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A package for retrieving data concerning forests on the European continent.

Project description

PyPI version GitHub last commit GitHub

forest_puller version 1.1.6

forest_puller is a python package for retrieving data concerning forests on the European continent. This includes forest growth rates, amount of forested areas and forest inventory (standing stock).

There are several public data sources accessible online that provide these types of information in various forms and granularity. This package automates the process of scrapping these websites and parsing the resulting csv tables or excel files.

Once forest_puller is installed you can easily access forest data through standard python pandas data frames.

Scope and sources

Currently forest_puller provides data for the following 26 member states (past and current):

  • Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom

Currently forest_puller caches and provides programmatic access to the forest-relevent data from these data sources:

What a data source you would like to see here ? Contact the authors by opening an issue in the issue tracker.

Installing

forest_puller is a python package and hence is compatible with all operating systems: Linux, macOS and Windows. Once python 3 is installed on your computer, if it is not already, simply type the following on your terminal:

$ pip3 install --user forest_puller

Or if you want to install it for all users of the system:

$ sudo pip3 install forest_puller

Usage

For instance to retrieve the net carbon dioxide emission of Austria in 2017 that were due to coniferous forest land from the IPCC official data source, you can do the following:

# Import #
from forest_puller.ipcc.country import countries

# Get the country #
austria = countries['AT']

# Get the 2017 indexed dataframe #
at_2017 = austria.years[2017].indexed

# Print some data #
print(at_2017.loc['remaining_forest', 'Coniferous']['net_co2'])
 904282.4970403439

To see what information is available you can of course display the column titles and row indexes of that data frame:

print(at_2017.columns)

# Index(['area', 'area_mineral', 'area_organic', 'biomass_gains_ratio',
#        'biomass_losses_ratio', 'biomass_net_change_ratio', 'net_dead_ratio',
#        'net_litter_ratio', 'net_mineral_soil_ratio', 'net_organic_soil_ratio',
#        'biomass_gains', 'biomass_losses', 'biomass_net_change', 'net_dead',
#        'net_litter', 'net_mineral_soils', 'net_organic_soils', 'net_co2'],
#        dtype='object', name='category')

print(at_2017.index)

# MultiIndex(levels=[['cropland_to_forest', 'grassland_to_forest',
# 'land_to_forest', 'other_land_to_forest', 'remaining_forest',
# 'settlements_to_forest', 'total_forest', 'wetlands_to_forest'],
# ['', 'Coniferous', 'Deciduous', 'Forest not in yield', 'Total']])

To examine what countries and what years are available:

print(list(c.iso2_code for c in countries.values()))

# ['AT', 'BE', 'BG', 'HR', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR',
# 'HU', 'IE', 'IT', 'LV', 'LT', 'LU', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI',
# 'ES', 'SE', 'GB', 'ZZ']

print(list(y for y in austria.years))
# [1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,
# 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
# 2013, 2014, 2015, 2016, 2017]

To get a large data frame with all years and all countries inside:

from forest_puller.ipcc.concat import df
print(df)

Cache

When you import forest_puller, we will check the $FOREST_PULLER_CACHE environment variable to see where to download and store the cached data. If this variable is not set, we will default to the platform's temporary directory and clone a repository there. This could result in re-downloading the cache after every reboot.

Data sources

IPCC

To access the same forest data directly from the IPCC website without forest_puller you would have to first select your country from the CRF country table in a browser at this address.

IPCC demo screenshot 1

Then you would have to manually download the zip file for that specific country through another page.

IPCC demo screenshot 2

Next, you would have to uncompress the zip file and locate the xls file that concerns the year you are interested in.

IPCC demo screenshot 4

Finally you would have to scroll to the right sheet in your spreadsheet software and find the pertinent cell.

IPCC demo screenshot 5

This operation would have to be repeated for every country, and every year you are interested in.

With forest_puller you can easily display any information you want for all countries at the same time:

from forest_puller.ipcc.country import countries

category, key = ['total_forest', 'biomass_net_change']
biomass_net_change = {
    k: c.last_year.indexed.loc[category, ''][key]
    for k,c in countries.items()
}

import pprint
pprint.pprint(biomass_net_change)
{'AT': 1367857.0940855271,
 'BE': 374245.08695361385,
 'BG': 2192942.031982918,
 'CZ': 387870.89395249996,
 'DE': 12317598.87352293,
 'DK': -216454.31026543948,
 'EE': 320710.2459538891,
 'ES': 8917649.261547482,
 'FI': 6603815.0,
 'FR': 15051831.9827214,
 'GB': 2892518.0859005335,
 'GR': 583205.0978272819,
 'HR': 1477791.7578513895,
 'HU': 1259385.5890665338,
 'IE': 1069648.7636722159,
 'IT': 5752883.095908434,
 'LT': 2146933.309581986,
 'LU': 101929.37461705346,
 'LV': 1244965.2120000012,
 'NL': 499021.93968,
 'PL': 9353198.2907701,
 'PT': 1536917.4736652463,
 'RO': 5561343.4405591395,
 'SE': 10185839.738999998,
 'SI': 35391.09710503432,
 'SK': 1184611.3471376207}

Forest Europe (SOEF)

This data is provided by the Ministerial Conference on the Protection of Forests in Europe and is accessible at: https://dbsoef.foresteurope.org/

Three tables are provided for every country:

  • Table 1.1a: Forest area
  • Table 1.3a1: Age class distribution (area of even-aged stands)
  • Table 3.1: Increment and fellings

It is accessed in a similar way to other data sources:

from forest_puller.soef.country import countries

country = countries['AT']
print(country.forest_area.indexed)
print(country.age_dist.indexed)
print(country.fellings.indexed)

There is also a large data frame containing all countries concatenated together:

from forest_puller.soef.concat import tables
print(tables['forest_area'])
print(tables['age_dist'])
print(tables['fellings'])

Faostat (forestry)

This data is acquired by picking the "All Data Normalized" option from the "Bulk download" sidebar at this address: http://www.fao.org/faostat/en/#data/FO

It is accessed in a similar way to other data sources:

from forest_puller.faostat.forestry.country import countries

country = countries['AT']
print(country.df)

There is also a large data frame containing all countries concatenated together:

from forest_puller.faostat.forestry.concat import df
print(df)

Faostat (land)

This data is acquired by picking the "All Data Normalized" option from the "Bulk download" sidebar at this address: http://www.fao.org/faostat/en/#data/GF

It is accessed in a similar way to other data sources:

from forest_puller.faostat.land.country import countries

country = countries['AT']
print(country.df)

There is also a large data frame containing all countries concatenated together:

from forest_puller.faostat.land.concat import df
print(df)

Diabolo (hpffre)

Is a consortium of 33 partners from 25 countries. Experts in the fields of policy analysis, forest inventory, forest modelling. 7 work packages.

Link: http://diabolo-project.eu/

One of the outcomes of the Diabolo project is the following publication:

Vauhkonen et al. 2019 - Harmonised projections of future forest resources in Europe

Abbreviated "hpffre". The authors used EFDM (mainly) to project forest area, growing stock, fellings and above ground carbon for European countries. There are several scenario outcomes.

The dataset is available at: https://doi.org/10.5061/dryad.4t880qh

It is accessed in a similar way to other data sources:

from forest_puller.hpffre.country import countries

country = countries['AT']
print(country.df)

There is also a large data frame containing all countries concatenate together:

from forest_puller.hpffre.concat import df
print(df)

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