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time-series.html
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<h1><a href="#time-series" name="time-series" class="anchor"><span class="anchor-link"></span></a>Time Series</h1>
<h2><a href="#analysis-plan" name="analysis-plan" class="anchor"><span class="anchor-link"></span></a>Analysis Plan</h2>
<p>In this example, we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will continue our example from the <a href="zonal-algebra.html">Zonal Map Algebra page</a>.</p>
<p>We will summarize the change in <a href="local-algebra.html#computing-ndvi">NDVI</a> over the spring and early summer of 2018 in the Cuyahoga Valley National Park in Ohio, USA. </p>
<h2><a href="#catalog-read" name="catalog-read" class="anchor"><span class="anchor-link"></span></a>Catalog Read</h2>
<p>As in our other example, we will query for a single known MODIS granule directly. We limit the vector data to the single park of interest. The longer time period selected should show the change in plant vigor as leaves emerge over the spring and into early summer. The definitions of <code>cat</code> and <code>park_vector</code> are as in the <a href="zonal-algebra.html">Zonal Map Algebra page</a>. </p>
<pre class="prettyprint"><code class="language-python">park_cat = cat \
.filter(
(cat.granule_id == 'h11v04') &
(cat.acquisition_date > lit('2018-02-19')) &
(cat.acquisition_date < lit('2018-07-01'))
) \
.crossJoin(park_vector.filter('UNIT_CODE == "CUVA"')) #only coyahuga
</code></pre>
<h2><a href="#vector-and-raster-data-interaction" name="vector-and-raster-data-interaction" class="anchor"><span class="anchor-link"></span></a>Vector and Raster Data Interaction</h2>
<p>We follow the same steps as the Zonal Map Algebra analysis: reprojecting the park geometry, filtering for intersection, rasterizing the geometry, and masking the NDVI by the <em>zone</em> <em>tiles</em>. The code from that analysis is condensed here for reference.</p>
<pre class="prettyprint"><code class="language-python">raster_cols = ['B01', 'B02',] # red and near-infrared respectively
rf_park_tile = spark.read.raster(
park_cat.select(['acquisition_date', 'granule_id', 'geo_simp'] + raster_cols),
catalog_col_names=raster_cols) \
.withColumn('park_native', st_reproject('geo_simp', lit('EPSG:4326'), rf_crs('B01'))) \
.filter(st_intersects('park_native', rf_geometry('B01'))) \
.withColumn('dims', rf_dimensions('B01')) \
.withColumn('park_tile', rf_rasterize('park_native', rf_geometry('B01'), lit(1), 'dims.cols', 'dims.rows')) \
.withColumn('ndvi', rf_normalized_difference('B02', 'B01')) \
.withColumn('ndvi_masked', rf_mask('ndvi', 'park_tile'))
</code></pre>
<h2><a href="#create-time-series" name="create-time-series" class="anchor"><span class="anchor-link"></span></a>Create Time Series</h2>
<p>We next aggregate across the cell values to arrive at an average NDVI for each week of the year. We use <code>pyspark</code>’s built in <code>groupby</code> and time functions with a RasterFrames <a href="aggregation.html">aggregate function</a> to do this. Note that the computation is creating a weighted average, which is weighted by the number of valid observations per week. </p>
<pre class="prettyprint"><code class="language-python">from pyspark.sql.functions import col, year, weekofyear, month
time_series = rf_park_tile \
.groupby(
year('acquisition_date').alias('year'),
weekofyear('acquisition_date').alias('week')) \
.agg(rf_agg_mean('ndvi_masked').alias('ndvi'))
</code></pre>
<p>Finally, we will take a look at the NDVI over time.</p>
<pre class="prettyprint"><code class="language-python">import matplotlib.pyplot as plt
time_series_pdf = time_series.toPandas()
time_series_pdf.sort_values('week', inplace=True)
plt.plot(time_series_pdf['week'], time_series_pdf['ndvi'], 'go-')
plt.xlabel('Week of year, 2018')
plt.ylabel('NDVI')
plt.title('Cuyahoga Valley NP Green-up')
</code></pre>
<pre><code>Text(0.5, 1.0, 'Cuyahoga Valley NP Green-up')
</code></pre>
<p><img src="figures/time-series_time_series_display_1.png" /></p>
<p>We can see two fairly clear elbows in the curve at week 17 and week 21, indicating the start and end of the green up period. Estimation of such parameters is one technique <a href="https://en.wikipedia.org/wiki/Phenology">phenology</a> researchers use to monitor changes in climate and environment.</p>
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