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+ + + + + -

xlandsat v0.0.post36

+

xlandsat v0.0.post58+geb1d62a
@@ -129,6 +134,21 @@

xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/_modules/xlandsat/_composite.html b/dev/_modules/xlandsat/_composite.html index a1e1997..c81325b 100644 --- a/dev/_modules/xlandsat/_composite.html +++ b/dev/_modules/xlandsat/_composite.html @@ -7,7 +7,7 @@ - xlandsat._composite | xlandsat v0.0.post36 + xlandsat._composite | xlandsat v0.0.post58+geb1d62a @@ -81,9 +81,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -129,6 +134,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    • @@ -573,7 +728,7 @@

    Source code for xlandsat.datasets
    diff --git a/dev/_sources/api/generated/xlandsat.datasets.fetch_manaus.rst.txt b/dev/_sources/api/generated/xlandsat.datasets.fetch_manaus.rst.txt new file mode 100644 index 0000000..9032988 --- /dev/null +++ b/dev/_sources/api/generated/xlandsat.datasets.fetch_manaus.rst.txt @@ -0,0 +1,10 @@ +xlandsat.datasets.fetch\_manaus +=============================== + +.. currentmodule:: xlandsat.datasets + +.. autofunction:: fetch_manaus + +.. raw:: html + +
    diff --git a/dev/_sources/api/generated/xlandsat.datasets.fetch_momotombo.rst.txt b/dev/_sources/api/generated/xlandsat.datasets.fetch_momotombo.rst.txt new file mode 100644 index 0000000..9965b21 --- /dev/null +++ b/dev/_sources/api/generated/xlandsat.datasets.fetch_momotombo.rst.txt @@ -0,0 +1,10 @@ +xlandsat.datasets.fetch\_momotombo +================================== + +.. currentmodule:: xlandsat.datasets + +.. autofunction:: fetch_momotombo + +.. raw:: html + +
    diff --git a/dev/_sources/api/generated/xlandsat.datasets.fetch_roraima.rst.txt b/dev/_sources/api/generated/xlandsat.datasets.fetch_roraima.rst.txt new file mode 100644 index 0000000..404832f --- /dev/null +++ b/dev/_sources/api/generated/xlandsat.datasets.fetch_roraima.rst.txt @@ -0,0 +1,10 @@ +xlandsat.datasets.fetch\_roraima +================================ + +.. currentmodule:: xlandsat.datasets + +.. autofunction:: fetch_roraima + +.. raw:: html + +
    diff --git a/dev/_sources/api/generated/xlandsat.equalize_histogram.rst.txt b/dev/_sources/api/generated/xlandsat.equalize_histogram.rst.txt new file mode 100644 index 0000000..5e55682 --- /dev/null +++ b/dev/_sources/api/generated/xlandsat.equalize_histogram.rst.txt @@ -0,0 +1,10 @@ +xlandsat.equalize\_histogram +============================ + +.. currentmodule:: xlandsat + +.. autofunction:: equalize_histogram + +.. raw:: html + +
    diff --git a/dev/_sources/api/generated/xlandsat.interpolate_missing.rst.txt b/dev/_sources/api/generated/xlandsat.interpolate_missing.rst.txt new file mode 100644 index 0000000..03475e1 --- /dev/null +++ b/dev/_sources/api/generated/xlandsat.interpolate_missing.rst.txt @@ -0,0 +1,10 @@ +xlandsat.interpolate\_missing +============================= + +.. currentmodule:: xlandsat + +.. autofunction:: interpolate_missing + +.. raw:: html + +
    diff --git a/dev/_sources/api/index.rst.txt b/dev/_sources/api/index.rst.txt index 59a1537..c89f923 100644 --- a/dev/_sources/api/index.rst.txt +++ b/dev/_sources/api/index.rst.txt @@ -25,6 +25,8 @@ Processing composite pansharpen + equalize_histogram + interpolate_missing Sample datasets --------------- @@ -40,3 +42,6 @@ Sample datasets datasets.fetch_brumadinho_before datasets.fetch_liverpool datasets.fetch_liverpool_panchromatic + datasets.fetch_manaus + datasets.fetch_momotombo + datasets.fetch_roraima diff --git a/dev/_sources/composites.rst.txt b/dev/_sources/composites.rst.txt index 44ad4db..df1cec7 100644 --- a/dev/_sources/composites.rst.txt +++ b/dev/_sources/composites.rst.txt @@ -8,7 +8,7 @@ images to visualize information from multiple bands at once. For that, we have to create **composites**. We provide the :func:`xlandsat.composite` function to make this process easier. -As an example, let's load two example scenes from the +As an example, let's load an example scene from the `Brumadinho tailings dam disaster `__: .. jupyter-execute:: @@ -16,30 +16,25 @@ As an example, let's load two example scenes from the import xlandsat as xls import matplotlib.pyplot as plt - path_before = xls.datasets.fetch_brumadinho_before() - path_after = xls.datasets.fetch_brumadinho_after() + path = xls.datasets.fetch_brumadinho_after() - before = xls.load_scene(path_before) - after = xls.load_scene(path_after) + scene = xls.load_scene(path) -Creating composites -------------------- +RGB composites +-------------- -Let's make both RGB (true color) and CIR (color infrared) composites for both -of our scenes: +Let's make an RGB (true color) composite since that is the most fundamental +type and it allows us to get a good handle on what we're seeing in the scene. +The RGB composite is also the default made by :func:`xlandsat.composite` if the +bands aren't specified. .. jupyter-execute:: - # Make the composite and add it as a variable to the scene - before = before.assign(rgb=xls.composite(before, rescale_to=[0.03, 0.2])) - cir_bands = ("nir", "red", "green") - before = before.assign( - cir=xls.composite(before, bands=cir_bands, rescale_to=[0, 0.4]), - ) - before + rgb = xls.composite(scene) + rgb -The composites have a similar layout as the bands but with an extra +The composite has a similar layout as the bands of a scene but with an extra ``"channel"`` dimension corresponding to red, green, blue, and alpha/transparency. The values are scaled to the [0, 255] range and the composite is an array of unsigned 8-bit integers. @@ -53,55 +48,81 @@ composite is an array of unsigned 8-bit integers. blue). -Now do the same for the after scene: - -.. jupyter-execute:: - - after = after.assign(rgb=xls.composite(after, rescale_to=[0.03, 0.2])) - after = after.assign( - cir=xls.composite(after, bands=cir_bands, rescale_to=[0, 0.4]), - ) - after - - -Plotting composites -------------------- +Plotting a composite +-------------------- Composites can be plotted using :meth:`xarray.DataArray.plot.imshow` (using :meth:`~xarray.DataArray.plot` won't work and will display histograms instead). -Let's make the before and after figures again for each of the composites we -generated. .. jupyter-execute:: - fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12)) + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) - # Plot the composites - before.rgb.plot.imshow(ax=ax1) - after.rgb.plot.imshow(ax=ax2) + rgb.plot.imshow(ax=ax) # The "long_name" of the composite is the band combination - ax1.set_title(f"Before: {before.rgb.attrs['long_name']}") - ax2.set_title(f"After: {after.rgb.attrs['long_name']}") + ax.set_title(f"Composite: {rgb.attrs['long_name']}") - ax1.set_aspect("equal") - ax2.set_aspect("equal") + # Make sure pixels are square and don't have any distortions from plotting + ax.set_aspect("equal") plt.show() -And now the CIR composites: +Well, this looks bad because that bright cloud is making it so the ground +pixels have only a small share of the full range of available values. This can +be mitigated by rescaling the intensity of the image to a smaller range of +reflectance values. + + +Rescaling intensity (AKA contrast stretching) +--------------------------------------------- + +We rescale the intensities of a composite to a given reflectance range by +setting the ``rescale_to`` parameter when creating a composite. It takes a list +of the min and max reflectance values allowed. For this image, we can arrive at +the following values by trial and error until it looks nice: .. jupyter-execute:: - fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12)) + rgb = xls.composite(scene, rescale_to=[0.03, 0.2]) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + rgb.plot.imshow(ax=ax) + ax.set_title(f"Rescaled composite: {rgb.attrs['long_name']}") + ax.set_aspect("equal") + plt.show() + +Notice that we can more clearly see the colors of the ground but we lose a lot +of detail in the clouds. + +.. note:: - before.cir.plot.imshow(ax=ax1) - after.cir.plot.imshow(ax=ax2) + The rescaling has to be done when creating the composite so that we can use + min/max values in reflectance units. After a composite is created, the + original range of values is lost and we'd have to specify the min/max + between 0 and 255 instead. - ax1.set_title(f"Before: {before.cir.attrs['long_name']}") - ax2.set_title(f"After: {after.cir.attrs['long_name']}") - ax1.set_aspect("equal") - ax2.set_aspect("equal") +Color infrared composites +------------------------- +Another common type of composite is the color infrared (CIR) composites. These +change the bands used to NIR, red, and green and serve primarily to distinguish +healthy vegetation from other objects in the scene. Let's make one by specifying +this band combination to :func:`xlandsat.composite` to see if we can more clearly spot the dam flood. + +.. jupyter-execute:: + + cir = xls.composite(scene, bands=("nir", "red", "green"), rescale_to=[0, 0.4]) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + cir.plot.imshow(ax=ax) + ax.set_title(f"Composite: {rgb.attrs['long_name']}") + ax.set_aspect("equal") plt.show() + +The flood region can be clearly spotted in the image above as the brown/gray +blog in the center. + +**With this, you can now make composites using any other band combination you +may want!** diff --git a/dev/_sources/equalize-histogram.rst.txt b/dev/_sources/equalize-histogram.rst.txt new file mode 100644 index 0000000..0da48a2 --- /dev/null +++ b/dev/_sources/equalize-histogram.rst.txt @@ -0,0 +1,86 @@ +.. _equalize-histogram: + +Histogram equalization +====================== + +Scenes with very dark or very bright spots (like clouds) can be difficult to +visualize without some sort of contrast enhancement when generating composites. +The simplest enhancement is to stretch the contrast linearly, but doing so +erases information in the very dark/light regions and won't always work. An +alternative is to use histogram equalization, which is implemented in +:func:`xlandsat.equalize_histogram`. + +Let's use our sample scene of October 2015 around `Mount Roraima +`__ to demonstrate how it's done. +The *tepui*, as it's called, is famous for it's near constant cloud coverage an +will make a good target for this example. + +First, we'll import the required packages and load the sample scene: + +.. jupyter-execute:: + + import xlandsat as xls + import matplotlib.pyplot as plt + import xarray as xr + import numpy as np + + path = xls.datasets.fetch_roraima() + scene = xls.load_scene(path) + +Histogram equalization doesn't work well with missing data, which this dataset +has, and so we need to first fill in the missing values through interpolation +with :func:`xlandsat.interpolate_missing`: + +.. jupyter-execute:: + + scene = xls.interpolate_missing(scene) + +Once that's done, we can make an RGB composite with no enhancements as a basis +for comparison: + +.. jupyter-execute:: + + rgb = xls.composite(scene) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + rgb.plot.imshow(ax=ax) + ax.set_aspect("equal") + plt.show() + +Notice how the clouds dominate the intensity range, making it difficult to make +out features of the *tepui* and the surrounding forest. + +Now we can do our best to stretch the contrast so that we can see more detail +in the cloud-free regions: + +.. jupyter-execute:: + + rgb_strech = xls.composite(scene, rescale_to=(0, 0.28)) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + rgb_strech.plot.imshow(ax=ax) + ax.set_aspect("equal") + plt.show() + +But, as we mentioned earlier, this means we don't get to see details of the +clouds anymore. For a more pleasing image, we can use the adaptive histogram +equalization in :func:`xlandsat.equalize_histogram`. It helps to do a bit of +contrast stretching first, but to a lesser degree than we did previously. + +.. jupyter-execute:: + + rgb = xls.composite(scene, rescale_to=(0, 0.6)) + rgb_eq = xls.equalize_histogram(rgb, clip_limit=0.02, kernel_size=300) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + rgb_eq.plot.imshow(ax=ax) + ax.set_aspect("equal") + plt.show() + +Now that's a much better visualization, we can see details in the clouds, +mountains, and forests! + +.. note:: + + Notice that :func:`xlandsat.equalize_histogram` must be **given a + composite** instead of the scene. diff --git a/dev/_sources/index.rst.txt b/dev/_sources/index.rst.txt index 1f11cc6..58b573a 100644 --- a/dev/_sources/index.rst.txt +++ b/dev/_sources/index.rst.txt @@ -30,21 +30,25 @@ Here's a quick example: .. jupyter-execute:: import xlandsat as xls + import matplotlib.pyplot as plt - # Download a cropped Landsat 8 scene from the Brumadinho dam disaster - # (Brazil). The function downloads it and returns the path to the .tar file - # containing the scene. - path = xls.datasets.fetch_brumadinho_after() + # Download a sample Landsat 9 scene in EarthExplorer format + path_to_scene_file = xls.datasets.fetch_manaus() - # Load the scene directly from the archive (no need to unpack it) - scene = xls.load_scene(path) + # Load the data from the file into an xarray.Dataset + scene = xls.load_scene(path_to_scene_file) - # Make an RGB composite and stretch the contrast - rgb = xls.composite(scene, rescale_to=[0.03, 0.2]) + # Make an RGB composite as an xarray.DataArray + rgb = xls.composite(scene, rescale_to=[0.02, 0.2]) - # Plot the composite + # Plot the composite using xarray's plotting machinery rgb.plot.imshow() + # Annotate the plot with the rich metadata xlandsat adds to the scene + plt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}") + plt.axis("scaled") + plt.show() + ---- @@ -112,8 +116,8 @@ Here's a quick example: .. note:: - Only **Landsat 8 and 9 Collection 2 Level 2 data products** are supported at - the moment. + Only **Landsat 8 and 9 Level 1 & 2 data products** have been tested at the + moment. .. admonition:: xlandsat is ready for use but still changing :class: important @@ -130,10 +134,12 @@ Here's a quick example: .. admonition:: Looking for large-scale cloud-based processing? :class: seealso - Our goal is not to provide a solution for large-scale data processing. The - target is smaller scale analysis done on individual computers (which is - probably the main way EarthExplorer is used). For cloud-based data - processing, see the `Pangeo Project `__. + Our goal is **not** to provide a solution for large-scale data processing. + Our target is smaller scale analysis done on individual computers (which is + probably the main way EarthExplorer is used). + + * For cloud-based data processing, see the `Pangeo Project `__. + * For other satellites and more powerful features, use `Satpy `__. .. toctree:: @@ -152,6 +158,9 @@ Here's a quick example: composites.rst indices.rst pansharpen.rst + missing-values.rst + equalize-histogram.rst + plot-overlay.rst .. toctree:: :maxdepth: 2 @@ -168,7 +177,9 @@ Here's a quick example: .. toctree:: :maxdepth: 2 :hidden: - :caption: Links + :caption: Community + How to contribute + Code of Conduct Source code on GitHub Computer-Oriented Geoscience Lab diff --git a/dev/_sources/indices.rst.txt b/dev/_sources/indices.rst.txt index 91f0334..818b2f0 100644 --- a/dev/_sources/indices.rst.txt +++ b/dev/_sources/indices.rst.txt @@ -8,13 +8,10 @@ quantitatively analyze these data. They take advantage of different spectral properties of materials to differentiate between them. Many of these indices can be calculated with simple arithmetic operations. -So now that our data are in :class:`xarray.Dataset`'s it's fairly easy to +So now that our data are in :class:`xarray.Dataset`'s, it's fairly easy to calculate them. -NDVI ----- - -As an example, let's load two example scenes from the +As an example, let's load two example scenes from before and after the `Brumadinho tailings dam disaster `__: .. jupyter-execute:: @@ -29,6 +26,9 @@ As an example, let's load two example scenes from the after = xls.load_scene(path_after) +NDVI +---- + We can calculate the `NDVI `__ for these scenes to see if we can isolate the effect of the flood following the @@ -37,20 +37,16 @@ dam collapse: .. jupyter-execute:: - before = before.assign( - ndvi=(before.nir - before.red) / (before.nir + before.red), - ) - after = after.assign( - ndvi=(after.nir - after.red) / (after.nir + after.red), - ) + ndvi_before = (before.nir - before.red) / (before.nir + before.red) + ndvi_after = (after.nir - after.red) / (after.nir + after.red) # Set some metadata for xarray to find - before.ndvi.attrs["long_name"] = "normalized difference vegetation index" - before.ndvi.attrs["units"] = "dimensionless" - after.ndvi.attrs["long_name"] = "normalized difference vegetation index" - after.ndvi.attrs["units"] = "dimensionless" + ndvi_before.attrs["long_name"] = "normalized difference vegetation index" + ndvi_before.attrs["units"] = "dimensionless" + ndvi_after.attrs["long_name"] = "normalized difference vegetation index" + ndvi_after.attrs["units"] = "dimensionless" - after + ndvi_before And now we can make pseudo-color plots of the NDVI: @@ -59,8 +55,8 @@ And now we can make pseudo-color plots of the NDVI: fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12)) # Limit the scale to [-1, +1] so the plots are easier to compare - before.ndvi.plot(ax=ax1, vmin=-1, vmax=1, cmap="RdBu_r") - after.ndvi.plot(ax=ax2, vmin=-1, vmax=1, cmap="RdBu_r") + ndvi_before.plot(ax=ax1, vmin=-1, vmax=1, cmap="RdBu_r") + ndvi_after.plot(ax=ax2, vmin=-1, vmax=1, cmap="RdBu_r") ax1.set_title(f"Before: {before.attrs['title']}") ax2.set_title(f"After: {after.attrs['title']}") @@ -75,7 +71,7 @@ taking the difference: .. jupyter-execute:: - ndvi_change = before.ndvi - after.ndvi + ndvi_change = ndvi_before - ndvi_after ndvi_change.name = "ndvi_change" ndvi_change.attrs["long_name"] = ( f"NDVI change between {before.attrs['date_acquired']} and " @@ -104,4 +100,4 @@ Now lets plot it: 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. +collapse in purple at the center. diff --git a/dev/_sources/missing-values.rst.txt b/dev/_sources/missing-values.rst.txt new file mode 100644 index 0000000..b9df8d3 --- /dev/null +++ b/dev/_sources/missing-values.rst.txt @@ -0,0 +1,69 @@ +.. _missing-values: + +Filling missing values +====================== + +Landsat Level 2 data can sometimes contain missing values, particularly around +bright clouds with dark shadows. These pixels will have a value of +``numpy.nan`` and can cause problems in other calculations. To fill them, we +can use the values of neighboring pixels to interpolate the missing values with +:func:`xlandsat.interpolate_missing`. + +Let's use our sample scene of the December 2015 eruption of `Momotombo volcano +`__, Nicaragua, to demonstrate how +it's done. + +First, we'll import the required packages and load the sample scene: + +.. jupyter-execute:: + + 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) + +Now we can plot an RGB composite to show some of the missing values. In order +to highlight them, we'll color the background of the plot in magenta so that +the missing values don't simply show up as white: + +.. jupyter-execute:: + + # Make the composite and add it to the scene + rgb = xls.composite(scene, rescale_to=(0.04, 0.17)) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + + rgb.plot.imshow(ax=ax) + + ax.set_facecolor("magenta") + ax.set_aspect("equal") + + plt.show() + +We can fill these values with reasonable estimates using interpolation: + +.. jupyter-execute:: + + scene_filled = xls.interpolate_missing(scene) + + rgb_filled = xls.composite(scene_filled, rescale_to=(0.04, 0.17)) + + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + + rgb_filled.plot.imshow(ax=ax) + + ax.set_facecolor("magenta") + ax.set_aspect("equal") + + plt.show() + +The interpolated scene no longer contains the magenta patches! + +.. warning:: + + This method works well when the missing data are only a few pixels or small + patches. Large portions of the image missing cannot be filled in accurately + by interpolation. diff --git a/dev/_sources/plot-overlay.rst.txt b/dev/_sources/plot-overlay.rst.txt new file mode 100644 index 0000000..a88d2d6 --- /dev/null +++ b/dev/_sources/plot-overlay.rst.txt @@ -0,0 +1,100 @@ +.. _plot-overlay: + +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: + +.. jupyter-execute:: + + 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 + +Now we can plot an RGB composite and thermal band separately to see that they +have to show: + +.. jupyter-execute:: + + # 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() + +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 :func:`xarray.where` function: + +.. jupyter-execute:: + + # 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() + + +.. 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 :class:`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``. + +.. jupyter-execute:: + + 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() + +With the plot above, all of the information we have available about the lava +flow is displayed in a nice format. diff --git a/dev/_static/copybutton.js b/dev/_static/copybutton.js index 02c5c82..2ea7ff3 100644 --- a/dev/_static/copybutton.js +++ b/dev/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', + 'copy_to_clipboard': 'Copier dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -224,7 +224,7 @@ var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); // get filtered text - let exclude = '.linenos, .gp'; + let exclude = '.linenos'; let text = filterText(target, exclude); return formatCopyText(text, '', false, true, true, true, '', '') diff --git a/dev/_static/favicon.png b/dev/_static/favicon.png index 3b9494d..08e297f 100644 Binary files a/dev/_static/favicon.png and b/dev/_static/favicon.png differ diff --git a/dev/_static/logo.png b/dev/_static/logo.png new file mode 100644 index 0000000..e1bce55 Binary files /dev/null and b/dev/_static/logo.png differ diff --git a/dev/_static/logo.svg b/dev/_static/logo.svg new file mode 100644 index 0000000..5049062 --- /dev/null +++ b/dev/_static/logo.svg @@ -0,0 +1,286 @@ + + + + diff --git a/dev/_static/readme-example.jpg b/dev/_static/readme-example.jpg index fcd52f5..9adf9b8 100644 Binary files a/dev/_static/readme-example.jpg and b/dev/_static/readme-example.jpg differ diff --git a/dev/api/generated/xlandsat.composite.html b/dev/api/generated/xlandsat.composite.html index aee0159..057acc3 100644 --- a/dev/api/generated/xlandsat.composite.html +++ b/dev/api/generated/xlandsat.composite.html @@ -8,7 +8,7 @@ - xlandsat.composite | xlandsat v0.0.post36 + xlandsat.composite | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html b/dev/api/generated/xlandsat.datasets.fetch_brumadinho_after.html index f6ea7d0..47cb018 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.post58+geb1d62a @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/api/generated/xlandsat.datasets.fetch_brumadinho_before.html b/dev/api/generated/xlandsat.datasets.fetch_brumadinho_before.html index 911de1c..2599233 100644 --- a/dev/api/generated/xlandsat.datasets.fetch_brumadinho_before.html +++ b/dev/api/generated/xlandsat.datasets.fetch_brumadinho_before.html @@ -8,7 +8,7 @@ - xlandsat.datasets.fetch_brumadinho_before | xlandsat v0.0.post36 + xlandsat.datasets.fetch_brumadinho_before | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/api/generated/xlandsat.datasets.fetch_liverpool.html b/dev/api/generated/xlandsat.datasets.fetch_liverpool.html index 5a4aff2..8d94375 100644 --- a/dev/api/generated/xlandsat.datasets.fetch_liverpool.html +++ b/dev/api/generated/xlandsat.datasets.fetch_liverpool.html @@ -8,7 +8,7 @@ - xlandsat.datasets.fetch_liverpool | xlandsat v0.0.post36 + xlandsat.datasets.fetch_liverpool | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/api/generated/xlandsat.datasets.fetch_liverpool_panchromatic.html b/dev/api/generated/xlandsat.datasets.fetch_liverpool_panchromatic.html index 84bf3ab..5a92d8c 100644 --- a/dev/api/generated/xlandsat.datasets.fetch_liverpool_panchromatic.html +++ b/dev/api/generated/xlandsat.datasets.fetch_liverpool_panchromatic.html @@ -8,7 +8,7 @@ - xlandsat.datasets.fetch_liverpool_panchromatic | xlandsat v0.0.post36 + xlandsat.datasets.fetch_liverpool_panchromatic | xlandsat v0.0.post58+geb1d62a @@ -49,7 +49,7 @@ - + @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • - +

    next

    -

    Citing

    +

    xlandsat.datasets.fetch_manaus

     @@ -491,7 +546,7 @@

    xlandsat.datasets.fetch_liverpool_panchromatic

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    diff --git a/dev/api/generated/xlandsat.datasets.fetch_manaus.html b/dev/api/generated/xlandsat.datasets.fetch_manaus.html new file mode 100644 index 0000000..495d34b --- /dev/null +++ b/dev/api/generated/xlandsat.datasets.fetch_manaus.html @@ -0,0 +1,566 @@ + + + + + + + + + + + xlandsat.datasets.fetch_manaus | xlandsat v0.0.post58+geb1d62a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    xlandsat.datasets.fetch_manaus

    + +
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    xlandsat.datasets.fetch_manaus#

    +
    +
    +xlandsat.datasets.fetch_manaus(untar=False)[source]#
    +

    Download a sample scene from Manaus, Brazil

    +

    Manaus is located in the Brazilian Amazon. The scene shows a part of the +city and the meeting of the Amazon and Negro rivers.

    +

    This is a cropped version of a Landsat 9 scene from 2023/07/23, during +the annual Amazon river floods.

    +

    The scene was downloaded from USGS Earth Explorer. Original data are in the public +domain and are redistributed here in accordance with the Landsat Data +Distribution Policy.

    +

    Source: https://doi.org/10.6084/m9.figshare.24167235.v1 +(CC0)

    +
    +
    Parameters
    +

    untar (bool) – If True, unpack the tar archive after downloading and return a path to +the folder containing the unpacked files instead. Default is False.

    +
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    Returns
    +

    path (str) – The path to the downloaded .tar file that contains the scene.

    +
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    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
    +

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    xlandsat.datasets.fetch_momotombo

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    xlandsat.datasets.fetch_momotombo#

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    +xlandsat.datasets.fetch_momotombo(untar=False)[source]#
    +

    Download a sample scene from the December 2015 Momotombo volcano eruption

    +

    This is a cropped version of a Landsat 8 scene from 2015/12/05. It was +taken during the December 2015 eruption of Momotombo volcano, Nicaragua.

    +

    The scene was downloaded from USGS Earth Explorer. Original data are in the public +domain and are redistributed here in accordance with the Landsat Data +Distribution Policy.

    +

    Source: https://doi.org/10.6084/m9.figshare.21931089.v3 +(CC0)

    +
    +
    Parameters
    +

    untar (bool) – If True, unpack the tar archive after downloading and return a path to +the folder containing the unpacked files instead. Default is False.

    +
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    Returns
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    path (str) – The path to the downloaded .tar file that contains the scene.

    +
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    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
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    xlandsat.datasets.fetch_roraima

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

    xlandsat.datasets.fetch_roraima#

    +
    +
    +xlandsat.datasets.fetch_roraima(untar=False)[source]#
    +

    Download a sample scene from Mount Roraima surrounded by clouds

    +

    Roraima is a tepui located in the junction of Brazil, Guyana, and +Venezuela. It’s famous for the near-constant cloud coverage.

    +

    This is a cropped version of a Landsat 8 scene from 2015/10/04, which is +one of the rare relatively cloud-free scenes available.

    +

    The scene was downloaded from USGS Earth Explorer. Original data are in the public +domain and are redistributed here in accordance with the Landsat Data +Distribution Policy.

    +

    Source: https://doi.org/10.6084/m9.figshare.24143622.v1 +(CC0)

    +
    +
    Parameters
    +

    untar (bool) – If True, unpack the tar archive after downloading and return a path to +the folder containing the unpacked files instead. Default is False.

    +
    +
    Returns
    +

    path (str) – The path to the downloaded .tar file that contains the scene.

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

    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/dev/api/generated/xlandsat.equalize_histogram.html b/dev/api/generated/xlandsat.equalize_histogram.html new file mode 100644 index 0000000..1458e9e --- /dev/null +++ b/dev/api/generated/xlandsat.equalize_histogram.html @@ -0,0 +1,575 @@ + + + + + + + + + + + xlandsat.equalize_histogram | xlandsat v0.0.post58+geb1d62a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
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    + + + + + + + + + + +
    + +
    + +
    + + + + +
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    + + + + +
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    + + +
    + +
    + +
    +
    + +
    + +
    + +
    +
    + +
    +
    + + +
    +
    +
    +
    +
    + +
    +

    xlandsat.equalize_histogram

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

    xlandsat.equalize_histogram#

    +
    +
    +xlandsat.equalize_histogram(composite, kernel_size=None, clip_limit=0.01)[source]#
    +

    Adaptive histogram equalization on each band of a composite

    +

    Use this function to enhance the contrast of a composite when there are a +few very dark or very light patches that dominate the color range. Use this +instead of rescaling intensity (contrast stretching) to try to preserve +some detail in the light/dark patches.

    +

    This is a wrapper around skimage.exposure.equalize_adapthist.

    +
    +

    Tip

    +

    Results can be very bad if there are missing values in the scene. Use +xlandsat.interpolate_missing first if that is the case.

    +
    +

    If the composite has an alpha channel (transparency), it will be copied to +the output intact.

    +
    +
    Parameters
    +
    +
    +
    Returns
    +

    equalized_composite (xarray.DataArray) – The composite after equalization, scaled back to unsigned 8-bit integer +range.

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

    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/dev/api/generated/xlandsat.interpolate_missing.html b/dev/api/generated/xlandsat.interpolate_missing.html new file mode 100644 index 0000000..ad1eafd --- /dev/null +++ b/dev/api/generated/xlandsat.interpolate_missing.html @@ -0,0 +1,567 @@ + + + + + + + + + + + xlandsat.interpolate_missing | xlandsat v0.0.post58+geb1d62a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
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    + + + + + + + + + + +
    + +
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    +
    + +
    +

    xlandsat.interpolate_missing

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

    xlandsat.interpolate_missing#

    +
    +
    +xlandsat.interpolate_missing(scene, pixel_radius=20)[source]#
    +

    Fill missing values (NaNs) in a scene by cubic interpolation

    +

    Each missing value is filled by interpolating the pixels within a +neighboring region (controlled by pixel_radius) using a piecewise cubic +2D interpolator. Interpolation is done for each band in a scene separately.

    +

    Note that this is mostly good if there are a few missing values, not large +regions of the scene.

    +
    +
    Parameters
    +
      +

    • scene (xarray.Dataset) – A Landsat scene, as read with xlandsat.load_scene.

      • +

    • pixel_radius (int) – Number of pixels to the east, west, south, and north of a missing value +that will be used for interpolation. Smaller values make for faster +interpolation but may lead to bad results if many missing values are +grouped together.

      • +
    +
    +
    Returns
    +

    filled_scene (xarray.Dataset) – The scene with missing values filled in.

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

    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/dev/api/generated/xlandsat.load_panchromatic.html b/dev/api/generated/xlandsat.load_panchromatic.html index de5c9a7..1b89608 100644 --- a/dev/api/generated/xlandsat.load_panchromatic.html +++ b/dev/api/generated/xlandsat.load_panchromatic.html @@ -8,7 +8,7 @@ - xlandsat.load_panchromatic | xlandsat v0.0.post36 + xlandsat.load_panchromatic | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • - +

    next

    -

    xlandsat.datasets.fetch_brumadinho_after

    +

    xlandsat.equalize_histogram

     @@ -495,7 +550,7 @@

    xlandsat.pansharpen

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    diff --git a/dev/api/generated/xlandsat.save_scene.html b/dev/api/generated/xlandsat.save_scene.html index 478a24a..8435808 100644 --- a/dev/api/generated/xlandsat.save_scene.html +++ b/dev/api/generated/xlandsat.save_scene.html @@ -8,7 +8,7 @@ - xlandsat.save_scene | xlandsat v0.0.post36 + xlandsat.save_scene | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • @@ -467,7 +522,7 @@

    xlandsat.save_scenepathlib.Path) – The desired path of the output tar archive. The file extension can be .tar (uncompressed) or .tar.gz, .tar.xz, or .tar.bz2 to make a compressed archive.

    -

  • scene (xarray.Dataset) – The scene including UTM easting and northing as dimensional +

  • scene (xarray.Dataset) – The scene including UTM easting and northing as dimensional coordinates, bands as 2D arrays of the given type as variables, and metadata read from the MTL file and other CF compliant fields in the attrs attribute.

    • @@ -508,7 +563,7 @@

    xlandsat.save_scene

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    diff --git a/dev/api/index.html b/dev/api/index.html index 2f9d763..0cd1993 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -8,7 +8,7 @@ - List of functions and classes (API) | xlandsat v0.0.post36 + List of functions and classes (API) | xlandsat v0.0.post58+geb1d62a @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/changes.html b/dev/changes.html index f7da80b..5121208 100644 --- a/dev/changes.html +++ b/dev/changes.html @@ -8,7 +8,7 @@ - Changelog | xlandsat v0.0.post36 + Changelog | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    This release contains contributions from:

    @@ -612,7 +667,7 @@

    Version 0.0.1

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    • diff --git a/dev/citing.html b/dev/citing.html index 93d9628..6cf65e0 100644 --- a/dev/citing.html +++ b/dev/citing.html @@ -8,7 +8,7 @@ - Citing | xlandsat v0.0.post36 + Citing | xlandsat v0.0.post58+geb1d62a @@ -50,7 +50,7 @@ - + @@ -84,9 +84,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • diff --git a/dev/composites.html b/dev/composites.html index 43f9056..caf6265 100644 --- a/dev/composites.html +++ b/dev/composites.html @@ -8,7 +8,7 @@ - Composites | xlandsat v0.0.post36 + Composites | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
    + + + + -

    xlandsat v0.0.post36

    + +

    xlandsat v0.0.post58+geb1d62a
    @@ -132,6 +137,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

      +
    • + + How to contribute + +
      • +
    • + + Code of Conduct + +
    • Source code on GitHub @@ -433,13 +488,23 @@

      xlandsat v0.

    • cir
      (northing, easting, channel)
      uint8
      178 66 62 255 175 ... 144 53 55 255
      long_name :
      near-infrared, red, green composite
      Conventions :
      CF-1.8
      title :
      Landsat 8 scene from 2019-01-14 (path/row=218/74)
      digital_object_identifier :
      https://doi.org/10.5066/P9OGBGM6
      origin :
      Image courtesy of the U.S. Geological Survey
      landsat_product_id :
      LC08_L2SP_218074_20190114_20200829_02_T1
      processing_level :
      L2SP
      collection_number :
      02
      collection_category :
      T1
      spacecraft_id :
      LANDSAT_8
      sensor_id :
      OLI_TIRS
      map_projection :
      UTM
      utm_zone :
      23
      datum :
      WGS84
      ellipsoid :
      WGS84
      date_acquired :
      2019-01-14
      scene_center_time :
      12:57:13.1804960Z
      wrs_path :
      218
      wrs_row :
      74
      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" - LANDSAT_PRODUCT_ID = "LC08_L2SP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L2SP" - COLLECTION_NUMBER = 02 - COLLECTION_CATEGORY = "T1" - OUTPUT_FORMAT = "GEOTIFF" - FILE_NAME_BAND_1 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B1.TIF" - FILE_NAME_BAND_2 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B2.TIF" - FILE_NAME_BAND_3 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B3.TIF" - FILE_NAME_BAND_4 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B4.TIF" - FILE_NAME_BAND_5 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B5.TIF" - FILE_NAME_BAND_6 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B6.TIF" - FILE_NAME_BAND_7 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B7.TIF" - FILE_NAME_BAND_ST_B10 = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_B10.TIF" - FILE_NAME_THERMAL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_TRAD.TIF" - FILE_NAME_UPWELL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_URAD.TIF" - FILE_NAME_DOWNWELL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_DRAD.TIF" - FILE_NAME_ATMOSPHERIC_TRANSMITTANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_ATRAN.TIF" - FILE_NAME_EMISSIVITY = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_EMIS.TIF" - FILE_NAME_EMISSIVITY_STDEV = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_EMSD.TIF" - FILE_NAME_CLOUD_DISTANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_CDIST.TIF" - FILE_NAME_QUALITY_L2_AEROSOL = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_QA_AEROSOL.TIF" - FILE_NAME_QUALITY_L2_SURFACE_TEMPERATURE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_QA.TIF" - FILE_NAME_QUALITY_L1_PIXEL = "LC08_L2SP_218074_20190114_20200829_02_T1_QA_PIXEL.TIF" - FILE_NAME_QUALITY_L1_RADIOMETRIC_SATURATION = "LC08_L2SP_218074_20190114_20200829_02_T1_QA_RADSAT.TIF" - FILE_NAME_ANGLE_COEFFICIENT = "LC08_L2SP_218074_20190114_20200829_02_T1_ANG.txt" - FILE_NAME_METADATA_ODL = "LC08_L2SP_218074_20190114_20200829_02_T1_MTL.txt" - FILE_NAME_METADATA_XML = "LC08_L2SP_218074_20190114_20200829_02_T1_MTL.xml" - DATA_TYPE_BAND_1 = "UINT16" - DATA_TYPE_BAND_2 = "UINT16" - DATA_TYPE_BAND_3 = "UINT16" - DATA_TYPE_BAND_4 = "UINT16" - DATA_TYPE_BAND_5 = "UINT16" - DATA_TYPE_BAND_6 = "UINT16" - DATA_TYPE_BAND_7 = "UINT16" - DATA_TYPE_BAND_ST_B10 = "UINT16" - DATA_TYPE_THERMAL_RADIANCE = "INT16" - DATA_TYPE_UPWELL_RADIANCE = "INT16" - DATA_TYPE_DOWNWELL_RADIANCE = "INT16" - DATA_TYPE_ATMOSPHERIC_TRANSMITTANCE = "INT16" - DATA_TYPE_EMISSIVITY = "INT16" - DATA_TYPE_EMISSIVITY_STDEV = "INT16" - DATA_TYPE_CLOUD_DISTANCE = "INT16" - DATA_TYPE_QUALITY_L2_AEROSOL = "UINT8" - DATA_TYPE_QUALITY_L2_SURFACE_TEMPERATURE = "INT16" - DATA_TYPE_QUALITY_L1_PIXEL = "UINT16" - DATA_TYPE_QUALITY_L1_RADIOMETRIC_SATURATION = "UINT16" - END_GROUP = PRODUCT_CONTENTS - GROUP = IMAGE_ATTRIBUTES - SPACECRAFT_ID = "LANDSAT_8" - SENSOR_ID = "OLI_TIRS" - WRS_TYPE = 2 - WRS_PATH = 218 - WRS_ROW = 74 - NADIR_OFFNADIR = "NADIR" - TARGET_WRS_PATH = 218 - TARGET_WRS_ROW = 74 - DATE_ACQUIRED = 2019-01-14 - SCENE_CENTER_TIME = "12:57:13.1804960Z" - STATION_ID = "LGN" - CLOUD_COVER = 9.86 - CLOUD_COVER_LAND = 9.86 - IMAGE_QUALITY_OLI = 9 - IMAGE_QUALITY_TIRS = 9 - SATURATION_BAND_1 = "Y" - SATURATION_BAND_2 = "Y" - SATURATION_BAND_3 = "Y" - SATURATION_BAND_4 = "Y" - SATURATION_BAND_5 = "Y" - SATURATION_BAND_6 = "Y" - SATURATION_BAND_7 = "Y" - SATURATION_BAND_8 = "N" - SATURATION_BAND_9 = "N" - ROLL_ANGLE = -0.001 - SUN_AZIMUTH = 97.81448496 - SUN_ELEVATION = 59.92196710 - EARTH_SUN_DISTANCE = 0.9835584 - TRUNCATION_OLI = "UPPER" - TIRS_SSM_MODEL = "FINAL" - TIRS_SSM_POSITION_STATUS = "ESTIMATED" - END_GROUP = IMAGE_ATTRIBUTES - GROUP = PROJECTION_ATTRIBUTES - MAP_PROJECTION = "UTM" - DATUM = "WGS84" - ELLIPSOID = "WGS84" - UTM_ZONE = 23 - GRID_CELL_SIZE_REFLECTIVE = 30.00 - GRID_CELL_SIZE_THERMAL = 30.00 - REFLECTIVE_LINES = 300 - REFLECTIVE_SAMPLES = 400 - THERMAL_LINES = 300 - THERMAL_SAMPLES = 400 - ORIENTATION = "NORTH_UP" - CORNER_UL_LAT_PRODUCT = -19.17924 - CORNER_UL_LON_PRODUCT = -45.36143 - CORNER_UR_LAT_PRODUCT = -19.17048 - CORNER_UR_LON_PRODUCT = -43.17034 - CORNER_LL_LAT_PRODUCT = -21.28831 - CORNER_LL_LON_PRODUCT = -45.36634 - CORNER_LR_LAT_PRODUCT = -21.27849 - CORNER_LR_LON_PRODUCT = -43.14551 - CORNER_UL_PROJECTION_X_PRODUCT = 583500.000 - CORNER_UL_PROJECTION_Y_PRODUCT = -2222700.000 - CORNER_UR_PROJECTION_X_PRODUCT = 595470.000 - CORNER_UR_PROJECTION_Y_PRODUCT = -2222700.000 - CORNER_LL_PROJECTION_X_PRODUCT = 583500.000 - CORNER_LL_PROJECTION_Y_PRODUCT = -2231670.000 - CORNER_LR_PROJECTION_X_PRODUCT = 595470.000 - CORNER_LR_PROJECTION_Y_PRODUCT = -2231670.000 - END_GROUP = PROJECTION_ATTRIBUTES - GROUP = LEVEL2_PROCESSING_RECORD - ORIGIN = "Image courtesy of the U.S. Geological Survey" - DIGITAL_OBJECT_IDENTIFIER = "https://doi.org/10.5066/P9OGBGM6" - REQUEST_ID = "L2" - LANDSAT_PRODUCT_ID = "LC08_L2SP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L2SP" - OUTPUT_FORMAT = "GEOTIFF" - DATE_PRODUCT_GENERATED = 2020-08-29T22:04:19Z - PROCESSING_SOFTWARE_VERSION = "LPGS_15.3.1c" - ALGORITHM_SOURCE_SURFACE_REFLECTANCE = "LaSRC_1.5.0" - DATA_SOURCE_OZONE = "MODIS" - DATA_SOURCE_PRESSURE = "Calculated" - DATA_SOURCE_WATER_VAPOR = "MODIS" - DATA_SOURCE_AIR_TEMPERATURE = "MODIS" - ALGORITHM_SOURCE_SURFACE_TEMPERATURE = "st_1.3.0" - DATA_SOURCE_REANALYSIS = "GEOS-5 FP-IT" - END_GROUP = LEVEL2_PROCESSING_RECORD - GROUP = LEVEL2_SURFACE_REFLECTANCE_PARAMETERS - REFLECTANCE_MAXIMUM_BAND_1 = 1.602213 - REFLECTANCE_MINIMUM_BAND_1 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_2 = 1.602213 - REFLECTANCE_MINIMUM_BAND_2 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_3 = 1.602213 - REFLECTANCE_MINIMUM_BAND_3 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_4 = 1.602213 - REFLECTANCE_MINIMUM_BAND_4 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_5 = 1.602213 - REFLECTANCE_MINIMUM_BAND_5 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_6 = 1.602213 - REFLECTANCE_MINIMUM_BAND_6 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_7 = 1.602213 - REFLECTANCE_MINIMUM_BAND_7 = -0.199972 - QUANTIZE_CAL_MAX_BAND_1 = 65535 - QUANTIZE_CAL_MIN_BAND_1 = 1 - QUANTIZE_CAL_MAX_BAND_2 = 65535 - QUANTIZE_CAL_MIN_BAND_2 = 1 - QUANTIZE_CAL_MAX_BAND_3 = 65535 - QUANTIZE_CAL_MIN_BAND_3 = 1 - QUANTIZE_CAL_MAX_BAND_4 = 65535 - QUANTIZE_CAL_MIN_BAND_4 = 1 - QUANTIZE_CAL_MAX_BAND_5 = 65535 - QUANTIZE_CAL_MIN_BAND_5 = 1 - QUANTIZE_CAL_MAX_BAND_6 = 65535 - QUANTIZE_CAL_MIN_BAND_6 = 1 - QUANTIZE_CAL_MAX_BAND_7 = 65535 - QUANTIZE_CAL_MIN_BAND_7 = 1 - REFLECTANCE_MULT_BAND_1 = 2.75e-05 - REFLECTANCE_MULT_BAND_2 = 2.75e-05 - REFLECTANCE_MULT_BAND_3 = 2.75e-05 - REFLECTANCE_MULT_BAND_4 = 2.75e-05 - REFLECTANCE_MULT_BAND_5 = 2.75e-05 - REFLECTANCE_MULT_BAND_6 = 2.75e-05 - REFLECTANCE_MULT_BAND_7 = 2.75e-05 - REFLECTANCE_ADD_BAND_1 = -0.2 - REFLECTANCE_ADD_BAND_2 = -0.2 - REFLECTANCE_ADD_BAND_3 = -0.2 - REFLECTANCE_ADD_BAND_4 = -0.2 - REFLECTANCE_ADD_BAND_5 = -0.2 - REFLECTANCE_ADD_BAND_6 = -0.2 - REFLECTANCE_ADD_BAND_7 = -0.2 - END_GROUP = LEVEL2_SURFACE_REFLECTANCE_PARAMETERS - GROUP = LEVEL2_SURFACE_TEMPERATURE_PARAMETERS - TEMPERATURE_MAXIMUM_BAND_ST_B10 = 372.999941 - TEMPERATURE_MINIMUM_BAND_ST_B10 = 149.003418 - QUANTIZE_CAL_MAXIMUM_BAND_ST_B10 = 65535 - QUANTIZE_CAL_MINIMUM_BAND_ST_B10 = 1 - TEMPERATURE_MULT_BAND_ST_B10 = 0.00341802 - TEMPERATURE_ADD_BAND_ST_B10 = 149.0 - END_GROUP = LEVEL2_SURFACE_TEMPERATURE_PARAMETERS - GROUP = LEVEL1_PROCESSING_RECORD - ORIGIN = "Image courtesy of the U.S. Geological Survey" - DIGITAL_OBJECT_IDENTIFIER = "https://doi.org/10.5066/P975CC9B" - REQUEST_ID = "L2" - LANDSAT_SCENE_ID = "LC82180742019014LGN00" - LANDSAT_PRODUCT_ID = "LC08_L1TP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L1TP" - COLLECTION_CATEGORY = "T1" - OUTPUT_FORMAT = "GEOTIFF" - DATE_PRODUCT_GENERATED = 2020-08-29T21:45:03Z - PROCESSING_SOFTWARE_VERSION = "LPGS_15.3.1c" - FILE_NAME_BAND_1 = "LC08_L1TP_218074_20190114_20200829_02_T1_B1.TIF" - FILE_NAME_BAND_2 = "LC08_L1TP_218074_20190114_20200829_02_T1_B2.TIF" - FILE_NAME_BAND_3 = "LC08_L1TP_218074_20190114_20200829_02_T1_B3.TIF" - FILE_NAME_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_B4.TIF" - FILE_NAME_BAND_5 = "LC08_L1TP_218074_20190114_20200829_02_T1_B5.TIF" - FILE_NAME_BAND_6 = "LC08_L1TP_218074_20190114_20200829_02_T1_B6.TIF" - FILE_NAME_BAND_7 = "LC08_L1TP_218074_20190114_20200829_02_T1_B7.TIF" - FILE_NAME_BAND_8 = "LC08_L1TP_218074_20190114_20200829_02_T1_B8.TIF" - FILE_NAME_BAND_9 = "LC08_L1TP_218074_20190114_20200829_02_T1_B9.TIF" - FILE_NAME_BAND_10 = "LC08_L1TP_218074_20190114_20200829_02_T1_B10.TIF" - FILE_NAME_BAND_11 = "LC08_L1TP_218074_20190114_20200829_02_T1_B11.TIF" - FILE_NAME_QUALITY_L1_PIXEL = "LC08_L1TP_218074_20190114_20200829_02_T1_QA_PIXEL.TIF" - FILE_NAME_QUALITY_L1_RADIOMETRIC_SATURATION = "LC08_L1TP_218074_20190114_20200829_02_T1_QA_RADSAT.TIF" - FILE_NAME_ANGLE_COEFFICIENT = "LC08_L1TP_218074_20190114_20200829_02_T1_ANG.txt" - FILE_NAME_ANGLE_SENSOR_AZIMUTH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_VAA.TIF" - FILE_NAME_ANGLE_SENSOR_ZENITH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_VZA.TIF" - FILE_NAME_ANGLE_SOLAR_AZIMUTH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_SAA.TIF" - FILE_NAME_ANGLE_SOLAR_ZENITH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_SZA.TIF" - FILE_NAME_METADATA_ODL = "LC08_L1TP_218074_20190114_20200829_02_T1_MTL.txt" - FILE_NAME_METADATA_XML = "LC08_L1TP_218074_20190114_20200829_02_T1_MTL.xml" - FILE_NAME_CPF = "LC08CPF_20190101_20190331_02.01" - FILE_NAME_BPF_OLI = "LO8BPF20190114123217_20190114131719.02" - FILE_NAME_BPF_TIRS = "LT8BPF20190106111321_20190121165109.02" - FILE_NAME_RLUT = "LC08RLUT_20150303_20431231_02_01.h5" - DATA_SOURCE_TIRS_STRAY_LIGHT_CORRECTION = "TIRS" - DATA_SOURCE_ELEVATION = "GLS2000" - GROUND_CONTROL_POINTS_VERSION = 5 - GROUND_CONTROL_POINTS_MODEL = 749 - GEOMETRIC_RMSE_MODEL = 6.471 - GEOMETRIC_RMSE_MODEL_Y = 5.198 - GEOMETRIC_RMSE_MODEL_X = 3.855 - GROUND_CONTROL_POINTS_VERIFY = 211 - GEOMETRIC_RMSE_VERIFY = 3.596 - END_GROUP = LEVEL1_PROCESSING_RECORD - GROUP = LEVEL1_MIN_MAX_RADIANCE - RADIANCE_MAXIMUM_BAND_1 = 785.68616 - RADIANCE_MINIMUM_BAND_1 = -64.88222 - RADIANCE_MAXIMUM_BAND_2 = 804.55225 - RADIANCE_MINIMUM_BAND_2 = -66.44019 - RADIANCE_MAXIMUM_BAND_3 = 741.38800 - RADIANCE_MINIMUM_BAND_3 = -61.22406 - RADIANCE_MAXIMUM_BAND_4 = 625.18011 - RADIANCE_MINIMUM_BAND_4 = -51.62758 - RADIANCE_MAXIMUM_BAND_5 = 382.57907 - RADIANCE_MINIMUM_BAND_5 = -31.59350 - RADIANCE_MAXIMUM_BAND_6 = 95.14393 - RADIANCE_MINIMUM_BAND_6 = -7.85702 - RADIANCE_MAXIMUM_BAND_7 = 32.06860 - RADIANCE_MINIMUM_BAND_7 = -2.64824 - RADIANCE_MAXIMUM_BAND_8 = 707.53186 - RADIANCE_MINIMUM_BAND_8 = -58.42821 - RADIANCE_MAXIMUM_BAND_9 = 149.52057 - RADIANCE_MINIMUM_BAND_9 = -12.34746 - RADIANCE_MAXIMUM_BAND_10 = 22.00180 - RADIANCE_MINIMUM_BAND_10 = 0.10033 - RADIANCE_MAXIMUM_BAND_11 = 22.00180 - RADIANCE_MINIMUM_BAND_11 = 0.10033 - END_GROUP = LEVEL1_MIN_MAX_RADIANCE - GROUP = LEVEL1_MIN_MAX_REFLECTANCE - REFLECTANCE_MAXIMUM_BAND_1 = 1.210700 - REFLECTANCE_MINIMUM_BAND_1 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_2 = 1.210700 - REFLECTANCE_MINIMUM_BAND_2 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_3 = 1.210700 - REFLECTANCE_MINIMUM_BAND_3 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_4 = 1.210700 - REFLECTANCE_MINIMUM_BAND_4 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_5 = 1.210700 - REFLECTANCE_MINIMUM_BAND_5 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_6 = 1.210700 - REFLECTANCE_MINIMUM_BAND_6 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_7 = 1.210700 - REFLECTANCE_MINIMUM_BAND_7 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_8 = 1.210700 - REFLECTANCE_MINIMUM_BAND_8 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_9 = 1.210700 - REFLECTANCE_MINIMUM_BAND_9 = -0.099980 - END_GROUP = LEVEL1_MIN_MAX_REFLECTANCE - GROUP = LEVEL1_MIN_MAX_PIXEL_VALUE - QUANTIZE_CAL_MAX_BAND_1 = 65535 - QUANTIZE_CAL_MIN_BAND_1 = 1 - QUANTIZE_CAL_MAX_BAND_2 = 65535 - QUANTIZE_CAL_MIN_BAND_2 = 1 - QUANTIZE_CAL_MAX_BAND_3 = 65535 - QUANTIZE_CAL_MIN_BAND_3 = 1 - QUANTIZE_CAL_MAX_BAND_4 = 65535 - QUANTIZE_CAL_MIN_BAND_4 = 1 - QUANTIZE_CAL_MAX_BAND_5 = 65535 - QUANTIZE_CAL_MIN_BAND_5 = 1 - QUANTIZE_CAL_MAX_BAND_6 = 65535 - QUANTIZE_CAL_MIN_BAND_6 = 1 - QUANTIZE_CAL_MAX_BAND_7 = 65535 - QUANTIZE_CAL_MIN_BAND_7 = 1 - QUANTIZE_CAL_MAX_BAND_8 = 65535 - QUANTIZE_CAL_MIN_BAND_8 = 1 - QUANTIZE_CAL_MAX_BAND_9 = 65535 - QUANTIZE_CAL_MIN_BAND_9 = 1 - QUANTIZE_CAL_MAX_BAND_10 = 65535 - QUANTIZE_CAL_MIN_BAND_10 = 1 - QUANTIZE_CAL_MAX_BAND_11 = 65535 - QUANTIZE_CAL_MIN_BAND_11 = 1 - END_GROUP = LEVEL1_MIN_MAX_PIXEL_VALUE - GROUP = LEVEL1_RADIOMETRIC_RESCALING - RADIANCE_MULT_BAND_1 = 1.2979E-02 - RADIANCE_MULT_BAND_2 = 1.3291E-02 - RADIANCE_MULT_BAND_3 = 1.2247E-02 - RADIANCE_MULT_BAND_4 = 1.0328E-02 - RADIANCE_MULT_BAND_5 = 6.3200E-03 - RADIANCE_MULT_BAND_6 = 1.5717E-03 - RADIANCE_MULT_BAND_7 = 5.2975E-04 - RADIANCE_MULT_BAND_8 = 1.1688E-02 - RADIANCE_MULT_BAND_9 = 2.4700E-03 - RADIANCE_MULT_BAND_10 = 3.3420E-04 - RADIANCE_MULT_BAND_11 = 3.3420E-04 - RADIANCE_ADD_BAND_1 = -64.89520 - RADIANCE_ADD_BAND_2 = -66.45347 - RADIANCE_ADD_BAND_3 = -61.23631 - RADIANCE_ADD_BAND_4 = -51.63791 - RADIANCE_ADD_BAND_5 = -31.59982 - RADIANCE_ADD_BAND_6 = -7.85859 - RADIANCE_ADD_BAND_7 = -2.64877 - RADIANCE_ADD_BAND_8 = -58.43990 - RADIANCE_ADD_BAND_9 = -12.34993 - RADIANCE_ADD_BAND_10 = 0.10000 - RADIANCE_ADD_BAND_11 = 0.10000 - REFLECTANCE_MULT_BAND_1 = 2.0000E-05 - REFLECTANCE_MULT_BAND_2 = 2.0000E-05 - REFLECTANCE_MULT_BAND_3 = 2.0000E-05 - REFLECTANCE_MULT_BAND_4 = 2.0000E-05 - REFLECTANCE_MULT_BAND_5 = 2.0000E-05 - REFLECTANCE_MULT_BAND_6 = 2.0000E-05 - REFLECTANCE_MULT_BAND_7 = 2.0000E-05 - REFLECTANCE_MULT_BAND_8 = 2.0000E-05 - REFLECTANCE_MULT_BAND_9 = 2.0000E-05 - REFLECTANCE_ADD_BAND_1 = -0.100000 - REFLECTANCE_ADD_BAND_2 = -0.100000 - REFLECTANCE_ADD_BAND_3 = -0.100000 - REFLECTANCE_ADD_BAND_4 = -0.100000 - REFLECTANCE_ADD_BAND_5 = -0.100000 - REFLECTANCE_ADD_BAND_6 = -0.100000 - REFLECTANCE_ADD_BAND_7 = -0.100000 - REFLECTANCE_ADD_BAND_8 = -0.100000 - REFLECTANCE_ADD_BAND_9 = -0.100000 - END_GROUP = LEVEL1_RADIOMETRIC_RESCALING - GROUP = LEVEL1_THERMAL_CONSTANTS - K1_CONSTANT_BAND_10 = 774.8853 - K2_CONSTANT_BAND_10 = 1321.0789 - K1_CONSTANT_BAND_11 = 480.8883 - K2_CONSTANT_BAND_11 = 1201.1442 - END_GROUP = LEVEL1_THERMAL_CONSTANTS - GROUP = LEVEL1_PROJECTION_PARAMETERS - MAP_PROJECTION = "UTM" - DATUM = "WGS84" - ELLIPSOID = "WGS84" - UTM_ZONE = 23 - GRID_CELL_SIZE_PANCHROMATIC = 15.00 - GRID_CELL_SIZE_REFLECTIVE = 30.00 - GRID_CELL_SIZE_THERMAL = 30.00 - ORIENTATION = "NORTH_UP" - RESAMPLING_OPTION = "CUBIC_CONVOLUTION" - END_GROUP = LEVEL1_PROJECTION_PARAMETERS -END_GROUP = LANDSAT_METADATA_FILE -END -
      array([[[178,  66,  62, 255],
-        [175,  66,  63, 255],
-        [204,  55,  59, 255],
-        ...,
-        [180,  53,  58, 255],
-        [185,  47,  52, 255],
-        [176,  50,  53, 255]],
-
-       [[180,  69,  65, 255],
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-        [194,  68,  66, 255],
-        ...,
-        [193,  44,  54, 255],
-        [180,  49,  56, 255],
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-
-       [[192,  68,  65, 255],
-        [194,  65,  63, 255],
-        [190,  68,  65, 255],
-        ...,
-...
-        ...,
-        [157,  48,  53, 255],
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-        [139,  49,  52, 255]],
-
-       [[151,  52,  55, 255],
-        [138,  50,  52, 255],
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-        ...,
-        [145,  51,  54, 255],
-        [135,  50,  53, 255],
-        [144,  53,  55, 255]]], dtype=uint8)

    • easting
      PandasIndex
      PandasIndex(Float64Index([583500.0, 583530.0, 583560.0, 583590.0, 583620.0, 583650.0,
-              583680.0, 583710.0, 583740.0, 583770.0,
-              ...
-              595200.0, 595230.0, 595260.0, 595290.0, 595320.0, 595350.0,
-              595380.0, 595410.0, 595440.0, 595470.0],
-             dtype='float64', name='easting', length=400))
    • northing
      PandasIndex
      PandasIndex(Float64Index([-2231670.0, -2231640.0, -2231610.0, -2231580.0, -2231550.0,
-              -2231520.0, -2231490.0, -2231460.0, -2231430.0, -2231400.0,
-              ...
-              -2222970.0, -2222940.0, -2222910.0, -2222880.0, -2222850.0,
-              -2222820.0, -2222790.0, -2222760.0, -2222730.0, -2222700.0],
-             dtype='float64', name='northing', length=300))
    • channel
      PandasIndex
      PandasIndex(Index(['red', 'green', 'blue', 'alpha'], dtype='object', name='channel'))
  • Conventions :
    CF-1.8
    title :
    Landsat 8 scene from 2019-01-14 (path/row=218/74)
    digital_object_identifier :
    https://doi.org/10.5066/P9OGBGM6
    origin :
    Image courtesy of the U.S. Geological Survey
    landsat_product_id :
    LC08_L2SP_218074_20190114_20200829_02_T1
    processing_level :
    L2SP
    collection_number :
    02
    collection_category :
    T1
    spacecraft_id :
    LANDSAT_8
    sensor_id :
    OLI_TIRS
    map_projection :
    UTM
    utm_zone :
    23
    datum :
    WGS84
    ellipsoid :
    WGS84
    date_acquired :
    2019-01-14
    scene_center_time :
    12:57:13.1804960Z
    wrs_path :
    218
    wrs_row :
    74
    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" - LANDSAT_PRODUCT_ID = "LC08_L2SP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L2SP" - COLLECTION_NUMBER = 02 - COLLECTION_CATEGORY = "T1" - OUTPUT_FORMAT = "GEOTIFF" - FILE_NAME_BAND_1 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B1.TIF" - FILE_NAME_BAND_2 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B2.TIF" - FILE_NAME_BAND_3 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B3.TIF" - FILE_NAME_BAND_4 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B4.TIF" - FILE_NAME_BAND_5 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B5.TIF" - FILE_NAME_BAND_6 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B6.TIF" - FILE_NAME_BAND_7 = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_B7.TIF" - FILE_NAME_BAND_ST_B10 = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_B10.TIF" - FILE_NAME_THERMAL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_TRAD.TIF" - FILE_NAME_UPWELL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_URAD.TIF" - FILE_NAME_DOWNWELL_RADIANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_DRAD.TIF" - FILE_NAME_ATMOSPHERIC_TRANSMITTANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_ATRAN.TIF" - FILE_NAME_EMISSIVITY = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_EMIS.TIF" - FILE_NAME_EMISSIVITY_STDEV = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_EMSD.TIF" - FILE_NAME_CLOUD_DISTANCE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_CDIST.TIF" - FILE_NAME_QUALITY_L2_AEROSOL = "LC08_L2SP_218074_20190114_20200829_02_T1_SR_QA_AEROSOL.TIF" - FILE_NAME_QUALITY_L2_SURFACE_TEMPERATURE = "LC08_L2SP_218074_20190114_20200829_02_T1_ST_QA.TIF" - FILE_NAME_QUALITY_L1_PIXEL = "LC08_L2SP_218074_20190114_20200829_02_T1_QA_PIXEL.TIF" - FILE_NAME_QUALITY_L1_RADIOMETRIC_SATURATION = "LC08_L2SP_218074_20190114_20200829_02_T1_QA_RADSAT.TIF" - FILE_NAME_ANGLE_COEFFICIENT = "LC08_L2SP_218074_20190114_20200829_02_T1_ANG.txt" - FILE_NAME_METADATA_ODL = "LC08_L2SP_218074_20190114_20200829_02_T1_MTL.txt" - FILE_NAME_METADATA_XML = "LC08_L2SP_218074_20190114_20200829_02_T1_MTL.xml" - DATA_TYPE_BAND_1 = "UINT16" - DATA_TYPE_BAND_2 = "UINT16" - DATA_TYPE_BAND_3 = "UINT16" - DATA_TYPE_BAND_4 = "UINT16" - DATA_TYPE_BAND_5 = "UINT16" - DATA_TYPE_BAND_6 = "UINT16" - DATA_TYPE_BAND_7 = "UINT16" - DATA_TYPE_BAND_ST_B10 = "UINT16" - DATA_TYPE_THERMAL_RADIANCE = "INT16" - DATA_TYPE_UPWELL_RADIANCE = "INT16" - DATA_TYPE_DOWNWELL_RADIANCE = "INT16" - DATA_TYPE_ATMOSPHERIC_TRANSMITTANCE = "INT16" - DATA_TYPE_EMISSIVITY = "INT16" - DATA_TYPE_EMISSIVITY_STDEV = "INT16" - DATA_TYPE_CLOUD_DISTANCE = "INT16" - DATA_TYPE_QUALITY_L2_AEROSOL = "UINT8" - DATA_TYPE_QUALITY_L2_SURFACE_TEMPERATURE = "INT16" - DATA_TYPE_QUALITY_L1_PIXEL = "UINT16" - DATA_TYPE_QUALITY_L1_RADIOMETRIC_SATURATION = "UINT16" - END_GROUP = PRODUCT_CONTENTS - GROUP = IMAGE_ATTRIBUTES - SPACECRAFT_ID = "LANDSAT_8" - SENSOR_ID = "OLI_TIRS" - WRS_TYPE = 2 - WRS_PATH = 218 - WRS_ROW = 74 - NADIR_OFFNADIR = "NADIR" - TARGET_WRS_PATH = 218 - TARGET_WRS_ROW = 74 - DATE_ACQUIRED = 2019-01-14 - SCENE_CENTER_TIME = "12:57:13.1804960Z" - STATION_ID = "LGN" - CLOUD_COVER = 9.86 - CLOUD_COVER_LAND = 9.86 - IMAGE_QUALITY_OLI = 9 - IMAGE_QUALITY_TIRS = 9 - SATURATION_BAND_1 = "Y" - SATURATION_BAND_2 = "Y" - SATURATION_BAND_3 = "Y" - SATURATION_BAND_4 = "Y" - SATURATION_BAND_5 = "Y" - SATURATION_BAND_6 = "Y" - SATURATION_BAND_7 = "Y" - SATURATION_BAND_8 = "N" - SATURATION_BAND_9 = "N" - ROLL_ANGLE = -0.001 - SUN_AZIMUTH = 97.81448496 - SUN_ELEVATION = 59.92196710 - EARTH_SUN_DISTANCE = 0.9835584 - TRUNCATION_OLI = "UPPER" - TIRS_SSM_MODEL = "FINAL" - TIRS_SSM_POSITION_STATUS = "ESTIMATED" - END_GROUP = IMAGE_ATTRIBUTES - GROUP = PROJECTION_ATTRIBUTES - MAP_PROJECTION = "UTM" - DATUM = "WGS84" - ELLIPSOID = "WGS84" - UTM_ZONE = 23 - GRID_CELL_SIZE_REFLECTIVE = 30.00 - GRID_CELL_SIZE_THERMAL = 30.00 - REFLECTIVE_LINES = 300 - REFLECTIVE_SAMPLES = 400 - THERMAL_LINES = 300 - THERMAL_SAMPLES = 400 - ORIENTATION = "NORTH_UP" - CORNER_UL_LAT_PRODUCT = -19.17924 - CORNER_UL_LON_PRODUCT = -45.36143 - CORNER_UR_LAT_PRODUCT = -19.17048 - CORNER_UR_LON_PRODUCT = -43.17034 - CORNER_LL_LAT_PRODUCT = -21.28831 - CORNER_LL_LON_PRODUCT = -45.36634 - CORNER_LR_LAT_PRODUCT = -21.27849 - CORNER_LR_LON_PRODUCT = -43.14551 - CORNER_UL_PROJECTION_X_PRODUCT = 583500.000 - CORNER_UL_PROJECTION_Y_PRODUCT = -2222700.000 - CORNER_UR_PROJECTION_X_PRODUCT = 595470.000 - CORNER_UR_PROJECTION_Y_PRODUCT = -2222700.000 - CORNER_LL_PROJECTION_X_PRODUCT = 583500.000 - CORNER_LL_PROJECTION_Y_PRODUCT = -2231670.000 - CORNER_LR_PROJECTION_X_PRODUCT = 595470.000 - CORNER_LR_PROJECTION_Y_PRODUCT = -2231670.000 - END_GROUP = PROJECTION_ATTRIBUTES - GROUP = LEVEL2_PROCESSING_RECORD - ORIGIN = "Image courtesy of the U.S. Geological Survey" - DIGITAL_OBJECT_IDENTIFIER = "https://doi.org/10.5066/P9OGBGM6" - REQUEST_ID = "L2" - LANDSAT_PRODUCT_ID = "LC08_L2SP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L2SP" - OUTPUT_FORMAT = "GEOTIFF" - DATE_PRODUCT_GENERATED = 2020-08-29T22:04:19Z - PROCESSING_SOFTWARE_VERSION = "LPGS_15.3.1c" - ALGORITHM_SOURCE_SURFACE_REFLECTANCE = "LaSRC_1.5.0" - DATA_SOURCE_OZONE = "MODIS" - DATA_SOURCE_PRESSURE = "Calculated" - DATA_SOURCE_WATER_VAPOR = "MODIS" - DATA_SOURCE_AIR_TEMPERATURE = "MODIS" - ALGORITHM_SOURCE_SURFACE_TEMPERATURE = "st_1.3.0" - DATA_SOURCE_REANALYSIS = "GEOS-5 FP-IT" - END_GROUP = LEVEL2_PROCESSING_RECORD - GROUP = LEVEL2_SURFACE_REFLECTANCE_PARAMETERS - REFLECTANCE_MAXIMUM_BAND_1 = 1.602213 - REFLECTANCE_MINIMUM_BAND_1 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_2 = 1.602213 - REFLECTANCE_MINIMUM_BAND_2 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_3 = 1.602213 - REFLECTANCE_MINIMUM_BAND_3 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_4 = 1.602213 - REFLECTANCE_MINIMUM_BAND_4 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_5 = 1.602213 - REFLECTANCE_MINIMUM_BAND_5 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_6 = 1.602213 - REFLECTANCE_MINIMUM_BAND_6 = -0.199972 - REFLECTANCE_MAXIMUM_BAND_7 = 1.602213 - REFLECTANCE_MINIMUM_BAND_7 = -0.199972 - QUANTIZE_CAL_MAX_BAND_1 = 65535 - QUANTIZE_CAL_MIN_BAND_1 = 1 - QUANTIZE_CAL_MAX_BAND_2 = 65535 - QUANTIZE_CAL_MIN_BAND_2 = 1 - QUANTIZE_CAL_MAX_BAND_3 = 65535 - QUANTIZE_CAL_MIN_BAND_3 = 1 - QUANTIZE_CAL_MAX_BAND_4 = 65535 - QUANTIZE_CAL_MIN_BAND_4 = 1 - QUANTIZE_CAL_MAX_BAND_5 = 65535 - QUANTIZE_CAL_MIN_BAND_5 = 1 - QUANTIZE_CAL_MAX_BAND_6 = 65535 - QUANTIZE_CAL_MIN_BAND_6 = 1 - QUANTIZE_CAL_MAX_BAND_7 = 65535 - QUANTIZE_CAL_MIN_BAND_7 = 1 - REFLECTANCE_MULT_BAND_1 = 2.75e-05 - REFLECTANCE_MULT_BAND_2 = 2.75e-05 - REFLECTANCE_MULT_BAND_3 = 2.75e-05 - REFLECTANCE_MULT_BAND_4 = 2.75e-05 - REFLECTANCE_MULT_BAND_5 = 2.75e-05 - REFLECTANCE_MULT_BAND_6 = 2.75e-05 - REFLECTANCE_MULT_BAND_7 = 2.75e-05 - REFLECTANCE_ADD_BAND_1 = -0.2 - REFLECTANCE_ADD_BAND_2 = -0.2 - REFLECTANCE_ADD_BAND_3 = -0.2 - REFLECTANCE_ADD_BAND_4 = -0.2 - REFLECTANCE_ADD_BAND_5 = -0.2 - REFLECTANCE_ADD_BAND_6 = -0.2 - REFLECTANCE_ADD_BAND_7 = -0.2 - END_GROUP = LEVEL2_SURFACE_REFLECTANCE_PARAMETERS - GROUP = LEVEL2_SURFACE_TEMPERATURE_PARAMETERS - TEMPERATURE_MAXIMUM_BAND_ST_B10 = 372.999941 - TEMPERATURE_MINIMUM_BAND_ST_B10 = 149.003418 - QUANTIZE_CAL_MAXIMUM_BAND_ST_B10 = 65535 - QUANTIZE_CAL_MINIMUM_BAND_ST_B10 = 1 - TEMPERATURE_MULT_BAND_ST_B10 = 0.00341802 - TEMPERATURE_ADD_BAND_ST_B10 = 149.0 - END_GROUP = LEVEL2_SURFACE_TEMPERATURE_PARAMETERS - GROUP = LEVEL1_PROCESSING_RECORD - ORIGIN = "Image courtesy of the U.S. Geological Survey" - DIGITAL_OBJECT_IDENTIFIER = "https://doi.org/10.5066/P975CC9B" - REQUEST_ID = "L2" - LANDSAT_SCENE_ID = "LC82180742019014LGN00" - LANDSAT_PRODUCT_ID = "LC08_L1TP_218074_20190114_20200829_02_T1" - PROCESSING_LEVEL = "L1TP" - COLLECTION_CATEGORY = "T1" - OUTPUT_FORMAT = "GEOTIFF" - DATE_PRODUCT_GENERATED = 2020-08-29T21:45:03Z - PROCESSING_SOFTWARE_VERSION = "LPGS_15.3.1c" - FILE_NAME_BAND_1 = "LC08_L1TP_218074_20190114_20200829_02_T1_B1.TIF" - FILE_NAME_BAND_2 = "LC08_L1TP_218074_20190114_20200829_02_T1_B2.TIF" - FILE_NAME_BAND_3 = "LC08_L1TP_218074_20190114_20200829_02_T1_B3.TIF" - FILE_NAME_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_B4.TIF" - FILE_NAME_BAND_5 = "LC08_L1TP_218074_20190114_20200829_02_T1_B5.TIF" - FILE_NAME_BAND_6 = "LC08_L1TP_218074_20190114_20200829_02_T1_B6.TIF" - FILE_NAME_BAND_7 = "LC08_L1TP_218074_20190114_20200829_02_T1_B7.TIF" - FILE_NAME_BAND_8 = "LC08_L1TP_218074_20190114_20200829_02_T1_B8.TIF" - FILE_NAME_BAND_9 = "LC08_L1TP_218074_20190114_20200829_02_T1_B9.TIF" - FILE_NAME_BAND_10 = "LC08_L1TP_218074_20190114_20200829_02_T1_B10.TIF" - FILE_NAME_BAND_11 = "LC08_L1TP_218074_20190114_20200829_02_T1_B11.TIF" - FILE_NAME_QUALITY_L1_PIXEL = "LC08_L1TP_218074_20190114_20200829_02_T1_QA_PIXEL.TIF" - FILE_NAME_QUALITY_L1_RADIOMETRIC_SATURATION = "LC08_L1TP_218074_20190114_20200829_02_T1_QA_RADSAT.TIF" - FILE_NAME_ANGLE_COEFFICIENT = "LC08_L1TP_218074_20190114_20200829_02_T1_ANG.txt" - FILE_NAME_ANGLE_SENSOR_AZIMUTH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_VAA.TIF" - FILE_NAME_ANGLE_SENSOR_ZENITH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_VZA.TIF" - FILE_NAME_ANGLE_SOLAR_AZIMUTH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_SAA.TIF" - FILE_NAME_ANGLE_SOLAR_ZENITH_BAND_4 = "LC08_L1TP_218074_20190114_20200829_02_T1_SZA.TIF" - FILE_NAME_METADATA_ODL = "LC08_L1TP_218074_20190114_20200829_02_T1_MTL.txt" - FILE_NAME_METADATA_XML = "LC08_L1TP_218074_20190114_20200829_02_T1_MTL.xml" - FILE_NAME_CPF = "LC08CPF_20190101_20190331_02.01" - FILE_NAME_BPF_OLI = "LO8BPF20190114123217_20190114131719.02" - FILE_NAME_BPF_TIRS = "LT8BPF20190106111321_20190121165109.02" - FILE_NAME_RLUT = "LC08RLUT_20150303_20431231_02_01.h5" - DATA_SOURCE_TIRS_STRAY_LIGHT_CORRECTION = "TIRS" - DATA_SOURCE_ELEVATION = "GLS2000" - GROUND_CONTROL_POINTS_VERSION = 5 - GROUND_CONTROL_POINTS_MODEL = 749 - GEOMETRIC_RMSE_MODEL = 6.471 - GEOMETRIC_RMSE_MODEL_Y = 5.198 - GEOMETRIC_RMSE_MODEL_X = 3.855 - GROUND_CONTROL_POINTS_VERIFY = 211 - GEOMETRIC_RMSE_VERIFY = 3.596 - END_GROUP = LEVEL1_PROCESSING_RECORD - GROUP = LEVEL1_MIN_MAX_RADIANCE - RADIANCE_MAXIMUM_BAND_1 = 785.68616 - RADIANCE_MINIMUM_BAND_1 = -64.88222 - RADIANCE_MAXIMUM_BAND_2 = 804.55225 - RADIANCE_MINIMUM_BAND_2 = -66.44019 - RADIANCE_MAXIMUM_BAND_3 = 741.38800 - RADIANCE_MINIMUM_BAND_3 = -61.22406 - RADIANCE_MAXIMUM_BAND_4 = 625.18011 - RADIANCE_MINIMUM_BAND_4 = -51.62758 - RADIANCE_MAXIMUM_BAND_5 = 382.57907 - RADIANCE_MINIMUM_BAND_5 = -31.59350 - RADIANCE_MAXIMUM_BAND_6 = 95.14393 - RADIANCE_MINIMUM_BAND_6 = -7.85702 - RADIANCE_MAXIMUM_BAND_7 = 32.06860 - RADIANCE_MINIMUM_BAND_7 = -2.64824 - RADIANCE_MAXIMUM_BAND_8 = 707.53186 - RADIANCE_MINIMUM_BAND_8 = -58.42821 - RADIANCE_MAXIMUM_BAND_9 = 149.52057 - RADIANCE_MINIMUM_BAND_9 = -12.34746 - RADIANCE_MAXIMUM_BAND_10 = 22.00180 - RADIANCE_MINIMUM_BAND_10 = 0.10033 - RADIANCE_MAXIMUM_BAND_11 = 22.00180 - RADIANCE_MINIMUM_BAND_11 = 0.10033 - END_GROUP = LEVEL1_MIN_MAX_RADIANCE - GROUP = LEVEL1_MIN_MAX_REFLECTANCE - REFLECTANCE_MAXIMUM_BAND_1 = 1.210700 - REFLECTANCE_MINIMUM_BAND_1 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_2 = 1.210700 - REFLECTANCE_MINIMUM_BAND_2 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_3 = 1.210700 - REFLECTANCE_MINIMUM_BAND_3 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_4 = 1.210700 - REFLECTANCE_MINIMUM_BAND_4 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_5 = 1.210700 - REFLECTANCE_MINIMUM_BAND_5 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_6 = 1.210700 - REFLECTANCE_MINIMUM_BAND_6 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_7 = 1.210700 - REFLECTANCE_MINIMUM_BAND_7 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_8 = 1.210700 - REFLECTANCE_MINIMUM_BAND_8 = -0.099980 - REFLECTANCE_MAXIMUM_BAND_9 = 1.210700 - REFLECTANCE_MINIMUM_BAND_9 = -0.099980 - END_GROUP = LEVEL1_MIN_MAX_REFLECTANCE - GROUP = LEVEL1_MIN_MAX_PIXEL_VALUE - QUANTIZE_CAL_MAX_BAND_1 = 65535 - QUANTIZE_CAL_MIN_BAND_1 = 1 - QUANTIZE_CAL_MAX_BAND_2 = 65535 - QUANTIZE_CAL_MIN_BAND_2 = 1 - QUANTIZE_CAL_MAX_BAND_3 = 65535 - QUANTIZE_CAL_MIN_BAND_3 = 1 - QUANTIZE_CAL_MAX_BAND_4 = 65535 - QUANTIZE_CAL_MIN_BAND_4 = 1 - QUANTIZE_CAL_MAX_BAND_5 = 65535 - QUANTIZE_CAL_MIN_BAND_5 = 1 - QUANTIZE_CAL_MAX_BAND_6 = 65535 - QUANTIZE_CAL_MIN_BAND_6 = 1 - QUANTIZE_CAL_MAX_BAND_7 = 65535 - QUANTIZE_CAL_MIN_BAND_7 = 1 - QUANTIZE_CAL_MAX_BAND_8 = 65535 - QUANTIZE_CAL_MIN_BAND_8 = 1 - QUANTIZE_CAL_MAX_BAND_9 = 65535 - QUANTIZE_CAL_MIN_BAND_9 = 1 - QUANTIZE_CAL_MAX_BAND_10 = 65535 - QUANTIZE_CAL_MIN_BAND_10 = 1 - QUANTIZE_CAL_MAX_BAND_11 = 65535 - QUANTIZE_CAL_MIN_BAND_11 = 1 - END_GROUP = LEVEL1_MIN_MAX_PIXEL_VALUE - GROUP = LEVEL1_RADIOMETRIC_RESCALING - RADIANCE_MULT_BAND_1 = 1.2979E-02 - RADIANCE_MULT_BAND_2 = 1.3291E-02 - RADIANCE_MULT_BAND_3 = 1.2247E-02 - RADIANCE_MULT_BAND_4 = 1.0328E-02 - RADIANCE_MULT_BAND_5 = 6.3200E-03 - RADIANCE_MULT_BAND_6 = 1.5717E-03 - RADIANCE_MULT_BAND_7 = 5.2975E-04 - RADIANCE_MULT_BAND_8 = 1.1688E-02 - RADIANCE_MULT_BAND_9 = 2.4700E-03 - RADIANCE_MULT_BAND_10 = 3.3420E-04 - RADIANCE_MULT_BAND_11 = 3.3420E-04 - RADIANCE_ADD_BAND_1 = -64.89520 - RADIANCE_ADD_BAND_2 = -66.45347 - RADIANCE_ADD_BAND_3 = -61.23631 - RADIANCE_ADD_BAND_4 = -51.63791 - RADIANCE_ADD_BAND_5 = -31.59982 - RADIANCE_ADD_BAND_6 = -7.85859 - RADIANCE_ADD_BAND_7 = -2.64877 - RADIANCE_ADD_BAND_8 = -58.43990 - RADIANCE_ADD_BAND_9 = -12.34993 - RADIANCE_ADD_BAND_10 = 0.10000 - RADIANCE_ADD_BAND_11 = 0.10000 - REFLECTANCE_MULT_BAND_1 = 2.0000E-05 - REFLECTANCE_MULT_BAND_2 = 2.0000E-05 - REFLECTANCE_MULT_BAND_3 = 2.0000E-05 - REFLECTANCE_MULT_BAND_4 = 2.0000E-05 - REFLECTANCE_MULT_BAND_5 = 2.0000E-05 - REFLECTANCE_MULT_BAND_6 = 2.0000E-05 - REFLECTANCE_MULT_BAND_7 = 2.0000E-05 - REFLECTANCE_MULT_BAND_8 = 2.0000E-05 - REFLECTANCE_MULT_BAND_9 = 2.0000E-05 - REFLECTANCE_ADD_BAND_1 = -0.100000 - REFLECTANCE_ADD_BAND_2 = -0.100000 - REFLECTANCE_ADD_BAND_3 = -0.100000 - REFLECTANCE_ADD_BAND_4 = -0.100000 - REFLECTANCE_ADD_BAND_5 = -0.100000 - REFLECTANCE_ADD_BAND_6 = -0.100000 - REFLECTANCE_ADD_BAND_7 = -0.100000 - REFLECTANCE_ADD_BAND_8 = -0.100000 - REFLECTANCE_ADD_BAND_9 = -0.100000 - END_GROUP = LEVEL1_RADIOMETRIC_RESCALING - GROUP = LEVEL1_THERMAL_CONSTANTS - K1_CONSTANT_BAND_10 = 774.8853 - K2_CONSTANT_BAND_10 = 1321.0789 - K1_CONSTANT_BAND_11 = 480.8883 - K2_CONSTANT_BAND_11 = 1201.1442 - END_GROUP = LEVEL1_THERMAL_CONSTANTS - GROUP = LEVEL1_PROJECTION_PARAMETERS - MAP_PROJECTION = "UTM" - DATUM = "WGS84" - ELLIPSOID = "WGS84" - UTM_ZONE = 23 - GRID_CELL_SIZE_PANCHROMATIC = 15.00 - GRID_CELL_SIZE_REFLECTIVE = 30.00 - GRID_CELL_SIZE_THERMAL = 30.00 - ORIENTATION = "NORTH_UP" - RESAMPLING_OPTION = "CUBIC_CONVOLUTION" - END_GROUP = LEVEL1_PROJECTION_PARAMETERS -END_GROUP = LANDSAT_METADATA_FILE -END -
    • - -

    The composites have a similar layout as the bands but with an extra -"channel" dimension corresponding to red, green, blue, and -alpha/transparency. The values are scaled to the [0, 255] range and the -composite is an array of unsigned 8-bit integers.

    -
    -

    Transparency

    -

    If any of the bands used for the composite have NaNs, those pixels will -have their transparency set to the maximum value of 255. If there are no -NaNs in any band, then the composite will only have 3 channels (red, green, -blue).

    -
    -

    Now do the same for the after scene:

    -
    -
    -
    after = after.assign(rgb=xls.composite(after, rescale_to=[0.03, 0.2]))
-after = after.assign(
-    cir=xls.composite(after, bands=cir_bands, rescale_to=[0, 0.4]),
-)
-after
-
    -
    -
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:   (easting: 400, northing: 300, channel: 4)
-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
-  * channel   (channel) <U5 'red' 'green' 'blue' 'alpha'
-Data variables:
-    blue      (northing, easting) float16 0.0686 0.07043 ... 0.05823 0.0564
-    green     (northing, easting) float16 0.1027 0.09839 ... 0.07593 0.07043
-    red       (northing, easting) float16 0.09778 0.09778 ... 0.06799 0.06177
-    nir       (northing, easting) float16 0.2988 0.2715 0.2881 ... 0.2637 0.251
-    swir1     (northing, easting) float16 0.2311 0.2274 0.2316 ... 0.1608 0.142
-    swir2     (northing, easting) float16 0.145 0.1442 0.144 ... 0.09961 0.08655
-    rgb       (northing, easting, channel) uint8 101 108 57 255 ... 47 60 39 255
-    cir       (northing, easting, channel) uint8 190 62 65 255 ... 160 39 44 255
-Attributes: (12/19)
-    Conventions:                CF-1.8
-    title:                      Landsat 8 scene from 2019-01-30 (path/row=218...
-    digital_object_identifier:  https://doi.org/10.5066/P9OGBGM6
-    origin:                     Image courtesy of the U.S. Geological Survey
-    landsat_product_id:         LC08_L2SP_218074_20190130_20200829_02_T1
-    processing_level:           L2SP
-    ...                         ...
-    ellipsoid:                  WGS84
-    date_acquired:              2019-01-30
-    scene_center_time:          12:57:09.1851220Z
-    wrs_path:                   218
-    wrs_row:                    74
-    mtl_file:                   GROUP = LANDSAT_METADATA_FILE\n  GROUP = PROD...
    +plt.show() +
    + +
    +_images/composites_2_0.png +
    + +

    Well, this looks bad because that bright cloud is making it so the ground +pixels have only a small share of the full range of available values. This can +be mitigated by rescaling the intensity of the image to a smaller range of +reflectance values.

    -
    -

    Plotting composites#

    -

    Composites can be plotted using xarray.DataArray.plot.imshow (using -plot won’t work and will display histograms instead). -Let’s make the before and after figures again for each of the composites we -generated.

    +
    +

    Rescaling intensity (AKA contrast stretching)#

    +

    We rescale the intensities of a composite to a given reflectance range by +setting the rescale_to parameter when creating a composite. It takes a list +of the min and max reflectance values allowed. For this image, we can arrive at +the following values by trial and error until it looks nice:

    -
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
-
-# Plot the composites
-before.rgb.plot.imshow(ax=ax1)
-after.rgb.plot.imshow(ax=ax2)
-
-# The "long_name" of the composite is the band combination
-ax1.set_title(f"Before: {before.rgb.attrs['long_name']}")
-ax2.set_title(f"After: {after.rgb.attrs['long_name']}")
-
-ax1.set_aspect("equal")
-ax2.set_aspect("equal")
+
    rgb = xls.composite(scene, rescale_to=[0.03, 0.2])
 
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+rgb.plot.imshow(ax=ax)
+ax.set_title(f"Rescaled composite: {rgb.attrs['long_name']}")
+ax.set_aspect("equal")
 plt.show()
 
    
 
    
@@ -3761,20 +1486,30 @@ 
    Plotting composites
    -

    And now the CIR composites:

    +

    Notice that we can more clearly see the colors of the ground but we lose a lot +of detail in the clouds.

    +
    +

    Note

    +

    The rescaling has to be done when creating the composite so that we can use +min/max values in reflectance units. After a composite is created, the +original range of values is lost and we’d have to specify the min/max +between 0 and 255 instead.

    +
    +
    +
    +

    Color infrared composites#

    +

    Another common type of composite is the color infrared (CIR) composites. These +change the bands used to NIR, red, and green and serve primarily to distinguish +healthy vegetation from other objects in the scene. Let’s make one by specifying +this band combination to xlandsat.composite to see if we can more clearly spot the dam flood.

    -
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
-
-before.cir.plot.imshow(ax=ax1)
-after.cir.plot.imshow(ax=ax2)
-
-ax1.set_title(f"Before: {before.cir.attrs['long_name']}")
-ax2.set_title(f"After: {after.cir.attrs['long_name']}")
-
-ax1.set_aspect("equal")
-ax2.set_aspect("equal")
+
    cir = xls.composite(scene, bands=("nir", "red", "green"), rescale_to=[0, 0.4])
 
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+cir.plot.imshow(ax=ax)
+ax.set_title(f"Composite: {rgb.attrs['long_name']}")
+ax.set_aspect("equal")
 plt.show()
 
    
 
    
@@ -3783,6 +1518,10 @@ 
    Plotting composites
    +

    The flood region can be clearly spotted in the image above as the brown/gray +blog in the center.

    +

    With this, you can now make composites using any other band combination you +may want!

    @@ -3816,7 +1555,7 @@

    Plotting composites

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    diff --git a/dev/equalize-histogram.html b/dev/equalize-histogram.html new file mode 100644 index 0000000..a4040f9 --- /dev/null +++ b/dev/equalize-histogram.html @@ -0,0 +1,642 @@ + + + + + + + + + + + Histogram equalization | xlandsat v0.0.post58+geb1d62a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    + + + + + + + + + + +
    + +
    + +
    + + + + +
    +
    + + + + +
    +
    + + +
    + +
    + +
    +
    + +
    + +
    + +
    +
    + +
    +
    + + +
    +
    +
    +
    +
    + +
    +

    Histogram equalization

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

    Histogram equalization#

    +

    Scenes with very dark or very bright spots (like clouds) can be difficult to +visualize without some sort of contrast enhancement when generating composites. +The simplest enhancement is to stretch the contrast linearly, but doing so +erases information in the very dark/light regions and won’t always work. An +alternative is to use histogram equalization, which is implemented in +xlandsat.equalize_histogram.

    +

    Let’s use our sample scene of October 2015 around Mount Roraima to demonstrate how it’s done. +The tepui, as it’s called, is famous for it’s near constant cloud coverage an +will make a good target for this example.

    +

    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_roraima()
+scene = xls.load_scene(path)
+
    +
    +
    +
    +
    +
    +

    Histogram equalization doesn’t work well with missing data, which this dataset +has, and so we need to first fill in the missing values through interpolation +with xlandsat.interpolate_missing:

    +
    +
    +
    scene = xls.interpolate_missing(scene)
+
    +
    +
    +
    +
    +
    +

    Once that’s done, we can make an RGB composite with no enhancements as a basis +for comparison:

    +
    +
    +
    rgb = xls.composite(scene)
+
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+rgb.plot.imshow(ax=ax)
+ax.set_aspect("equal")
+plt.show()
+
    +
    +
    +
    +_images/equalize-histogram_2_0.png +
    +
    +

    Notice how the clouds dominate the intensity range, making it difficult to make +out features of the tepui and the surrounding forest.

    +

    Now we can do our best to stretch the contrast so that we can see more detail +in the cloud-free regions:

    +
    +
    +
    rgb_strech = xls.composite(scene, rescale_to=(0, 0.28))
+
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+rgb_strech.plot.imshow(ax=ax)
+ax.set_aspect("equal")
+plt.show()
+
    +
    +
    +
    +_images/equalize-histogram_3_0.png +
    +
    +

    But, as we mentioned earlier, this means we don’t get to see details of the +clouds anymore. For a more pleasing image, we can use the adaptive histogram +equalization in xlandsat.equalize_histogram. It helps to do a bit of +contrast stretching first, but to a lesser degree than we did previously.

    +
    +
    +
    rgb = xls.composite(scene, rescale_to=(0, 0.6))
+rgb_eq = xls.equalize_histogram(rgb, clip_limit=0.02, kernel_size=300)
+
+fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+rgb_eq.plot.imshow(ax=ax)
+ax.set_aspect("equal")
+plt.show()
+
    +
    +
    +
    +_images/equalize-histogram_4_0.png +
    +
    +

    Now that’s a much better visualization, we can see details in the clouds, +mountains, and forests!

    +
    +

    Note

    +

    Notice that xlandsat.equalize_histogram must be given a +composite instead of the scene.

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

    + + © Copyright 2023, The xlandsat developers.
    + Last updated on Sep 20, 2023.
    +

    +
    +
    + +
    + + +
    +
    + + + + + + + \ No newline at end of file diff --git a/dev/genindex.html b/dev/genindex.html index cdb0ebc..56c1fc5 100644 --- a/dev/genindex.html +++ b/dev/genindex.html @@ -7,7 +7,7 @@ - Index | xlandsat v0.0.post36 + Index | xlandsat v0.0.post58+geb1d62a @@ -81,9 +81,14 @@
    + + + + -

    xlandsat v0.0.post36

    + +

    xlandsat v0.0.post58+geb1d62a
    @@ -129,6 +134,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -171,6 +191,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -191,6 +221,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -221,10 +266,20 @@

    xlandsat v0.

    - Links + Community

    +

    E

    + + +
    +

    F

    - + +
    + +

    I

    + +
    @@ -487,7 +566,7 @@

    X

    © Copyright 2023, The xlandsat developers.
    - Last updated on Feb 08, 2023.
    + Last updated on Sep 20, 2023.

    • diff --git a/dev/index.html b/dev/index.html index 2c4c8be..a39ecea 100644 --- a/dev/index.html +++ b/dev/index.html @@ -8,7 +8,7 @@ - xlandsat v0.0.post36 + xlandsat v0.0.post58+geb1d62a @@ -83,9 +83,14 @@
    + + + + + -

    xlandsat v0.0.post36

    +

    xlandsat v0.0.post58+geb1d62a
    @@ -131,6 +136,21 @@

    xlandsat v0. Pansharpening +
  • + + Filling missing values + +
    • +
  • + + Histogram equalization + +
    • +
  • + + Plotting bands overlaid on composites + +

    • @@ -173,6 +193,16 @@

    xlandsat v0. xlandsat.pansharpen +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -193,6 +223,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -223,10 +268,20 @@

    xlandsat v0.

    - Links + Community

      +
    • + + How to contribute + +
      • +
    • + + Code of Conduct + +
    • Source code on GitHub @@ -472,35 +527,36 @@

      xlandsat

      xlandsat is Python library for loading Landsat scenes downloaded from USGS EarthExplorer into -xarray.Dataset containers. +xarray.Dataset containers. We take care of reading the metadata from the *_MTL.txt files provided by EarthExplorer and organizing the bands into a single data structure for easier manipulation, processing, and visualization.

      Here’s a quick example:

      -
      import xlandsat as xls
+
      import xlandsat as xls
+import matplotlib.pyplot as plt
 
-# Download a cropped Landsat 8 scene from the Brumadinho dam disaster
-# (Brazil). The function downloads it and returns the path to the .tar file
-# containing the scene.
-path = xls.datasets.fetch_brumadinho_after()
+# Download a sample Landsat 9 scene in EarthExplorer format
+path_to_scene_file = xls.datasets.fetch_manaus()
 
-# Load the scene directly from the archive (no need to unpack it)
-scene = xls.load_scene(path)
+# Load the data from the file into an xarray.Dataset
+scene = xls.load_scene(path_to_scene_file)
 
-# Make an RGB composite and stretch the contrast
-rgb = xls.composite(scene, rescale_to=[0.03, 0.2])
+# Make an RGB composite as an xarray.DataArray
+rgb = xls.composite(scene, rescale_to=[0.02, 0.2])
 
-# Plot the composite
+# Plot the composite using xarray's plotting machinery
 rgb.plot.imshow()
+
+# Annotate the plot with the rich metadata xlandsat adds to the scene
+plt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}")
+plt.axis("scaled")
+plt.show()
      -
      <matplotlib.image.AxesImage at 0x7fb2e641e7d0>
-
      -
      _images/index_0_1.png
      @@ -552,8 +608,8 @@

      xlandsat

      Note

      -

      Only Landsat 8 and 9 Collection 2 Level 2 data products are supported at -the moment.

      +

      Only Landsat 8 and 9 Level 1 & 2 data products have been tested at the +moment.

      xlandsat is ready for use but still changing

      @@ -567,10 +623,13 @@

      xlandsat

      Looking for large-scale cloud-based processing?

      -

      Our goal is not to provide a solution for large-scale data processing. The -target is smaller scale analysis done on individual computers (which is -probably the main way EarthExplorer is used). For cloud-based data -processing, see the Pangeo Project.

      +

      Our goal is not to provide a solution for large-scale data processing. +Our target is smaller scale analysis done on individual computers (which is +probably the main way EarthExplorer is used).

      +
        +

      • For cloud-based data processing, see the Pangeo Project.

        • +

      • For other satellites and more powerful features, use Satpy.

        • +
      @@ -604,7 +663,7 @@

      xlandsat

      © Copyright 2023, The xlandsat developers.
      - Last updated on Feb 08, 2023.
      + Last updated on Sep 20, 2023.

      diff --git a/dev/indices.html b/dev/indices.html index 1c7b021..0cd6cbe 100644 --- a/dev/indices.html +++ b/dev/indices.html @@ -8,7 +8,7 @@ - Indices | xlandsat v0.0.post36 + Indices | xlandsat v0.0.post58+geb1d62a @@ -84,9 +84,14 @@
      + + + + -

      xlandsat v0.0.post36

      + +

      xlandsat v0.0.post58+geb1d62a
      @@ -132,6 +137,21 @@

      xlandsat v0. Pansharpening

      • +
    • + + Filling missing values + +
      • +
    • + + Histogram equalization + +
      • +
    • + + Plotting bands overlaid on composites + +

    @@ -174,6 +194,16 @@

    xlandsat v0. xlandsat.pansharpen

    • +
  • + + xlandsat.equalize_histogram + +
    • +
  • + + xlandsat.interpolate_missing + +
  • xlandsat.datasets.fetch_brumadinho_after @@ -194,6 +224,21 @@

    xlandsat v0. xlandsat.datasets.fetch_liverpool_panchromatic

    • +
  • + + xlandsat.datasets.fetch_manaus + +
    • +
  • + + xlandsat.datasets.fetch_momotombo + +
    • +
  • + + xlandsat.datasets.fetch_roraima + +
  • @@ -224,10 +269,20 @@

    xlandsat v0.

    - Links + Community

    • easting
      (easting)
      float64
      5.835e+05 5.835e+05 ... 5.955e+05
      long_name :
      UTM easting
      standard_name :
      projection_x_coordinate
      units :
      m
      array([583500., 583530., 583560., ..., 595410., 595440., 595470.])
    • northing
      (northing)
      float64
      -2.232e+06 ... -2.223e+06
      long_name :
      UTM northing
      standard_name :
      projection_y_coordinate
      units :
      m
      array([-2231670., -2231640., -2231610., ..., -2222760., -2222730., -2222700.])
    • easting
      PandasIndex
      PandasIndex(Index([583500.0, 583530.0, 583560.0, 583590.0, 583620.0, 583650.0, 583680.0,
+       583710.0, 583740.0, 583770.0,
+       ...
+       595200.0, 595230.0, 595260.0, 595290.0, 595320.0, 595350.0, 595380.0,
+       595410.0, 595440.0, 595470.0],
+      dtype='float64', name='easting', length=400))
    • northing
      PandasIndex
      PandasIndex(Index([-2231670.0, -2231640.0, -2231610.0, -2231580.0, -2231550.0, -2231520.0,
+       -2231490.0, -2231460.0, -2231430.0, -2231400.0,
+       ...
+       -2222970.0, -2222940.0, -2222910.0, -2222880.0, -2222850.0, -2222820.0,
+       -2222790.0, -2222760.0, -2222730.0, -2222700.0],
+      dtype='float64', name='northing', length=300))
  • long_name :
    normalized difference vegetation index
    units :
    dimensionless

    • And now we can make pseudo-color plots of the NDVI:

    -
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
+
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
 
 # Limit the scale to [-1, +1] so the plots are easier to compare
-before.ndvi.plot(ax=ax1, vmin=-1, vmax=1, cmap="RdBu_r")
-after.ndvi.plot(ax=ax2, vmin=-1, vmax=1, cmap="RdBu_r")
+ndvi_before.plot(ax=ax1, vmin=-1, vmax=1, cmap="RdBu_r")
+ndvi_after.plot(ax=ax2, vmin=-1, vmax=1, cmap="RdBu_r")
 
 ax1.set_title(f"Before: {before.attrs['title']}")
 ax2.set_title(f"After: {after.attrs['title']}")
@@ -1368,7 +1009,7 @@ 
    NDVI#<
 taking the difference:
    
 
    
 
    
-
    ndvi_change = before.ndvi - after.ndvi
+
    ndvi_change = ndvi_before - ndvi_after
 ndvi_change.name = "ndvi_change"
 ndvi_change.attrs["long_name"] = (
     f"NDVI change between {before.attrs['date_acquired']} and "
@@ -1760,7 +1401,7 @@ 
    NDVI#<
   * 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 between 2019-01-14 and 2019-01-30
    
+         0.0105  ]], dtype=float16)
    • easting
      (easting)
      float64
      5.844e+05 5.844e+05 ... 5.955e+05
      long_name :
      UTM easting
      standard_name :
      projection_x_coordinate
      units :
      m
      array([584400., 584430., 584460., ..., 595410., 595440., 595470.])
    • northing
      (northing)
      float64
      -2.232e+06 ... -2.223e+06
      long_name :
      UTM northing
      standard_name :
      projection_y_coordinate
      units :
      m
      array([-2231670., -2231640., -2231610., ..., -2222760., -2222730., -2222700.])
    • easting
      PandasIndex
      PandasIndex(Index([584400.0, 584430.0, 584460.0, 584490.0, 584520.0, 584550.0, 584580.0,
+       584610.0, 584640.0, 584670.0,
+       ...
+       595200.0, 595230.0, 595260.0, 595290.0, 595320.0, 595350.0, 595380.0,
+       595410.0, 595440.0, 595470.0],
+      dtype='float64', name='easting', length=370))
    • northing
      PandasIndex
      PandasIndex(Index([-2231670.0, -2231640.0, -2231610.0, -2231580.0, -2231550.0, -2231520.0,
+       -2231490.0, -2231460.0, -2231430.0, -2231400.0,
+       ...
+       -2222970.0, -2222940.0, -2222910.0, -2222880.0, -2222850.0, -2222820.0,
+       -2222790.0, -2222760.0, -2222730.0, -2222700.0],
+      dtype='float64', name='northing', length=300))
  • long_name :
    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 +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.

    @@ -1795,7 +1436,7 @@

    NDVI#<

    Now lets plot it:

    -
    fig, ax = plt.subplots(1, 1, figsize=(10, 6))
+
    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()
@@ -1808,7 +1449,7 @@ 
    NDVI#<
 
    
 
    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.
    
+collapse in purple at the center.
    
 
 
 
@@ -1841,7 +1482,7 @@ 
    NDVI#<
         
    
   
       © Copyright 2023, The xlandsat developers.
    
-    Last updated on Feb 08, 2023.
    
+    Last updated on Sep 20, 2023.
    
 
    
         
    
     
    
diff --git a/dev/install.html b/dev/install.html
index 6779862..0a94956 100644
--- a/dev/install.html
+++ b/dev/install.html
@@ -8,7 +8,7 @@
     
 
     
-        Installing | xlandsat v0.0.post36
+        Installing | xlandsat v0.0.post58+geb1d62a
     
 
     
@@ -84,9 +84,14 @@
         
    
     
       
+        
+        
+      
+      
+      
       
       
-      
    xlandsat v0.0.post36
    
+      
    xlandsat v0.0.post58+geb1d62a
    
       
         
 
    
@@ -132,6 +137,21 @@ 
    xlandsat v0.
    Pansharpening
   
  
+ 
  • 
+  
+   Filling missing values
+  
+ 
    • 
+ 
  • 
+  
+   Histogram equalization
+  
+ 
    • 
+ 
  • 
+  
+   Plotting bands overlaid on composites
+  
+ 
    • 
 
 
    
  
@@ -174,6 +194,16 @@ 
    xlandsat v0.
      xlandsat.pansharpen
     
    
+   
  • 
+    
+     xlandsat.equalize_histogram
+    
+   
    • 
+   
  • 
+    
+     xlandsat.interpolate_missing
+    
+   
    • 
    
  • 
     
      xlandsat.datasets.fetch_brumadinho_after
@@ -194,6 +224,21 @@ 
    xlandsat v0.
      xlandsat.datasets.fetch_liverpool_panchromatic
     
    
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_manaus
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_momotombo
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_roraima
+    
+   
    • 
   
  
  
  • 
@@ -224,10 +269,20 @@ 
    xlandsat v0.
 
 
    
  
-  Links
+  Community
    • - -

    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)
@@ -2494,10 +2549,10 @@ 
    Pansharpening
    
             
    Indices
    
         
    
     
-    
+    
     
    
         
    next
    
-        
    List of functions and classes (API)
    
+        
    Filling missing values
    
     
    
     
         
@@ -2509,7 +2564,7 @@ 
    Pansharpening
    
         
    
   
       © Copyright 2023, The xlandsat developers.
    
-    Last updated on Feb 08, 2023.
    
+    Last updated on Sep 20, 2023.
    
 
    
         
    
     
    
diff --git a/dev/plot-overlay.html b/dev/plot-overlay.html
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+++ b/dev/plot-overlay.html
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+
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+        Plotting bands overlaid on composites | xlandsat v0.0.post58+geb1d62a
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+                
    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
+
    
+
    
+
    
+
    
+
    
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+
    <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.
    
+
    
+
+
+              
    
+              
+            
    
+            
+        
    
+    
    
+    
    
+        
    
+  
+      © Copyright 2023, The xlandsat developers.
    
+    Last updated on Sep 20, 2023.
    
+
    
+        
    
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\ No newline at end of file
diff --git a/dev/py-modindex.html b/dev/py-modindex.html
index c488e52..e27ce67 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.post58+geb1d62a
     
 
     
@@ -84,9 +84,14 @@
         
    
     
       
+        
+        
+      
+      
+      
       
       
-      
    xlandsat v0.0.post36
    
+      
    xlandsat v0.0.post58+geb1d62a
    
       
         
 
    
@@ -132,6 +137,21 @@ 
    xlandsat v0.
    Pansharpening
   
  
+ 
  • 
+  
+   Filling missing values
+  
+ 
    • 
+ 
  • 
+  
+   Histogram equalization
+  
+ 
    • 
+ 
  • 
+  
+   Plotting bands overlaid on composites
+  
+ 
    • 
 
 
    
  
@@ -174,6 +194,16 @@ 
    xlandsat v0.
      xlandsat.pansharpen
     
    
+   
  • 
+    
+     xlandsat.equalize_histogram
+    
+   
    • 
+   
  • 
+    
+     xlandsat.interpolate_missing
+    
+   
    • 
    
  • 
     
      xlandsat.datasets.fetch_brumadinho_after
@@ -194,6 +224,21 @@ 
    xlandsat v0.
      xlandsat.datasets.fetch_liverpool_panchromatic
     
    
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_manaus
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_momotombo
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_roraima
+    
+   
    • 
   
  
  
  • 
@@ -224,10 +269,20 @@ 
    xlandsat v0.
 
 
    
  
-  Links
+  Community
  
 
    
 
    • 
diff --git a/dev/references.html b/dev/references.html
index f32c617..e56e2b1 100644
--- a/dev/references.html
+++ b/dev/references.html
@@ -8,7 +8,7 @@
     
 
     
-        References | xlandsat v0.0.post36
+        References | xlandsat v0.0.post58+geb1d62a
     
 
     
@@ -84,9 +84,14 @@
         
    
     
       
+        
+        
+      
+      
+      
       
       
-      
    xlandsat v0.0.post36
    
+      
    xlandsat v0.0.post58+geb1d62a
    
       
         
 
    
@@ -132,6 +137,21 @@ 
    xlandsat v0.
    Pansharpening
   
  
+ 
  • 
+  
+   Filling missing values
+  
+ 
    • 
+ 
  • 
+  
+   Histogram equalization
+  
+ 
    • 
+ 
  • 
+  
+   Plotting bands overlaid on composites
+  
+ 
    • 
 
 
    
  
@@ -174,6 +194,16 @@ 
    xlandsat v0.
      xlandsat.pansharpen
     
    
+   
  • 
+    
+     xlandsat.equalize_histogram
+    
+   
    • 
+   
  • 
+    
+     xlandsat.interpolate_missing
+    
+   
    • 
    
  • 
     
      xlandsat.datasets.fetch_brumadinho_after
@@ -194,6 +224,21 @@ 
    xlandsat v0.
      xlandsat.datasets.fetch_liverpool_panchromatic
     
    
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_manaus
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_momotombo
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_roraima
+    
+   
    • 
   
  
  
  • 
@@ -224,10 +269,20 @@ 
    xlandsat v0.
 
 
    
  
-  Links
+  Community
  
 
    
 
    • 
diff --git a/dev/search.html b/dev/search.html
index 4d23128..8f2c689 100644
--- a/dev/search.html
+++ b/dev/search.html
@@ -7,7 +7,7 @@
     
     
     
-        Search | xlandsat v0.0.post36
+        Search | xlandsat v0.0.post58+geb1d62a
     
 
     
@@ -87,9 +87,14 @@
         
    
     
       
+        
+        
+      
+      
+      
       
       
-      
    xlandsat v0.0.post36
    
+      
    xlandsat v0.0.post58+geb1d62a
    
       
         
 
    
@@ -135,6 +140,21 @@ 
    xlandsat v0.
    Pansharpening
   
  
+ 
  • 
+  
+   Filling missing values
+  
+ 
    • 
+ 
  • 
+  
+   Histogram equalization
+  
+ 
    • 
+ 
  • 
+  
+   Plotting bands overlaid on composites
+  
+ 
    • 
 
 
    
  
@@ -177,6 +197,16 @@ 
    xlandsat v0.
      xlandsat.pansharpen
     
    
+   
  • 
+    
+     xlandsat.equalize_histogram
+    
+   
    • 
+   
  • 
+    
+     xlandsat.interpolate_missing
+    
+   
    • 
    
  • 
     
      xlandsat.datasets.fetch_brumadinho_after
@@ -197,6 +227,21 @@ 
    xlandsat v0.
      xlandsat.datasets.fetch_liverpool_panchromatic
     
    
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_manaus
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_momotombo
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_roraima
+    
+   
    • 
   
  
  
  • 
@@ -227,10 +272,20 @@ 
    xlandsat v0.
 
 
    
  
-  Links
+  Community
  
 
    
 
    • 
diff --git a/dev/searchindex.js b/dev/searchindex.js
index 0b2abc8..7613de7 100644
--- a/dev/searchindex.js
+++ b/dev/searchindex.js
@@ -1 +1 @@
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18,20,22],your:[16,24],zenodo:[15,16]},titles:["xlandsat.composite","xlandsat.datasets.fetch_brumadinho_after","xlandsat.datasets.fetch_brumadinho_before","xlandsat.datasets.fetch_liverpool","xlandsat.datasets.fetch_liverpool_panchromatic","xlandsat.datasets.fetch_manaus","xlandsat.datasets.fetch_momotombo","xlandsat.datasets.fetch_roraima","xlandsat.equalize_histogram","xlandsat.interpolate_missing","xlandsat.load_panchromatic","xlandsat.load_scene","xlandsat.pansharpen","xlandsat.save_scene","List of functions and classes (API)","Changelog","Citing","Version compatibility","Composites","Histogram equalization","Home","Indices","Installing","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:24,aka:18,api:14,band:[24,26],base:20,chang:20,changelog:15,cite:16,cloud:20,color:18,compat:17,composit:[0,18,26],contrast:18,dataset:[1,2,3,4,5,6,7,14],depend:[17,22],did:[21,24],document:28,download:24,equal:19,equalize_histogram:8,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:23,histogram:19,indic:21,infrar:18,input:14,instal:22,intens:18,interpolate_miss:9,larg:20,librari:24,list:14,load:24,load_panchromat:10,load_scen:11,look:20,miss:23,ndvi:21,notic:[21,24],now:24,other:28,output:14,overlaid:26,overview:24,pansharpen:[12,25],plot:[18,24,26],process:[14,20],python:[17,22],readi:20,refer:27,reflect:24,rescal:18,rgb:18,sampl:[14,24],save_scen:13,scale:20,scene:24,some:24,still:20,stretch:18,support:17,transpar:18,us:20,valu:23,version:[15,17,28],what:24,which:22,xlandsat:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,17,20],you:[21,24]}})
\ No newline at end of file
diff --git a/dev/versions.html b/dev/versions.html
index a3a04a4..40084e9 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.post58+geb1d62a
     
 
     
@@ -83,9 +83,14 @@
         
    
     
       
+        
+        
+      
+      
+      
       
       
-      
    xlandsat v0.0.post36
    
+      
    xlandsat v0.0.post58+geb1d62a
    
       
         
 
    
@@ -131,6 +136,21 @@ 
    xlandsat v0.
    Pansharpening
   
  
+ 
  • 
+  
+   Filling missing values
+  
+ 
    • 
+ 
  • 
+  
+   Histogram equalization
+  
+ 
    • 
+ 
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+  
+   Plotting bands overlaid on composites
+  
+ 
    • 
 
 
    
  
@@ -173,6 +193,16 @@ 
    xlandsat v0.
      xlandsat.pansharpen
     
    
+   
  • 
+    
+     xlandsat.equalize_histogram
+    
+   
    • 
+   
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+    
+     xlandsat.interpolate_missing
+    
+   
    • 
    
  • 
     
      xlandsat.datasets.fetch_brumadinho_after
@@ -193,6 +223,21 @@ 
    xlandsat v0.
      xlandsat.datasets.fetch_liverpool_panchromatic
     
    
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_manaus
+    
+   
    • 
+   
  • 
+    
+     xlandsat.datasets.fetch_momotombo
+    
+   
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+   
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+     xlandsat.datasets.fetch_roraima
+    
+   
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@@ -223,10 +268,20 @@ 
    xlandsat v0.
 
 
    
  
-  Links
+  Community
  
 
    
 
    •