0, 487110.0, 487140.0, 487170.0, 487200.0,
      +       487230.0, 487260.0, 487290.0,
      +       ...
      +       499710.0, 499740.0, 499770.0, 499800.0, 499830.0, 499860.0, 499890.0,
      +       499920.0, 499950.0, 499980.0],
      +      dtype='float64', name='easting', length=433))
    • northing
      PandasIndex
      PandasIndex(Index([5922000.0, 5922030.0, 5922060.0, 5922090.0, 5922120.0, 5922150.0,
      +       5922180.0, 5922210.0, 5922240.0, 5922270.0,
      +       ...
      +       5929710.0, 5929740.0, 5929770.0, 5929800.0, 5929830.0, 5929860.0,
      +       5929890.0, 5929920.0, 5929950.0, 5929980.0],
      +      dtype='float64', name='northing', length=267))
  • Conventions :
    CF-1.8
    title :
    Landsat 8 scene from 2020-09-27 (path/row=204/23)
    digital_object_identifier :
    https://doi.org/10.5066/P9OGBGM6
    origin :
    Image courtesy of the U.S. Geological Survey
    landsat_product_id :
    LC08_L2SP_204023_20200927_20201006_02_T1
    processing_level :
    L2SP
    collection_number :
    02
    collection_category :
    T1
    spacecraft_id :
    LANDSAT_8
    sensor_id :
    OLI_TIRS
    map_projection :
    UTM
    utm_zone :
    30
    datum :
    WGS84
    ellipsoid :
    WGS84
    date_acquired :
    2020-09-27
    scene_center_time :
    11:10:50.3140030Z
    wrs_path :
    204
    wrs_row :
    23
    mtl_file :
    GROUP = LANDSAT_METADATA_FILE GROUP = PRODUCT_CONTENTS ORIGIN = "Image courtesy of the U.S. Geological Survey" DIGITAL_OBJECT_IDENTIFIER = "https://doi.org/10.5066/P9OGBGM6" @@ -1280,7 +1340,7 @@

    Pansharpening

    And load the panchromatic band with xlandsat.load_panchromatic:

    -
    panchromatic = xls.load_panchromatic(path_pan)
    +
    panchromatic = xls.load_panchromatic(path_pan)
     panchromatic
     
    @@ -1679,7 +1739,7 @@

    Pansharpening

    scene_center_time: 11:10:50.3140030Z wrs_path: 204 wrs_row: 23 - mtl_file: GROUP = LANDSAT_METADATA_FILE\n GROUP = PROD...
    • northing
      PandasIndex
      PandasIndex(Index([5922000.0, 5922015.0, 5922030.0, 5922045.0, 5922060.0, 5922075.0,
      +       5922090.0, 5922105.0, 5922120.0, 5922135.0,
      +       ...
      +       5929860.0, 5929875.0, 5929890.0, 5929905.0, 5929920.0, 5929935.0,
      +       5929950.0, 5929965.0, 5929980.0, 5929995.0],
      +      dtype='float64', name='northing', length=534))
    • easting
      PandasIndex
      PandasIndex(Index([487005.0, 487020.0, 487035.0, 487050.0, 487065.0, 487080.0, 487095.0,
      +       487110.0, 487125.0, 487140.0,
      +       ...
      +       499860.0, 499875.0, 499890.0, 499905.0, 499920.0, 499935.0, 499950.0,
      +       499965.0, 499980.0, 499995.0],
      +      dtype='float64', name='easting', length=867))
  • Conventions :
    CF-1.8
    title :
    Pansharpend Landsat 8 scene from 2020-09-27 (path/row=204/23)
    digital_object_identifier :
    https://doi.org/10.5066/P9OGBGM6
    origin :
    Image courtesy of the U.S. Geological Survey
    landsat_product_id :
    LC08_L2SP_204023_20200927_20201006_02_T1
    processing_level :
    L2SP
    collection_number :
    02
    collection_category :
    T1
    spacecraft_id :
    LANDSAT_8
    sensor_id :
    OLI_TIRS
    map_projection :
    UTM
    utm_zone :
    30
    datum :
    WGS84
    ellipsoid :
    WGS84
    date_acquired :
    2020-09-27
    scene_center_time :
    11:10:50.3140030Z
    wrs_path :
    204
    wrs_row :
    23
    pansharpening_method :
    Weighted Brovey Transform
    pansharpening_rgb_weights :
    (1, 1, 0.2)
    pansharpening_band_filename :
    LC08_L1TP_204023_20200927_20201006_02_T1_B8.TIF
  • Finally, let’s compare the sharpened and original RGB composites:

    -
    rgb_sharp = xls.composite(scene_sharp, rescale_to=(0, 0.15))
    +
    rgb_sharp = xls.composite(scene_sharp, rescale_to=(0, 0.15))
     
     plt.figure(figsize=(16, 10))
     ax = plt.subplot(2, 1, 1)
    @@ -2491,13 +2551,13 @@ 

    Pansharpening

    previous

    -

    Indices

    +

    Working with indices

    - +

    next

    -

    List of functions and classes (API)

    +

    Filling missing values

    @@ -2509,7 +2569,7 @@

    Pansharpening

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 28, 2023.

    diff --git a/dev/plot-overlay.html b/dev/plot-overlay.html new file mode 100644 index 0000000..791ef6e --- /dev/null +++ b/dev/plot-overlay.html @@ -0,0 +1,1461 @@ + + + + + + + + + + + Plotting bands overlaid on composites | xlandsat v0.0.post67+gd60f8e0 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    + + + + + + +
    +
    + + +
    +
    +
    +
    +
    + +
    +

    Plotting bands overlaid on composites

    + +
    +
    + +
    +
    +
    +
    + +
    + +
    +

    Plotting bands overlaid on composites#

    +

    Sometimes, we may want to overlay a part of a band (for example, the thermal +band) on top of a composite. The overlay band can be partially transparent, +cropped to a range of values, or both. +As an example, let’s make a plot of the December 2015 eruption of Momotombo +volcano, Nicaragua. +We’ll overlay the thermal band (only pixels above 320 K) on top of an RGB +composite to show the ongoing lava flow.

    +

    First, we’ll import the required packages and load the sample scene:

    +
    +
    +
    import xlandsat as xls
    +import matplotlib.pyplot as plt
    +import xarray as xr
    +import numpy as np
    +
    +path = xls.datasets.fetch_momotombo()
    +scene = xls.load_scene(path)
    +# Fill the missing values due to the volcanic clouds to make it look nicer
    +scene = xls.interpolate_missing(scene)
    +scene
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + +
    <xarray.Dataset>
    +Dimensions:   (easting: 467, northing: 333)
    +Coordinates:
    +  * easting   (easting) float64 5.44e+05 5.44e+05 ... 5.58e+05 5.58e+05
    +  * northing  (northing) float64 1.369e+06 1.369e+06 ... 1.379e+06 1.379e+06
    +Data variables:
    +    blue      (northing, easting) float16 -0.05554 -0.05481 ... 0.07788 0.07727
    +    green     (northing, easting) float16 0.04834 0.05066 ... 0.1056 0.1027
    +    red       (northing, easting) float16 0.006226 0.01105 ... 0.1163 0.1045
    +    nir       (northing, easting) float16 0.01483 0.02124 ... 0.2435 0.2367
    +    swir1     (northing, easting) float16 0.06189 0.06409 ... 0.2098 0.1976
    +    swir2     (northing, easting) float16 0.06519 0.06628 ... 0.1571 0.1527
    +    thermal   (northing, easting) float16 293.5 293.5 293.8 ... 311.0 312.0
    +Attributes: (12/19)
    +    Conventions:                CF-1.8
    +    title:                      Landsat 8 scene from 2015-12-05 (path/row=17/51)
    +    digital_object_identifier:  https://doi.org/10.5066/P9OGBGM6
    +    origin:                     Image courtesy of the U.S. Geological Survey
    +    landsat_product_id:         LC08_L2SP_017051_20151205_20200908_02_T1
    +    processing_level:           L2SP
    +    ...                         ...
    +    ellipsoid:                  WGS84
    +    date_acquired:              2015-12-05
    +    scene_center_time:          16:06:06.8773380Z
    +    wrs_path:                   17
    +    wrs_row:                    51
    +    mtl_file:                   GROUP = LANDSAT_METADATA_FILE\n  GROUP = PROD...
    +
    +

    Now we can plot an RGB composite and thermal band separately to see that they +have to show:

    +
    +
    +
    # Make the composite
    +rgb = xls.composite(scene, rescale_to=(0, 0.6))
    +
    +# Histogram equalization for a better looking image
    +rgb = xls.equalize_histogram(rgb, clip_limit=0.02, kernel_size=200)
    +
    +# Plot the RGB and thermal separately
    +fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
    +
    +rgb.plot.imshow(ax=ax1)
    +scene.thermal.plot.imshow(ax=ax2, cmap="magma")
    +
    +ax1.set_aspect("equal")
    +ax2.set_aspect("equal")
    +
    +plt.show()
    +
    +
    +
    +
    +_images/plot-overlay_1_0.png +
    +
    +

    Notice that the lava flow is clearly visible as temperatures above 320 K in the +thermal band but it’s difficult to see where the volcano and other landmarks +are. Looking at the RGB composite, we can’t really make out the lava flow but +we have a clear picture of where the volcano is and where the old lava flows +are. A way to show the thermal data with the geographic context of the RGB is +to overlay the two in a single plot.

    +

    To do so, we’ll first create a version of the thermal band that has all pixels +with temperature below 320 K set to NaN (not-a-number). This is used to +indicate to matplotlib that a pixel should be transparent. An easy way to do +this is with the xarray.where function:

    +
    +
    +
    # If the condition is true, use the thermal values. If it's false, use nan
    +lava = xr.where(scene.thermal >= 320, scene.thermal, np.nan, keep_attrs=True)
    +
    +fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    +
    +lava.plot.imshow(ax=ax, cmap="magma")
    +
    +ax.set_aspect("equal")
    +plt.show()
    +
    +
    +
    +
    +_images/plot-overlay_2_0.png +
    +
    +
    +

    Note

    +

    We used the keep_attrs=True parameter to tell xarray that it should +keep the metadata from the original band in the lava-only version. This +will preserve the information on units, procedence, etc. But be careful +with this since it can lead to metadata being propagated when it’s no +longer valid.

    +
    +

    Now that we have an xarray.DataArray with the lava flow only, we can +plot that on top of the RGB composite and add a bit of transparency using the +alpha parameter of imshow.

    +
    +
    +
    fig, ax = plt.subplots(1, 1, figsize=(10, 6))
    +
    +# RGB goes first so it's at the bottom
    +rgb.plot.imshow(ax=ax)
    +lava.plot.imshow(ax=ax, cmap="magma", alpha=0.6)
    +
    +ax.set_aspect("equal")
    +plt.show()
    +
    +
    +
    +
    +_images/plot-overlay_3_0.png +
    +
    +

    With the plot above, all of the information we have available about the lava +flow is displayed in a nice format.

    +
    + + +
    + +
    + +
    +
    + + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/dev/py-modindex.html b/dev/py-modindex.html index c488e52..e044278 100644 --- a/dev/py-modindex.html +++ b/dev/py-modindex.html @@ -7,7 +7,7 @@ - Python Module Index | xlandsat v0.0.post36 + Python Module Index | xlandsat v0.0.post67+gd60f8e0 @@ -84,9 +84,14 @@ diff --git a/dev/references.html b/dev/references.html index f32c617..56d8724 100644 --- a/dev/references.html +++ b/dev/references.html @@ -8,7 +8,7 @@ - References | xlandsat v0.0.post36 + References | xlandsat v0.0.post67+gd60f8e0 @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@
    diff --git a/dev/search.html b/dev/search.html index 4d23128..e9f4eed 100644 --- a/dev/search.html +++ b/dev/search.html @@ -7,7 +7,7 @@ - Search | xlandsat v0.0.post36 + Search | xlandsat v0.0.post67+gd60f8e0 @@ -87,9 +87,14 @@ diff --git a/dev/searchindex.js b/dev/searchindex.js index 0b2abc8..4e4f750 100644 --- a/dev/searchindex.js +++ b/dev/searchindex.js @@ -1 +1 @@ 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of functions and classes (API)","Changelog","Citing xlandsat","Version compatibility","Making composites","Histogram equalization","Home","Working with indices","Installing","Loading and cropping scenes","Filling missing values","Overview","Pansharpening","Plotting bands overlaid on composites","References","Documentation for other versions"],titleterms:{"0":15,"1":15,"2":15,"3":15,"class":14,"function":14,The:[23,25],aka:18,api:14,archiv:23,area:21,band:[23,25,27],chang:20,changelog:15,cite:16,color:18,compat:17,composit:[0,18,27],contrast:18,crop:23,dataset:[1,2,3,4,5,6,7,14],depend:[17,22],did:21,differ:21,document:29,download:[23,25],earthexplor:23,equal:19,equalize_histogram:8,estim:21,fetch_brumadinho_aft:1,fetch_brumadinho_befor:2,fetch_liverpool:3,fetch_liverpool_panchromat:4,fetch_manau:5,fetch_momotombo:6,fetch_roraima:7,fill:24,folder:23,from:23,histogram:19,indic:21,infrar:18,input:14,instal:22,intens:18,interpolate_miss:9,larg:20,librari:25,list:14,load:[23,25],load_panchromat:10,load_scen:11,look:20,make:18,metadata:23,miss:24,ndvi:21,notic:21,now:25,onli:23,other:[18,20,21,29],output:14,overlaid:27,overview:25,panchromat:23,pansharpen:[12,26],plot:[18,25,27],process:[14,20],python:[17,22],readi:20,refer:28,reflect:25,rescal:18,rgb:18,sampl:[14,25],satellit:20,save_scen:13,scale:20,scene:[23,25],segment:23,select:23,some:25,still:20,stretch:18,support:17,tar:23,track:21,transpar:18,trigger:21,us:[20,25],valu:24,version:[15,17,29],warn:21,what:25,which:[22,23],why:25,work:21,xlandsat:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,16,17,20,25],you:21}}) \ No newline at end of file diff --git a/dev/versions.html b/dev/versions.html index a3a04a4..f302a70 100644 --- a/dev/versions.html +++ b/dev/versions.html @@ -8,7 +8,7 @@ - Documentation for other versions | xlandsat v0.0.post36 + Documentation for other versions | xlandsat v0.0.post67+gd60f8e0 @@ -83,9 +83,14 @@