GeoMAD

GeoMAD is a rich new data service that condenses an entire year’s worth of satellite viewing into a handful of images. By combining geomedian and Median Absolute Deviation (MAD) data, the service allows users to access annual cloud-free mosaics of the African landscape, as well as to view statistical variation over time. The data is available on an annual basis from 2017 to 2020.

GeoMAD data can be used to inform decision making on crucial sustainability issues such as water resourcing, flooding, coastal erosion, land degradation, food security and urbanisation, and is of particular significance when analysing areas with heavy cloud cover.

You can explore the versatility of GeoMAD data from Digital Earth Africa Map by navigating to Annual GeoMAD(Sentinel-2) in the catalog and selecting options from the “Style” dropdown menu.

  • Birds-eye view: inspect continental Africa from above as it appears to the human eye by selecting Geomedian - Red, Green, Blue
  • Highlight vegetation and water features using the infrared spectrum with Geomedian - SWIR, NIR, Green
  • Visualise metrics of annual change using Median Absolute Deviations MADs - SMAD, EMAD, BCMAD or focus on arid land variation with MADs (desert) - SMAD, EMAD, BCMAD
  • Identify likely areas of green vegetation with the Normalised Difference Vegetation Index, calculated in NDVI - Red, NIR
  • Locate water and water bodies with commonly-used indices: Normalised Difference Water Index NDWI - Green, NIR *and the Modified Normalised Difference Water Index *MNDWI - Green, SWIR
  • Assess water quality using the Normalised Difference Chlorophyll Index *NDCI Red Edge, Red*

For more information about GeoMAD, view the product details here or explore the Map.

Comosite GeoMAD of Africa

Water Resources

Our first continental-wide service allows anyone to better understand water availability anywhere in Africa. It translates years of satellite imagery of surface water, known as Water Observations from Space (WOfS), into easy to consume information on the presence, location and recurrence of water within Africa.

This allows countries across Africa to map, assess, visualise, and manage water resources and understand trends over time.

WOfS is currently in beta form, which means that we are confirming its accuracy. A fully operational service with accuracy assessment will be released in early 2021.

Access our water services from our platform.

This video demonstration shows users how to use the DE Africa map, and how to run a sandbox notebook for measuring water extent

Food Security

Our second continental-wide service, currently in development, will help to accurately define farmland areas and the change to crops over time. A cropland area map translates satellite imagery to calculate cropland annual extent to understand how agriculture productions change over time due to climate or farming practices.

Access our crop map services from our platform.

This video demonstration shows users how to run a sandbox notebook for measuring crop health

Other Analysis Tools

We have a number of other analysis tools currently available for use in the DE Africa Sandbox, including the following examples.

Monitoring coastal erosion along Africa’s coastline

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Coastal_erosion.ipynb)

In this notebook, we combine data from the Landsat 5, 7 and 8 satellites with image compositing and tide filtering techniques to accurately map shorelines across time, to identify areas that have changed significantly between 1987 and 2019.

Monitoring chlorophyll-a in African waterbodies

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Chlorophyll_monitoring.ipynb)

In this notebook, we measure the NDCI for Lake Bosomtwe, which is being affected by the pollution. This is combined with information about the size of the waterbody, helps visualise how the water-level and presence of chlorophyll-a changes over time.

Monitoring Mangroves

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Mangrove_analysis.ipynb)

In this notebook the baseline extent of mangroves is provided as a shapefile, showing the locations of mangroves across the entire world. The shapefile used by this notebook is a country specific extraction from the global shapefile provided by GMW (from Bucomil, Guinea-Bissau). The purpose of this notebook is to use the extracted shapefile as a baseline for classifying mangroves through NDVI thresholding.

Modelling intertidal evaluation using tidal data

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Intertidal_elevation.ipynb)

In this notebook we monitor the rise and fall of the tide to reveal the three-dimensional shape of the coastline by mapping the boundary between water and land across a range of known tides (e.g. from low tide to high tide). Assuming that the land-water boundary is a line of constant height relative to mean sea level (MSL), elevations can be modelled for the area of coastline located between the lowest and highest observed tide.

Monitoring Wetlands in Africa

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Wetlands_insight_tool.ipynb)

In this notebook we run the Wetlands Insight Tool for the area encompassed by a polygon. The notebook uses a default shapefile that provides an example wetland for running the analysis. The polygons in this shapefile were hand drawn from a basemap, and they are only provided for demonstration purposes, they are not meant to represent the true delineation of any specific wetland.

Detecting change in urban extent

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Urban_change_detection.ipynb)

In this notebook we use Landsat satellite images to examine the change in urban extent between a baseline period and a more recent period. The difference in urban extent (area is square kilometres) between the two periods is calculated, along with a map highlighting the location of urban growth hotspots.

Vegetation Change and Detection

(https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/blob/master/Real_world_examples/Vegetation_change_detection.ipynb)

In this notebook, we measure the presence of vegetation from Landsat imagery and apply a hypothesis test to identify areas of significant change (along with the direction of the change).