Landsat 8 NDVI Analysis on the Cloud

This notebook demonstrates a "Cloud-native" analysis of Normalized Difference Vegetation Index (NDVI) using Landsat 8 data.

What is unique about this workflow is that no data is downloaded to our local computer! All calculations are performed in memory across many distributed machines on the Google Cloud.

This workflow is possible because the Landsat 8 data is stored in Cloud-Optimized Geotiff format, which can be accessed remotely via xarray and rasterio Python libraries. Distributed computing is enabled through a Pangeo JupyterHub deployment with Dask Kubernetes.

About Landsat 8: https://landsat.usgs.gov/landsat-8

About the Landsat archive: https://cloud.google.com/storage/docs/public-datasets/landsat

Date: August 30, 2018

Created by: Scott Henderson ([email protected]), Daniel Rothenberg

In [1]:
# Import required libraries
import os
import pandas as pd
import rasterio
import xarray as xr
import requests

from dask_gateway import Gateway
from dask.distributed import Client
from dask.distributed import wait, progress

import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
# Print package versions
print('Xarray version: ', xr.__version__)
print('Rasterio version: ', rasterio.__version__)
Xarray version:  0.10.8
Rasterio version:  1.0.3
In [3]:
# Set environment variables for cloud-optimized-geotiffs efficiency
os.environ['GDAL_DISABLE_READDIR_ON_OPEN']='YES'
os.environ['CPL_VSIL_CURL_ALLOWED_EXTENSIONS']='TIF'

Use NASA Common Metadata Repository (CMR) to get Landsat 8 images

NASA CMR is a new unified way to search for remote sensing assests across many archive centers. If you prefer a graphical user interface, NASA Earthdata Search is built on top of CMR. CMR returns download links through the USGS (https://earthexplorer.usgs.gov), but the same archive is mirrored as a (Google Public Dataset)[https://cloud.google.com/storage/docs/public-datasets/landsat], so we'll make a function that queries CMR and returns URLs to the imagery stored on Google Cloud.

In [4]:
def query_cmr_landsat(collection='Landsat_8_OLI_TIRS_C1',tier='T1', path=47, row=27):
    """Query NASA CMR for Collection1, Tier1 Landsat scenes from a specific path and row."""
    
    data = [f'short_name={collection}',
            f'page_size=2000',
            f'attribute[]=string,CollectionCategory,{tier}',
            f'attribute[]=int,WRSPath,{path}',
            f'attribute[]=int,WRSRow,{row}',
           ]

    query = 'https://cmr.earthdata.nasa.gov/search/granules.json?' + '&'.join(data)

    r = requests.get(query, timeout=100)
    print(r.url)
    
    df = pd.DataFrame(r.json()['feed']['entry'])
    
    # Save results to a file
    #print('Saved results to cmr-result.json')
    #with open('cmr-result.json', 'w') as j:
    #    j.write(r.text)
    
    return df
In [5]:
def make_google_archive(pids, bands):
    """Turn list of product_ids into pandas dataframe for NDVI analysis."""
    
    path =  pids[0].split('_')[2][1:3]
    row =  pids[0].split('_')[2][-2:]
    baseurl = f'https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/0{path}/0{row}'
    
    dates = [pd.to_datetime(x.split('_')[3]) for x in pids]
    df = pd.DataFrame(dict(product_id=pids, date=dates))
    
    for band in bands:
        df[band] = [f'{baseurl}/{x}/{x}_{band}.TIF' for x in pids]
    
    return df
In [6]:
df = query_cmr_landsat()
https://cmr.earthdata.nasa.gov/search/granules.json?short_name=Landsat_8_OLI_TIRS_C1&page_size=2000&attribute[]=string,CollectionCategory,T1&attribute[]=int,WRSPath,47&attribute[]=int,WRSRow,27
In [7]:
pids = df.title.tolist()
df = make_google_archive(pids, ['B4', 'B5'])
In [8]:
df.head()
Out[8]:
product_id date B4 B5
0 LC08_L1TP_047027_20130421_20170310_01_T1 2013-04-21 https://storage.googleapis.com/gcp-public-data... https://storage.googleapis.com/gcp-public-data...
1 LC08_L1TP_047027_20130523_20170310_01_T1 2013-05-23 https://storage.googleapis.com/gcp-public-data... https://storage.googleapis.com/gcp-public-data...
2 LC08_L1TP_047027_20130608_20170310_01_T1 2013-06-08 https://storage.googleapis.com/gcp-public-data... https://storage.googleapis.com/gcp-public-data...
3 LC08_L1TP_047027_20130624_20170309_01_T1 2013-06-24 https://storage.googleapis.com/gcp-public-data... https://storage.googleapis.com/gcp-public-data...
4 LC08_L1TP_047027_20130710_20180201_01_T1 2013-07-10 https://storage.googleapis.com/gcp-public-data... https://storage.googleapis.com/gcp-public-data...

Launch Dask Kubernetes Cluster

This will allow us to distribute our analysis across many machines. In the default configuration for Pangeo Binder, each worker has 2 vCPUs and 7Gb of RAM. It may take several minutes to initialize these workers and make them available to Dask.

In [ ]:
gateway = Gateway()
cluster = gateway.new_cluster()
cluster.scale(10)
cluster
In [10]:
# Attach Dask to the cluster
client = Client(cluster)
client

Examine a single band Landsat image

The rasterio library allows us to read Geotiffs on the web without downloading the entire image. Xarray has a built-in load_rasterio() function that allows us to open the file as a DataArray. Xarray also uses Dask for lazy reading, so we want to make sure the native block tiling of the image matches the dask "chunk size". These dask chunks are automatically distributed among all our workers when a computation is requested, so ideally they will fit in the worker memory. A chunk size of 2048x2048 with a float32 datatype implies a 16Mb array.

In [11]:
# Load with rasterio
image_url = df.iloc[0]['B4']
with rasterio.open(image_url) as src:
    print(src.profile)
{'driver': 'GTiff', 'dtype': 'uint16', 'nodata': None, 'width': 7751, 'height': 7531, 'count': 1, 'crs': CRS({'init': 'epsg:32610'}), 'transform': Affine(30.0, 0.0, 356685.0,
       0.0, -30.0, 5367615.0), 'blockxsize': 256, 'blockysize': 256, 'tiled': True, 'compress': 'lzw', 'interleave': 'band'}
In [12]:
# Note that the blocksize of the image is 256 by 256, so we want xarray to use some multiple of that
xchunk = 2048
ychunk = 2048
da = xr.open_rasterio(image_url, chunks={'band': 1, 'x': xchunk, 'y': ychunk})
da
Out[12]:
<xarray.DataArray (band: 1, y: 7531, x: 7751)>
dask.array<shape=(1, 7531, 7751), dtype=uint16, chunksize=(1, 2048, 2048)>
Coordinates:
  * band     (band) int64 1
  * y        (y) float64 5.368e+06 5.368e+06 5.368e+06 5.368e+06 5.367e+06 ...
  * x        (x) float64 3.567e+05 3.567e+05 3.568e+05 3.568e+05 3.568e+05 ...
Attributes:
    transform:   (30.0, 0.0, 356685.0, 0.0, -30.0, 5367615.0, 0.0, 0.0, 1.0)
    crs:         +init=epsg:32610
    res:         (30.0, 30.0)
    is_tiled:    1
    nodatavals:  (nan,)
In [13]:
# If we request to compute something or plot these arrays, the necessary data chunks will be accessed on cloud storage:
# Watch the KubeCluster dashboard to see the worker activity when this command is run:
# Note that no data is stored on the disk here, it's all in memory
da.sel(band=1).plot.imshow()
Out[13]:
<matplotlib.image.AxesImage at 0x7fab9c479b00>

Load all Landsat bands into an xarray dataset

Often we want to analyze a time series of satellite imagery, but we are constrained by computational resources. So we either download all the images, extract a small subset and then do our analysis. Or, we coarsen the resolution of all our images so that the entire set fits into our computer RAM. Because this notebook is running on Google Cloud with access to many resources in our Kube Cluster, we no longer have to worry about the computational constraints, and can conduct our analysis at full resoution!

First we need to construct an xarray dataset object (which has data variables 'band4' and 'band5' in a n-dimensional array with x-coordinates representing UTM easting, y-coordinates representing UTM northing, and a time coordinate representing the image acquisition date).

There are different ways to go about this, but we will load our images with a timestamp index since each image is taken on a different date. Typically, this is a chore if our images are not on the same grid to begin with, but xarray knows how to automatically align images based on their georeferenced coordinates.

In [14]:
# Note that these landsat images are not necessarily the same shape or on the same grid:
for image_url in df.B4[:5]:
    with rasterio.open(image_url) as src:
        print(src.shape, src.bounds)
(7531, 7751) BoundingBox(left=356685.0, bottom=5141685.0, right=589215.0, top=5367615.0)
(7541, 7751) BoundingBox(left=353385.0, bottom=5141685.0, right=585915.0, top=5367915.0)
(7541, 7751) BoundingBox(left=354285.0, bottom=5141385.0, right=586815.0, top=5367615.0)
(7531, 7751) BoundingBox(left=356385.0, bottom=5141685.0, right=588915.0, top=5367615.0)
(7971, 7861) BoundingBox(left=353985.0, bottom=5135085.0, right=589815.0, top=5374215.0)
In [15]:
def create_multiband_dataset(row, bands=['B4','B5'], chunks={'band': 1, 'x': 2048, 'y': 2048}):
    '''A function to load multiple landsat bands into an xarray dataset '''
    
    # Each image is a dataset containing both band4 and band5
    datasets = []
    for band in bands:
        url = row[band]
        da = xr.open_rasterio(url, chunks=chunks)
        da = da.squeeze().drop(labels='band')
        ds = da.to_dataset(name=band)
        datasets.append(ds)

    DS = xr.merge(datasets)
    
    return DS
In [16]:
# Merge all acquisitions into a single large Dataset
datasets = []
for i,row in df.iterrows():
    try:
        print('loading...', row.date)
        ds = create_multiband_dataset(row)
        datasets.append(ds)
    except Exception as e:
        print('ERROR loading, skipping acquistion!')
        print(e)
loading... 2013-04-21 00:00:00
loading... 2013-05-23 00:00:00
loading... 2013-06-08 00:00:00
loading... 2013-06-24 00:00:00
loading... 2013-07-10 00:00:00
loading... 2013-07-26 00:00:00
loading... 2013-08-11 00:00:00
loading... 2013-08-27 00:00:00
loading... 2013-09-12 00:00:00
loading... 2013-10-14 00:00:00
loading... 2013-10-30 00:00:00
loading... 2014-01-02 00:00:00
loading... 2014-01-18 00:00:00
loading... 2014-02-03 00:00:00
loading... 2014-02-19 00:00:00
loading... 2014-03-07 00:00:00
loading... 2014-03-23 00:00:00
loading... 2014-04-08 00:00:00
loading... 2014-04-24 00:00:00
loading... 2014-05-10 00:00:00
loading... 2014-05-26 00:00:00
loading... 2014-06-11 00:00:00
loading... 2014-06-27 00:00:00
loading... 2014-07-13 00:00:00
loading... 2014-07-29 00:00:00
loading... 2014-08-14 00:00:00
loading... 2014-09-15 00:00:00
loading... 2014-10-01 00:00:00
loading... 2014-11-18 00:00:00
loading... 2015-01-21 00:00:00
loading... 2015-02-22 00:00:00
loading... 2015-03-10 00:00:00
loading... 2015-03-26 00:00:00
loading... 2015-04-11 00:00:00
loading... 2015-04-27 00:00:00
loading... 2015-05-29 00:00:00
loading... 2015-06-14 00:00:00
loading... 2015-06-30 00:00:00
loading... 2015-07-16 00:00:00
loading... 2015-08-01 00:00:00
loading... 2015-08-17 00:00:00
loading... 2015-09-02 00:00:00
loading... 2015-09-18 00:00:00
loading... 2015-10-04 00:00:00
loading... 2015-10-20 00:00:00
loading... 2015-11-05 00:00:00
loading... 2015-11-21 00:00:00
loading... 2015-12-23 00:00:00
loading... 2016-01-08 00:00:00
loading... 2016-01-24 00:00:00
loading... 2016-02-09 00:00:00
loading... 2016-02-25 00:00:00
loading... 2016-03-12 00:00:00
loading... 2016-03-28 00:00:00
loading... 2016-04-13 00:00:00
loading... 2016-04-29 00:00:00
loading... 2016-05-31 00:00:00
loading... 2016-06-16 00:00:00
loading... 2016-07-02 00:00:00
loading... 2016-07-18 00:00:00
loading... 2016-08-03 00:00:00
loading... 2016-08-19 00:00:00
loading... 2016-09-04 00:00:00
loading... 2016-09-20 00:00:00
loading... 2016-10-22 00:00:00
loading... 2016-11-07 00:00:00
loading... 2016-11-23 00:00:00
loading... 2016-12-25 00:00:00
loading... 2017-01-10 00:00:00
loading... 2017-01-26 00:00:00
loading... 2017-02-11 00:00:00
loading... 2017-03-31 00:00:00
loading... 2017-04-16 00:00:00
loading... 2017-05-02 00:00:00
loading... 2017-05-18 00:00:00
loading... 2017-06-03 00:00:00
loading... 2017-06-19 00:00:00
loading... 2017-07-05 00:00:00
loading... 2017-07-21 00:00:00
loading... 2017-08-06 00:00:00
loading... 2017-08-22 00:00:00
loading... 2017-09-07 00:00:00
loading... 2017-09-23 00:00:00
loading... 2017-10-09 00:00:00
loading... 2017-10-25 00:00:00
loading... 2017-11-10 00:00:00
loading... 2017-12-12 00:00:00
loading... 2018-01-13 00:00:00
loading... 2018-02-14 00:00:00
loading... 2018-03-02 00:00:00
loading... 2018-03-18 00:00:00
loading... 2018-04-03 00:00:00
loading... 2018-04-19 00:00:00
loading... 2018-05-05 00:00:00
loading... 2018-05-21 00:00:00
loading... 2018-06-06 00:00:00
loading... 2018-06-22 00:00:00
loading... 2018-07-08 00:00:00
loading... 2018-07-24 00:00:00
loading... 2018-08-09 00:00:00
In [17]:
DS = xr.concat(datasets, dim=pd.DatetimeIndex(df.date.tolist(), name='time'))
print('Dataset size (Gb): ', DS.nbytes/1e9)
DS
Dataset size (Gb):  103.572115136
Out[17]:
<xarray.Dataset>
Dimensions:  (time: 100, x: 8121, y: 7971)
Coordinates:
  * x        (x) float64 3.492e+05 3.492e+05 3.493e+05 3.493e+05 3.493e+05 ...
  * y        (y) float64 5.135e+06 5.135e+06 5.135e+06 5.135e+06 5.135e+06 ...
  * time     (time) datetime64[ns] 2013-04-21 2013-05-23 2013-06-08 ...
Data variables:
    B4       (time, y, x) float64 dask.array<shape=(100, 7971, 8121), chunksize=(1, 1607, 2048)>
    B5       (time, y, x) float64 dask.array<shape=(100, 7971, 8121), chunksize=(1, 1607, 2048)>

Note that xarray has automatically expanded the dimensions to include the maximum extents of all the images, also the chunksize has been automatically adjusted.

There is definitely some room for improvement here from a computational efficiency standpoint - in particular the dask chunks are no longer aligned with the image tiles. This is because each image starts at different coordinates and has different shapes, but xarray uses a single chunk size for the entire datasets. There will also be many zeros in this dataset, so future work could take advantage of sparse arrays.

These points aside, our KubeCluster will automatically parallelize our computations for us, so we can not worry too much about optimal efficiency and just go ahead and run our analysis!

In [18]:
# Here is the syntax to plot the same image as before
# Again, the actually image data is downloaded from cloud storage to memory
da = DS.sel(time='2013-04-21')['B4']
print('Image size (Gb): ', da.nbytes/1e9)
da.plot.imshow()
Image size (Gb):  0.517859928
Out[18]:
<matplotlib.image.AxesImage at 0x7fab6d93d208>

Distributed NDVI computations

Set up our NDVI dataset. Note that NDVI is not actually computed until we call the Dask compute(), persist(), or call other functions such as plot() that require actually operate on the data!

In [19]:
NDVI = (DS['B5'] - DS['B4']) / (DS['B5'] + DS['B4'])
NDVI
Out[19]:
<xarray.DataArray (time: 100, y: 7971, x: 8121)>
dask.array<shape=(100, 7971, 8121), dtype=float64, chunksize=(1, 1607, 2048)>
Coordinates:
  * x        (x) float64 3.492e+05 3.492e+05 3.493e+05 3.493e+05 3.493e+05 ...
  * y        (y) float64 5.135e+06 5.135e+06 5.135e+06 5.135e+06 5.135e+06 ...
  * time     (time) datetime64[ns] 2013-04-21 2013-05-23 2013-06-08 ...

Plot NDVI on specific date (full resolution)

Only data for a single landsat acquisition date is pulled from Cloud storage

In [20]:
NDVI.sel(time='2013-04-21').plot.imshow()
Out[20]:
<matplotlib.image.AxesImage at 0x7fab3f4073c8>

Mean NDVI for a range of dates

This example calculates the mean NDVI per-pixel (30m) for 2013-2014, storing result in local RAM

In [21]:
ndvi = NDVI.sel(time=slice('2013-01-01', '2014-01-01')).mean(dim='time').persist()
print('projected dataset size (Gb): ', ndvi.nbytes/1e9)
projected dataset size (Gb):  0.517859928
In [22]:
print(ndvi)
ndvi.plot.imshow()

# Point of interest we'll extract a timeseries from
plt.plot(562370, 5312519, 'ko')
<xarray.DataArray (y: 7971, x: 8121)>
dask.array<shape=(7971, 8121), dtype=float64, chunksize=(1607, 2048)>
Coordinates:
  * x        (x) float64 3.492e+05 3.492e+05 3.493e+05 3.493e+05 3.493e+05 ...
  * y        (y) float64 5.135e+06 5.135e+06 5.135e+06 5.135e+06 5.135e+06 ...
Out[22]:
[<matplotlib.lines.Line2D at 0x7fab3f5ab6a0>]

Extract region of interest Farmland near Everett, WA (Ebey Island) and resample to monthly mean average

We expect to see higher NDVI values in the summer months, corresponding to dense vegetation

In [23]:
# https://www.geoplaner.com
# lat, lon to northing, easting
# 47.962940 --> 5312519
# -122.164483--> 562370
#+ 5 km buffer
#EPSG:32610 WGS 84 / UTM zone 10N
xcen = 562370
ycen = 5312519
buf = 5000  # look at point +/- 5km
ds = NDVI.sel(x=slice(xcen-buf,xcen+buf), y=slice(ycen-buf,ycen+buf))
timeseries = ds.resample(time='1MS').mean().persist()
In [24]:
timeseries
Out[24]:
<xarray.DataArray (time: 65)>
dask.array<shape=(65,), dtype=float64, chunksize=(1,)>
Coordinates:
  * time     (time) datetime64[ns] 2013-04-01 2013-05-01 2013-06-01 ...
In [25]:
s = timeseries.to_series()
s.plot(title='mean NDVI', figsize=(8,4), style='ko-')
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7faaff0fa7b8>

Plot subset of all NDVI arrays at full resultion for acquisitions in 2015

In [26]:
ds.sel(time=slice('2015-01-01', '2016-01-01')).plot.imshow('x', 'y', col='time', col_wrap=4)
Out[26]:
<xarray.plot.facetgrid.FacetGrid at 0x7faaff093710>