3. Analyze LUCAS data

3.1. Install

3.1.1. Note for Google Colab

In the case that this notebook is run in Google Colab, additional steps are needed. Google Colab comes with GDAL 2.2. We have to update this library to version 3.0. It will takes a while.

!add-apt-repository --yes ppa:ubuntugis/ubuntugis-unstable
!apt update
!apt install libgdal26 libsqlite3-mod-spatialite
!pip install gdal==3.0.4
print("INSTALLATION COMPLETED")

3.1.2. Install st_lucas package

First of all, let’s install st_lucas Python package.

!pip install st_lucas

Now we have to restart runtime: Runtime -> Restart runtime (on Google Colab) or Kernel -> Restart (on JupyterLab).

From st_lucas package let’s import LucasRequest, LucasIO and LucasClassAggregate.

from st_lucas import LucasRequest, LucasIO, LucasClassAggregate

3.2. Download sample LUCAS points

3.2.1. Define request

Request LUCAS points located in the Czech Republic and surveyed in 2018.

request = LucasRequest()
request.countries = ['CZ']
request.years = [2018]

3.2.2. Download data

lucasio = LucasIO()
lucasio.download(request)

2023-06-25 19:52:50,928 - LUCAS - INFO - io.download - Download process successfuly finished. Size of downloaded data: 27387kb

3.2.3. Inspect downloaded LUCAS data

df = lucasio.to_geopandas()
df[["point_id","lc1"]]

	point_id	lc1
0	48462894	A30
1	46142892	C10
2	48042992	A30
3	46142990	A30
4	46582898	A22
...	...	...
5708	46482884	A12
5709	47402970	A11
5710	46682906	G11
5711	47303016	G11
5712	45623046	A11

5713 rows × 2 columns

df.count()

point_id         5713
nuts0            5713
nuts1            5713
nuts2            5713
nuts3            5713
                 ...
crop_residues    5713
transect            0
ex_ante          5713
survey_year      5713
geometry         5713
Length: 116, dtype: int64

3.2.4. Show statistics

df["lc1"].describe()

count     5713
unique      57
top        E20
freq       990
Name: lc1, dtype: object

df['lc1_l1'] = df['lc1'].str.slice(0,1)
df['lc1_l1'].value_counts().plot.pie(figsize=(10, 10), autopct="%.1f")

<AxesSubplot:ylabel='lc1_l1'>

3.3. Class aggregation

LUCAS points are by default obtained with information corresponding to LUCAS land cover level 3. With the LucasClassAggregate method, you can get information about level 2 or level 1. New column will be added at the end of attribute table.

from st_lucas import LucasRequest, LucasIO, LucasClassAggregate
request = LucasRequest()
request.countries = ['CZ']
request.years = [2018]
lucasio = LucasIO()
lucasio.download(request)

lc1_to_level1 = {
    "A00": ["A11", "A12", "A13", "A21", "A22", "A30"],
    "B00": ["B11", "B12", "B13", "B14", "B15", "B16", "B17", "B18", "B19", "B21", "B22", "B23", "B31", "B32", "B33", "B34", "B35", "B36", "B37", "B41", "B42", "B43", "B44", "B45", "B51", "B52", "B53", "B54", "B55", "B71", "B72", "B73", "B74", "B75", "B76", "B77", "B81", "B82", "B83", "B84"],
    "Bx1": ["Bx1"],
    "Bx2": ["Bx2"],
    "C00": ["C10", "C21", "C22", "C23", "C31", "C32", "C33"],
    "D00": ["D10", "D20"],
    "E00": ["E10", "E20", "E30"],
    "F00": ["F10", "F20", "F30", "F40"],
    "G00": ["G11", "G12", "G21", "G22", "G30", "G40", "G50"],
    "H00": ["H11", "H12", "H21", "H22", "H23"]
}

lucasaggr = LucasClassAggregate(lucasio.data, mappings=lc1_to_level1)
lucasaggr.apply()

2023-06-25 19:53:18,974 - LUCAS - INFO - io.download - Download process successfuly finished. Size of downloaded data: 27387kb

Check the appearance of the new column.

df = lucasio.to_geopandas()
df[["lc1_h", "lc1_a"]].head()

	lc1_h	lc1_a
0	A30	A00
1	C10	C00
2	A30	A00
3	A30	A00
4	A22	A00

3.4. Nomenclature translation

Translate the obtained data into CLC2.

from st_lucas import LucasClassTranslate

lucastrans = LucasClassTranslate(lucasio.data)
lucastrans.set_translations("CLC2")
lucastrans.apply()

Check the appearance of the new column.

df = lucasio.to_geopandas()
df[["lc1_h", "lu1_h", "clc2"]].head(10)

	lc1_h	lu1_h	clc2
0	A30	U319	99
1	C10	U120	31
2	A30	U319	99
3	A30	U319	99
4	A22	U312	12
5	A22	U312	12
6	A30	U319	99
7	B73	U113	22
8	B71	U113	22
9	B73	U113	22