Time Series By Region (with Conversion to Pandas and Visualization with Seaborn)

Tutorial created by **David Montero Loaiza**: GitHub | Twitter

GitHub Repo: https://github.com/davemlz/eemont
PyPI link: https://pypi.org/project/eemont/
Conda-forge: https://anaconda.org/conda-forge/eemont
Documentation: https://eemont.readthedocs.io/
More tutorials: https://github.com/davemlz/eemont/tree/master/docs/tutorials

Let’s start!

If required, please uncomment:

[1]:

#!pip install eemont
#!pip install geemap

Import the required packages.

[2]:

import ee, eemont, geemap
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt

Authenticate and Initialize Earth Engine and geemap.

[3]:

Map = geemap.Map()

Let’s use a buffered point for the time series!

[4]:

point = ee.Geometry.Point([11.178576,51.122064]).buffer(100)

Let’s pre-process and process our image collection:

[5]:

L8 = (ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
      .filterBounds(point)
      .maskClouds()
      .scaleAndOffset()
      .spectralIndices(['GNDVI','EVI']))

Time Series By Region

Let’s get the L8 time series for our buffer. Checklist:

Image Collection: The Landsat 8 collection.
Bands to use for the time series: GNDVI and EVI.
Geometry: Our buffered point.
Statistics to compute: Minimum, Mean, and Maximum.
Scale: 30 m.

[6]:

ts = L8.getTimeSeriesByRegion(geometry = point,
                              bands = ['EVI','GNDVI'],
                              reducer = [ee.Reducer.min(),ee.Reducer.mean(),ee.Reducer.max()],
                              scale = 30)

Conversion to Pandas

The time series is retrieved as a feature collection. To convert it to a pandas dataframe we’ll use geemap (This may take a little bit):

[7]:

tsPandas = geemap.ee_to_pandas(ts)

Let’s check our pandas data frame:

[8]:

tsPandas

[8]:

	EVI	GNDVI	reducer	date
0	-9999.000000	-9999.000000	min	2013-04-20T10:04:44
1	-9999.000000	-9999.000000	min	2013-06-07T10:04:59
2	0.556182	0.653012	min	2013-07-09T10:04:56
3	0.624375	0.610642	min	2013-07-25T10:04:55
4	0.652409	0.768563	min	2013-08-10T10:04:57
...	...	...	...	...
739	-9999.000000	-9999.000000	max	2021-07-22T10:09:06
740	-9999.000000	-9999.000000	max	2021-08-07T10:09:14
741	-9999.000000	-9999.000000	max	2021-08-23T10:09:18
742	0.648737	0.834015	max	2021-09-08T10:09:23
743	0.475067	0.802076	max	2021-10-10T10:09:32

744 rows × 4 columns

What can we see here?

The values for each band (GNDVI and EVI) are in separated columns.
There are some -9999 values in the GNDVI and EVI columns. These values represent the NA values (e.g. Clouds or shadows). The -9999 can be changed by modifying the naValue parameter in the getTimeSeriesByRegion method (e.g. naValue = -10000).
Multiple reducers can be used. In the output dataframe they are specified by a single column named reducer: min, mean, max.
The date is a string that needs to be converted to a date data type.

Given this, let’s curate our data frame!

First, let’s get rid of the -9999 value:

[9]:

tsPandas[tsPandas == -9999] = np.nan

And now, let’s convert the date to a date data type:

[10]:

tsPandas['date'] = pd.to_datetime(tsPandas['date'],infer_datetime_format = True)

We can also gather the GNDVI and EVI columns into a single column to make the data frame more ‘tidy-er’ (This is optional):

[11]:

tsPandas = pd.melt(tsPandas,
                   id_vars = ['reducer','date'],
                   value_vars = ['GNDVI','EVI'],
                   var_name = 'Index',
                   value_name = 'Value')

Let’s check our curated data frame:

[12]:

tsPandas

[12]:

	reducer	date	Index	Value
0	min	2013-04-20 10:04:44	GNDVI	NaN
1	min	2013-06-07 10:04:59	GNDVI	NaN
2	min	2013-07-09 10:04:56	GNDVI	0.653012
3	min	2013-07-25 10:04:55	GNDVI	0.610642
4	min	2013-08-10 10:04:57	GNDVI	0.768563
...	...	...	...	...
1483	max	2021-07-22 10:09:06	EVI	NaN
1484	max	2021-08-07 10:09:14	EVI	NaN
1485	max	2021-08-23 10:09:18	EVI	NaN
1486	max	2021-09-08 10:09:23	EVI	0.648737
1487	max	2021-10-10 10:09:32	EVI	0.475067

1488 rows × 4 columns

Visualization

Now, let’s visualize our time series using seaborn:

[13]:

plt.figure(figsize = (20,10))
sns.lineplot(data = tsPandas,
             x = 'date',
             y = 'Value',
             hue = 'Index',
             style = 'reducer')

[13]:

<AxesSubplot:xlabel='date', ylabel='Value'>

../_images/tutorials_013-Time-Series-By-Region-Pandas_33_1.png

Getting the Closest Image to a Specific Date

Time Series By Regions (with Conversion to Pandas and Visualization with Seaborn)