Data Ingestion - Geospatial-Specific Tooling

PySTAC


Overview

In this notebook, you will ingest Landsat data for use in machine learning. Machine learning tasks often involve a lot of data, and in Python, data is typically stored in memory as simple NumPy arrays. However, higher-level containers built on top of NumPy arrays provide more functionality for multidimensional gridded data (xarray) or out-of-core and distributed data (Dask). Our goal for data ingestion will be to load specific Landsat data of interest into one of these higher-level containers.

Microsoft Plantery Computer is one of several providers of Landsat Data. We are using it together with pystac-client and odc-stac because together they provide a nice Python API for searching and loading with specific criteria such as spatial area, datetime, Landsat mission, and cloud coverage.

Earth science datasets are often stored on remote servers that may be too large to download locally. Therefore, in this cookbook, we will focus primarily on ingestion approaches that load small portions of data from a remote source, as needed. However, the approach for your own work will depend not only on data size and location but also the intended analysis, so in a follow up notebook, you will see an alternative approache for generalized data access and management.

Prerequisites

Concepts

Importance

Notes

Intro to Landsat

Necessary

Background

About the Microsoft Planetary Computer

Helpful

Background

pystac-client Usage

Helpful

Consult as needed

odc.stac.load Reference

Helpful

Consult as needed

xarray

Necessary

Intro to Dask Array

Helpful

Panel Getting Started Guide

Helpful

  • Time to learn: 10 minutes

Imports

import odc.stac
import pandas as pd
import planetary_computer
import pystac_client
import xarray as xr
from pystac.extensions.eo import EOExtension as eo

# Viz
import hvplot.xarray
import panel as pn

pn.extension()

Open and read the root of the STAC catalog

catalog = pystac_client.Client.open(
    "https://planetarycomputer.microsoft.com/api/stac/v1",
    modifier=planetary_computer.sign_inplace,
)
catalog.title
'Microsoft Planetary Computer STAC API'

Microsoft Planetary Computer has a public STAC metadata but the actual data assets are in private Azure Blob Storage containers and require authentication. pystac-client provides a modifier keyword that we can use to manually sign the item. Otherwise, we’d get an error when trying to access the asset.

Search for Landsat Data

Let’s say that an analysis we want to run requires landsat data over a specific region and from a specific time period. We can use our catalog to search for assets that fit our search criteria.

First, let’s find the name of the landsat dataset. This page is a nice resource for browsing the available collections, but we can also just search the catalog for ‘landsat’:

all_collections = [i.id for i in catalog.get_collections()]
landsat_collections = [
    collection for collection in all_collections if "landsat" in collection
]
landsat_collections
['landsat-c2-l2', 'landsat-c2-l1']

We’ll use the landsat-c2-l2 dataset, which stands for Collection 2 Level-2. It contains data from several landsat missions and has better data quality than Level 1 (landsat-c2-l1). Microsoft Planetary Computer has descriptions of Level 1 and Level 2, but a direct and succinct comparison can be found in this community post, and the information can be verified with USGS.

Now, let’s set our search parameters. You may already know the bounding box (region/area of interest) coordinates, but if you don’t, there are many useful tools like bboxfinder.com that can help.

bbox = [-118.89, 38.54, -118.57, 38.84]  # Region over a lake in Nevada, USA
datetime = "2017-06-01/2017-09-30"  # Summer months of 2017
collection = "landsat-c2-l2"

We can also specify other parameters in the query, such as a specific landsat mission and the max percent of cloud cover:

platform = "landsat-8"
cloudy_less_than = 1  # percent

Now we run the search and list the results:

search = catalog.search(
    collections=["landsat-c2-l2"],
    bbox=bbox,
    datetime=datetime,
    query={"eo:cloud_cover": {"lt": cloudy_less_than}, "platform": {"in": [platform]}},
)
items = search.item_collection()
print(f"Returned {len(items)} Items:")
item_id = {(i, item.id): i for i, item in enumerate(items)}
item_id
Returned 3 Items:
{(0, 'LC08_L2SP_042033_20170718_02_T1'): 0,
 (1, 'LC08_L2SP_042033_20170702_02_T1'): 1,
 (2, 'LC08_L2SP_042033_20170616_02_T1'): 2}

It looks like there were three image stacks taken by Landsat 8 over this spatial region during the summer months of 2017 that has less than 1 percent cloud cover.

Preview Results and Select a Dataset

Before loading one of the available image stacks, it would be useful to get a visual check of the results. Many datasets have a rendered preview or thumbnail image that can be accessed without having to load the full resolution data.

We can create a simple interactive application using the Panel library to access and display rendered PNG previews of the our search results. Note that these pre-rendered images are of large tiles that span beyond our bounding box of interest. In the next steps, we will only be loading in a small area around the lake.

item_sel = pn.widgets.Select(value=1, options=item_id, name="item")

def get_preview(i):
    return pn.panel(items[i].assets["rendered_preview"].href, height=300)


pn.Row(item_sel, pn.bind(get_preview, item_sel))
selected_item = items[1]
selected_item

Access the Data

Now that we have selected a dataset from our catalog, we can procede to access the data. We want to be very selective about the data that we read and when we read it because the amount of downloaded data can quickly get out of hand. Therefore, let’s select only a subset of images.

First, we’ll preview the different image assets (or Bands) available in the Landsat item.

assets = []
for _, asset in selected_item.assets.items():
    try:
        assets.append(asset.extra_fields["eo:bands"][0])
    except:
        pass

cols_ordered = [
    "common_name",
    "description",
    "name",
    "center_wavelength",
    "full_width_half_max",
]
bands = pd.DataFrame.from_dict(assets)[cols_ordered]
bands
common_name description name center_wavelength full_width_half_max
0 red Visible red OLI_B4 0.65 0.04
1 blue Visible blue OLI_B2 0.48 0.06
2 green Visible green OLI_B3 0.56 0.06
3 nir08 Near infrared OLI_B5 0.87 0.03
4 lwir11 Long-wave infrared TIRS_B10 10.90 0.59
5 swir16 Short-wave infrared OLI_B6 1.61 0.09
6 swir22 Short-wave infrared OLI_B7 2.20 0.19
7 coastal Coastal/Aerosol OLI_B1 0.44 0.02

Then we will select a few bands (images) of interest:

bands_of_interest = ["red", "green", "blue"]

Finally, we lazily load the selected data. We will use the package called odc which allows us to load only a specific region of interest (bounding box or ‘bbox’) and specific bands (images) of interest. We will also use the chunks argument to load the data as dask arrays; this will load the metadata now and delay the loading until we actually use the data, or until we force the data to be loaded by using .compute().

ds = odc.stac.stac_load(
    [selected_item],
    bands=bands_of_interest,
    bbox=bbox,
    chunks={},  # <-- use Dask
).isel(time=0)
ds
<xarray.Dataset>
Dimensions:      (y: 1128, x: 950)
Coordinates:
  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
    spatial_ref  int32 32611
    time         datetime64[ns] 2017-07-02T18:33:06.200763
Data variables:
    red          (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
    green        (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
    blue         (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>

Let’s combine the bands of the dataset into a single DataArray that has the band names as coordinates of a new ‘band’ dimension, and also call .compute() to finally load the data.

da = ds.to_array(dim="band").compute()
da
<xarray.DataArray (band: 3, y: 1128, x: 950)>
array([[[14691, 14914, 14988, ..., 16283, 16292, 16316],
        [14655, 14859, 14969, ..., 16272, 16185, 16079],
        [14531, 14699, 14972, ..., 15318, 15526, 14734],
        ...,
        [13804, 13561, 13601, ..., 18311, 18202, 17625],
        [13857, 13828, 13858, ..., 19400, 18942, 18551],
        [13840, 13786, 13867, ..., 17873, 17917, 18453]],

       [[13233, 13402, 13565, ..., 14553, 14658, 14657],
        [13291, 13428, 13585, ..., 14590, 14478, 14550],
        [13122, 13287, 13601, ..., 13987, 14220, 13571],
        ...,
        [12720, 12552, 12468, ..., 16580, 16411, 15899],
        [12704, 12644, 12658, ..., 17351, 16853, 16505],
        [12647, 12620, 12698, ..., 15990, 16211, 16686]],

       [[11572, 11629, 11723, ..., 12857, 12918, 12946],
        [11588, 11655, 11721, ..., 12848, 12792, 12715],
        [11510, 11608, 11781, ..., 12371, 12453, 12053],
        ...,
        [11195, 11104, 11045, ..., 14182, 14031, 13716],
        [11125, 11061, 11106, ..., 14652, 14284, 14062],
        [11059, 11050, 11134, ..., 13756, 13865, 14209]]], dtype=uint16)
Coordinates:
  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
    spatial_ref  int32 32611
    time         datetime64[ns] 2017-07-02T18:33:06.200763
  * band         (band) object 'red' 'green' 'blue'

Visualize the data

Often, data ingestion involves quickly visualizing your raw data to get a sense that things are proceeding accordingly. As we have created an array with red, blue, and green bands, we can quickly display a natural color image of the lake using the .plot.imshow() function of xarray. We’ll use the robust=True argument because the data values are outside the range of typical RGB images.

da.plot.imshow(robust=True, size=3)
<matplotlib.image.AxesImage at 0x7f6efd20f4c0>
../_images/44752ad5ede4bfd4ff006172839266a1d5ca899c87f3d555f5c61208891dfbd7.png

Now, let’s use hvplot to provide an interactive visualization of the inividual bands in our array.

ds
<xarray.Dataset>
Dimensions:      (y: 1128, x: 950)
Coordinates:
  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
    spatial_ref  int32 32611
    time         datetime64[ns] 2017-07-02T18:33:06.200763
Data variables:
    red          (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
    green        (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
    blue         (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
da.hvplot.image(x="x", y="y", cmap="viridis", aspect=1)