Detailed Examples of SQL2GEE’s use

We present a few detailed use cases of SQL2GEE.

Finding forest-loss statistics between 2001 - 2015

The Hansen et al Global Forest Change data-set shows high-resolution statistics on forest cover and loss during the past decade. These data are available via Google’s Earth Engine platform, via the data-set ID UMD/hansen/global_forest_change_2014.

These data have a band called lossyear, which shows, for every pixel where forest was lost, the year that the loss likely occured. The integers are from 0 to 14, all values > 0 indicate year since 2000. Pixels with a 0 value indicate locations where forest was not lost (and should be ignored).

Let’s use the ST_HISTOGRAM function of the SQL2GEE library to create a step-plot (using Matplotlib), to show these data.

First, we load the required libraries, and authenticate with Earth Engine.

 from sql2gee import SQL2GEE
 import ee
 import matplotlib.pyplot as plt
 ee.Initialize()

Next, we make a request via SQL2GEE to the Hansen dataset. Calling the response property we can confirm that a histogram has been successfully returned.

 sql = 'SELECT ST_HISTOGRAM(raster, lossyear, 15, true) FROM "UMD/hansen/global_forest_change_2015"'
 q = SQL2GEE(sql)
 q.response
 {'lossyear': [[0.0, 1291270.0],
   [1.0, 192.0],
   [2.0, 483.0],
   [3.0, 449.0],
   [4.0, 1185.0],
   [5.0, 462.0],
   [6.0, 1264.0],
   [7.0, 2156.0],
   [8.0, 514.0],
   [9.0, 343.0],
   [10.0, 1374.0],
   [11.0, 334.0],
   [12.0, 992.0],
   [13.0, 2624.0],
   [14.0, 7629.0]]}

We can extract these data and plot them as follows:

 bin_pos = []
 counts = []
 for pair in response['lossyear']:
     bin_left, count = pair
     bin_pos.append(bin_left)
     counts.append(count)
 bin_pos = np.array(bin_pos)
 counts = np.array(counts)

 percent_value = (counts[1:]/sum(counts[1:])*100.)
 year = bin_pos[1:]+2000

 plt.plot(year, percent_value)
 plt.title('Of all forest lost in the last decade, \n which year was it lost in?')
 plt.xlabel('Year')
 plt.ylabel("Percent (%)")
 plt.show()