- Describe the impacts of cloud cover on analysis of remote sensing data.
- Use a mask to remove portions of an spectral dataset (image) that is covered by clouds / shadows.
- Define mask / describe how a mask can be useful when working with remote sensing data.
About Landsat Scenes
Landsat satellites orbit the earth continuously collecting images of the Earth’s surface. These images, are divided into smaller regions - known as scenes.
Landsat images are usually divided into scenes for easy downloading. Each Landsat scene is about 115 miles long and 115 miles wide (or 100 nautical miles long and 100 nautical miles wide, or 185 kilometers long and 185 kilometers wide). -wikipedia
Challenges Working with Landsat Remote Sensing Data
In the previous lessons, you learned how to import a set of geotiffs that made up the bands of a Landsat raster. Each geotiff file was a part of a Landsat scene, that had been downloaded for this class by your instructor. The scene was further cropped to reduce the file size for the class.
You ran into some challenges when you began to work with the data. The biggest problem was a large cloud and associated shadow that covered your study area of interest - the Cold Springs fire burn scar.
Work with Clouds, Shadows and Bad Pixels in Remote Sensing Data
Clouds and atmospheric conditions present a significant challenge when working with multispectral remote sensing data. Extreme cloud cover and shadows can make the data in those areas, un-usable given reflectance values are either washed out (too bright - as the clouds scatter all light back to the sensor) or are too dark (shadows which represent blocked or absorbed light).
In this lesson you will learn how to deal with clouds in your remote sensing data. There is no perfect solution of course. You will just learn one approach.
import os from glob import glob import matplotlib.pyplot as plt from matplotlib import patches as mpatches, colors from matplotlib.colors import ListedColormap import seaborn as sns import numpy as np import numpy.ma as ma import pandas as pd import rasterio as rio from rasterio.plot import plotting_extent from rasterio.mask import mask import geopandas as gpd from shapely.geometry import mapping import earthpy as et import earthpy.spatial as es import earthpy.plot as ep import earthpy.mask as em # Prettier plotting with seaborn sns.set_style('white') sns.set(font_scale=1.5) # Download data and set working directory data = et.data.get_data('cold-springs-fire') os.chdir(os.path.join(et.io.HOME, 'earth-analytics'))
Next, you will load and plot landsat data. If you are completing the earth analytics course, you have worked with these data already in your homework.
landsat_paths_pre_path = os.path.join("data", "cold-springs-fire", "landsat_collect", "LC080340322016070701T1-SC20180214145604", "crop", "*band*.tif") landsat_paths_pre = glob(landsat_paths_pre_path) landsat_paths_pre.sort() # Stack the Landsat pre fire data landsat_pre_st_path = os.path.join("data", "cold-springs-fire", "outputs", "landsat_pre_st.tif") es.stack(landsat_paths_pre, landsat_pre_st_path) # Read landsat pre fire data with rio.open(landsat_pre_st_path) as landsat_pre_src: landsat_pre = landsat_pre_src.read(masked=True) landsat_extent = plotting_extent(landsat_pre_src) ep.plot_rgb(landsat_pre, rgb=[3, 2, 1], extent=landsat_extent, title="Landsat True Color Composite Image | 30 meters \n Post Cold Springs Fire \n July 8, 2016") plt.show()
Notice in the data above there is a large cloud in your scene. This cloud will impact any quantitative analysis that you perform on the data. You can remove cloudy pixels using a mask. Masking “bad” pixels:
- Allows you to remove them from any quantitative analysis that you may perform such as calculating NDVI.
- Allows you to replace them (if you want) with better pixels from another scene. This replacement if often performed when performing time series analysis of data. The following lesson will teach you have to replace pixels in a scene.
Cloud Masks in Python
You can use the cloud mask layer to identify pixels that are likely to be clouds or shadows. You can then set those pixel values to
masked so they are not included in your quantitative analysis in Python.
When you say “mask”, you are talking about a layer that “turns off” or sets to
nan, the values of pixels in a raster that you don’t want to include in an analysis. It’s very similar to setting data points that equal -9999 to
nan in a time series data set. You are just doing it with spatial raster data instead.
The code below demonstrated how to mask a landsat scene using the pixel_qa layer.
Raster Masks for Remote Sensing Data
Many remote sensing data sets come with quality layers that you can use as a mask to remove “bad” pixels from your analysis. In the case of Landsat, the mask layers identify pixels that are likely representative of cloud cover, shadow and even water. When you download Landsat 8 data from Earth Explorer, the data came with a processed cloud shadow / mask raster layer called
landsat_file_name_pixel_qa.tif. Just replace the name of your Landsat scene with the text landsat_file_name above. For this class the layer is:
You will explore using this pixel quality assurance (QA) layer, next. To begin, open the
pixel_qa layer using rasterio and plot it with matplotlib.
landsat_pre_cl_path = os.path.join("data", "cold-springs-fire", "landsat_collect", "LC080340322016070701T1-SC20180214145604", "crop", "LC08_L1TP_034032_20160707_20170221_01_T1_pixel_qa_crop.tif") # Open the pixel_qa layer for your landsat scene with rio.open(landsat_pre_cl_path) as landsat_pre_cl: landsat_qa = landsat_pre_cl.read(1) landsat_ext = plotting_extent(landsat_pre_cl)
First, plot the pixel_qa layer in matplotlib.
# This is optional code to plot the qa layer - don't worry too much about the details. # Create a colormap with 11 colors cmap = plt.cm.get_cmap('tab20b', 11) # Get a list of unique values in the qa layer vals = np.unique(landsat_qa).tolist() bins =  + vals # Normalize the colormap bounds = [((a + b) / 2) for a, b in zip(bins[:-1], bins[1::1])] + \ [(bins[-1] - bins[-2]) + bins[-1]] norm = colors.BoundaryNorm(bounds, cmap.N) # Plot the data fig, ax = plt.subplots(figsize=(12, 8)) im = ax.imshow(landsat_qa, cmap=cmap, norm=norm) ep.draw_legend(im, classes=vals, cmap=cmap, titles=vals) ax.set_title("Landsat Collection Quality Assessment Layer") ax.set_axis_off() plt.show()
In the image above, you can see the cloud and the shadow that is obstructing our landsat image. Unfortunately for you, this cloud covers a part of your analysis area in the Cold Springs Fire location. There are a few ways to handle this issue. We will look at one: simply masking out or removing the cloud for your analysis, first.
To remove all pixels that are cloud and cloud shadow covered we need to first determine what each value in our qa raster represents. The table below is from the USGS landsat website. It describes what all of the values in the pixel_qa layer represent.
We are interested in
- cloud shadow
- cloud and
- high confidence cloud
Note that your specific analysis may require a different set of masked pixels. For instance, your analysis may require you identify pixels that are low confidence clouds too. We are just using these classes for the purpose of this class.
|Clear||322, 386, 834, 898, 1346|
|Water||324, 388, 836, 900, 1348|
|Cloud Shadow||328, 392, 840, 904, 1350|
|Snow/Ice||336, 368, 400, 432, 848, 880, 912, 944, 1352|
|Cloud||352, 368, 416, 432, 480, 864, 880, 928, 944, 992|
|Low confidence cloud||322, 324, 328, 336, 352, 368, 834, 836, 840, 848, 864, 880|
|Medium confidence cloud||386, 388, 392, 400, 416, 432, 900, 904, 928, 944|
|High confidence cloud||480, 992|
|Low confidence cirrus||322, 324, 328, 336, 352, 368, 386, 388, 392, 400, 416, 432, 480|
|High confidence cirrus||834, 836, 840, 848, 864, 880, 898, 900, 904, 912, 928, 944, 992|
|Terrain occlusion||1346, 1348, 1350, 1352|
To better understand the values above, create a better map of the data. To do that you will:
- classify the data into x classes where x represents the total number of unique values in the
- plot the data using these classes.
We are reclassifying the data because matplotlib colormaps will assign colors to values along a continuous gradient. Reclassifying the data allows us to enforce one color for each unique value in our data.
This next section shows you how to create a mask using the earthpy mask helper function
_create_mask to create a binary cloud mask layer. In this mask all pixels that you wish to remove from your analysis or mask will be set to
1. All other pixels which represent pixels you want to use in your analysis will be set to
This step can be done by changing the inputs into the main
mask_pixels function. We include it here so you can see what is going on in the function. See lower down in the lesson for this call.
[322, 324, 328, 352, 386, 416, 480, 834, 864, 928, 992]
# You can grab the cloud pixel values from earthpy high_cloud_confidence = em.pixel_flags["pixel_qa"]["L8"]["High Cloud Confidence"] cloud = em.pixel_flags["pixel_qa"]["L8"]["Cloud"] cloud_shadow = em.pixel_flags["pixel_qa"]["L8"]["Cloud Shadow"] all_masked_values = cloud_shadow + cloud + high_cloud_confidence all_masked_values
[328, 392, 840, 904, 1350, 352, 368, 416, 432, 480, 864, 880, 928, 944, 992, 480, 992]
# This is using a helper function from earthpy to create the mask so we can plot it # You don't need to do this in your workflow as you can perform the mask in one step # But we have it here for demonstration purposes cl_mask = em._create_mask(landsat_qa, all_masked_values) np.unique(cl_mask)
array([0, 1], dtype=int16)
Below is the plot of the reclassified raster mask created from the
_create_mask helper function.
What Does the Metadata Tell You?
You just explored two layers that potentially have information about cloud cover. However what do the values stored in those rasters mean? You can refer to the metadata provided by USGS to learn more about how each layer in your landsat dataset are both stored and calculated.
When you download remote sensing data, often (but not always), you will find layers that tell us more about the error and uncertainty in the data. Often whomever created the data will do some of the work for us to detect where clouds and shadows are - given they are common challenges that you need to work around when using remote sensing data.
Create Mask Layer in Python
To create the mask this you do the following:
- Make sure you use a raster layer for the mask that is the SAME EXTENT and the same pixel resolution as your landsat scene. In this case you have a mask layer that is already the same spatial resolution and extent as your landsat scene.
- Set all of the values in that layer that are clouds and / or shadows to
1(1 to represent
mask = True)
- Finally you use the
masked_arrayfunction to apply the mask layer to the numpy array (or the landsat scene that you are working with in Python). all pixel locations that were flagged as clouds or shadows in your mask to
rasteror in this case
Mask A Landsat Scene Using EarthPy
Below you mask your data in one single step. This function
em.mask_pixels() creates the mask as you saw above and then masks your data.
# Call the earthpy mask function using your mask layer landsat_pre_cl_free = em.mask_pixels(landsat_pre, cl_mask)
Alternatively, you can directly input your mask values and the pixel QA layer into the
mask_pixels function. This is the easiest way to mask your data!
# Call the earthpy mask function using pixel QA layer landsat_pre_cl_free = em.mask_pixels( landsat_pre, landsat_qa, vals=all_masked_values)
# Plot the data ep.plot_bands(landsat_pre_cl_free, extent=landsat_extent, cmap="Greys", title="Landsat CIR Composite Image | 30 meters \n Post Cold Springs Fire \n July 8, 2016", cbar=False) plt.show()
# Plot data ep.plot_rgb(landsat_pre_cl_free, rgb=[4, 3, 2], extent=landsat_ext, title="Landsat CIR Composite Image | 30 meters \n Post Cold Springs Fire \n July 8, 2016") plt.show()