# 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 during 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¶

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.

[1]:

from deafrica_tools.app import forestmonitoring

/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 (3.10.3-CAPI-1.16.1). Conversions between both will be slow.
warnings.warn(


## Digital Earth Africa forest monitoring app¶

Run the forestmonitoring.forest_monitoring_app() cell below to launch the interactive application that is used to select the area to load and plot the Global Forest Change layer.

### Selecting parameters¶

On the left of the map, you will see a list of basemaps you can use for plotting and a list of Global Forest Change layers you can load:

Global Forest Change layers available

Year of gross forest cover loss event

Shows forest loss during the period 2000–2021

Global forest cover gain 2000–2012

Shows forest gain during the period 2000-2012

Tree canopy cover for year 2000

Show the tree canopy cover for the year 2000

All layers

Show the forest loss during the period 2000–2021, forest gain during the period 2000-2012 and the tree canopy cover for the year 2000

Use the Forest Cover Loss Time Range slider to set the time range for which to load the Year of gross forest cover loss event layer. Tick the Override maximum size limit box to override the app’s default 500 square kilometres area limit. This can be used to load larger areas of imagery, but should be used with caution as it can lead to memory issues or crashes.

### Selecting a location to plot¶

Zoom into the map to a location you wish to plot. Select the Draw a rectangle tool or the Draw a polygon tool on the left of the map, and draw a shape around the forest area you are interested in. When you are ready, press the blue Generate plot button on the bottom left to plot the Global Forest Change layer selected for the area drawn on the map.

Optional: Upload a GeoJSON or ESRI Shapefile containing the area of interest geometry using the Upload button (uploaded files should be less than 5 megabytes in size).

If running the notebook for the first time, keep the default settings above. This will demonstrate how the analysis works and provide meaningful results.

[2]:

forestmonitoring.forest_monitoring_app()


## Next steps¶

When you have exported your first plot, select a different area and rerun the analysis by clicking Generate plot.

Contact: If you need assistance, please post a question on the Open Data Cube Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on Github.

Compatible datacube version:

[3]:

import datacube
print(datacube.__version__)

1.8.8


Last Tested:

[4]:

from datetime import datetime

datetime.today().strftime("%Y-%m-%d")

[4]:

'2022-11-11'