Monitoring changes in forest extent¶

Keywords data used; Global Forest Change

Background¶

Forests worldwide are in a state of flux, with accelerating losses in some regions and gains in others (Hansen et al., 2013). The Global Forest Change 2000-2021 dataset characterizes the global forest extent and change from 2000 to 2021.

For this dataset: - Forest loss is defined as stand-replacement disturbance, or a change from a forest to non-forest state - Forest gain is defined as the inverse of as the inverse of loss, or a non-forest to forest change entirely within the study period - Tree cover is defined as canopy closure for all vegetation taller than 5m in height.

The Year of gross forest cover loss event (lossyear) layer shows the forest loss during the period 2000 to 2021. Forest loss is encoded as either 0 (no loss) or else a value in the range 1-20, representing loss detected primarily in the year 2001-2021, respectively. The Tree canopy cover for year 2000 (treecover2000) layer shows the tree cover in the year 2000. The Global forest cover gain 2000–2012 (gain) layer shows the forest gain duirng the period 2000 to 2012. Forest gain is encoded as either 1 (gain) or 0 (no gain).

Description¶

This notebook provide an interactive tool for selecting, loading and plotting the Global Forest Change “lossyear”, “treecover2000” and “gain” layers in order to monitor forests.

Getting started¶

Uncomment the lines in the cell below to upgrade the panel module and install the selenium python module. After installing the modules, restart the kernel of this notebook and proceed to Load packages.

[1]:

#!python -m pip install --upgrade panel selenium


To run this analysis, run all the cells in the notebook, starting with the “Load packages” cell.

Import Python packages that are used for the analysis.

[2]:

from deafrica_tools.app import forestmonitoring

/usr/local/lib/python3.8/dist-packages/dask/dataframe/utils.py:367: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
_numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)
/usr/local/lib/python3.8/dist-packages/dask/dataframe/utils.py:367: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
_numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)
/usr/local/lib/python3.8/dist-packages/dask/dataframe/utils.py:367: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
_numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)
/usr/local/lib/python3.8/dist-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
warnings.warn(