Normalized Difference Vegetation Index (NDVI)

"The normalized difference vegetation index (NDVI) is a standardized index allowing you to generate an image displaying greenness, also known as relative biomass. This index takes advantage of the contrast of characteristics between two bands from a multispectral raster dataset—the chlorophyll pigment absorption in the red band and the high reflectivity of plant material in the near-infrared (NIR) band.

The documented and default NDVI equation is as follows:

NDVI = (NIR - Red) / (NIR + Red)

    NIR = pixel values from the near-infrared band
    Red = pixel values from the red band

This index outputs values between -1.0 and 1.0." (ESRI, 2018)

Sources

"Indices gallery". ArcGIS Pro, ESRI. 2018. 
http://pro.arcgis.com/en/pro-app/help/data/imagery/indices-gallery.htm.
Accessed February 1, 2019.

Related Content

Article

Using space-based technologies to predict mosquito-borne disease outbreaks

Mosquitos are often cited as one of the deadliest animals in the world, causing up to one million deaths per year (WHO, 2020; CDC, 2021). They can carry and transmit a variety of diseases, including malaria, West Nile virus, dengue fever, and Zika virus; transmitting illness across the globe (Figure 1). To help decrease the burden of disease resulting from mosquitos, researchers are utilising satellite data and remote sensing models to better predict where mosquito breeding grounds may occur in the future.

Geospatial analysis of climate change induced drought using NDVI and LST

Ethiopia, like many developing countries, faces significant threat from droughts triggered by climate change. The country's heavy reliance on agriculture for production, export revenues, and employment makes it highly susceptible to climate change-induced challenges, such as frequent floods, droughts and rising temperatures. Therefore, this research aims to assess drought-prone areas in Meyo district, Borena Zone, thereby contributing to the attainment of SDG 13.1 and the creation of a more resilient and sustainable future in the face of climate change. To achieve the objective, the study employs the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as indicators and the drought risk map was developed using weighted overlay analysis. Landsat images and rainfall datasets from December in the years 2002, 2012, and 2022 were analyzed to track changes. The result reveals a clear inverse relationship between NDVI and LST, where higher temperatures coincide with decreased NDVI values, signifying vegetation stress caused by reduced water availability. The study also highlights the deficient rainfall and high drought vulnerability in the norther and eastern parts of the study area. The provided drought risk map classifies areas into Low, Moderate, and High risk, illustrating the evolving drought scenario and it signifies increasing severity of drought risk in recent years, particularly from 2012 to 2022. The finding holds vital information for decision-makers, policymakers, and stakeholders in devising effective strategies to mitigate the adverse effect of drought and build resilience in the of climate change.

Interview with Sarhan Zerouali

Sarhan Zerouali became fascinated with water at a young age through learning about water scarcity around the world and about traditional methods for locating groundwater. In a space applications course Sahran then learnt about space-based technologies. He is currently working on a research project on how remote sensing and other technologies can help alleviate global challenges arising from land degradation. As an aerospace engineer, Sahran has worked with various modern technologies in his work including nanosatellites, artificial intelligence, and feature extraction algorithms.

Interview with Sarhan Zerouali

Sarhan Zerouali became fascinated with water at a young age through learning about water scarcity around the world and about traditional methods for locating groundwater. In a space applications course Sahran then learnt about space-based technologies. He is currently working on a research project on how remote sensing and other technologies can help alleviate global challenges arising from land degradation. As an aerospace engineer, Sahran has worked with various modern technologies in his work including nanosatellites, artificial intelligence, and feature extraction algorithms.

Launch of Zimbabwe's first Satellite ZIMSAT - 1

What began as the development of a cubesat (BIRD-5) at the Kyushu Institute of Technology in Japan took off on a spacecraft to the International Space Station from the Mid-Atlantic Regional Spaceport at the National Aeronautics and Space Administration's (NASA's) Wallops Flight Facility in Virginia, US on 6 November 2022 (watch the video of the launch of the CRS2 NG-18 (Cygnus) Mission (Antares), in the video below the article).

Capacity Building and Training Material

Digital Earth Africa: Agriculture and Food Security

Digital Earth Africa learning platform

This learning platform helps users understand the significance of Earth observations, explore Digital Earth Africa datasets through an interactive map, and get started on the basics of python coding for spatial analysis.

Digital Earth Africa makes Earth observation (EO) data readily available, delivering decision-ready products to the African continent. Data generated by Digital Earth Africa will provide valuable insights for better decision-making across many areas, including resource management, food security and urbanisation.

Space-based Solution

Addressed challenge(s)

Samburu tribe lacks access to safe drinking water - Dry spells due to water scarcity

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

Suggested solution   

Rainwater harvesting is a crucial solution for water scarcity in semi-arid countries like Kenya. Kenya’s arid and semi-arid lands (ASALs) cover 80% of its territory, making rainwater harvesting essential. There are various reasons why this approach can be beneficial in Samburu County.  

  • Water Scarcity Mitigation: Semi-arid regions face unpredictable rainfall and frequent droughts, exacerbated by climate change. Rainwater harvesting captures the little rainfall received, providing a reliable water source. 
  • Sustainable Water Supply: Rainwater harvesting techniques include small planting basins, trenches, stone bunds, and grass strips. These structures redirect runoff toward crops and pastures. By capturing rainwater, communities can sustain livestock, crop production, and domestic needs. 
  • Environmental Resilience: Droughts in Kenya are becoming more frequent due to environmental degradation and climate change. Rainwater harvesting helps mitigate
  • the impact of these droughts.  
  • Cost-Effective and Low-Tech: Rainwater harvesting doesn’t require complex infrastructure. It utilizes existing resources effectively. 

Outline of the solution

Steps to be taken: 

  1. Rainy season identification: the rainy season in the selected area needs to be identified. Further, the precipitation data from the past three to ten years needs to be studied.
  2. A geological study needs to be developed, this includes the study of the geology of the region, the development of geological maps, digital elevation model (DEM) maps, and normalized difference vegetation index (NDVI) map. 
  3. Precipitation maps of the location need to be developed. 
  4. Site Selection: Identify suitable locations for rainwater harvesting. Factors such as rainfall patterns, topography, and proximity to communities need to be considered. Choose areas with consistent rainfall during specific seasons. 
  5. Catchment area: Determine the catchment area where rainwater will be collected. Common catchment surfaces include rooftops, roads, or open fields. Ensure that the catchment area is clean and free from contaminants. 
  6. Conveyance system: Design an efficient system to channel rainwater from the catchment area to storage facilities. Components include gutters, downspouts, pipes, and first-flush diverters. Proper sizing and maintenance are crucial. 
  7. Storage tanks or reservoirs: Select appropriate storage options based on community needs. Common choices include: 
  8. Roof catchment tanks: Placed near buildings to store rainwater from rooftops. 
  9. Ground-level tanks: Buried or partially buried to store larger volumes. 
  10. Rock catchments: Natural depressions or excavated pits lined with impermeable materials. 
  11. Consider tank capacity, material durability, and accessibility for maintenance. 
  12. Water quality and treatment: Rainwater may contain impurities. Implement filtration systems to improve water quality. Use first-flush diverters to discard initial runoff (which may contain debris). 
  13. Climate resilience: Adapt the project to changing climate conditions. Monitor rainfall patterns and adjust storage capacity accordingly. 

Accomplished progress

Steps 1 - 3 have been successfully accomplished. Step 2 was developed in another space-based solution.

  • Rainy season identification 
    Decadal Precipitation in Kenya
    Figure 1: Decadal Precipitation in Kenya. Precipitation information during 21-31 December 2023. (Source: Dekadal Rainfall (meteo.go.ke))

     

    • Precipitation data from at least the last three years: CHRIPS  
    Decadal rainfall data
    Figure 2: Decadal rainfall in mm from July 2020 to July 2023. (Source: Dekadal Rainfall (meteo.go.ke))

     

    •  Digital elevation Model (DEM)
    Samburu DEM map
    Figure 3: Precipitation DEM map made with QGIS. Version 3.32.3 / Version 3.28.11 LTR. 

     

    • Precipitation maps 
    samburu precipitation map
    Figure 4: Precipitation map from 2023 made with QGIS. Version 3.32.3 / Version 3.28.11 LTR. The yellow areas indicate heavy rainfall, the green areas indicate moderate rainfall. 

     

    samburu precipitation map
    Figure 5: Precipitation map from 2024 made with QGIS. Version 3.32.3 / Version 3.28.11 LTR. The yellow areas indicate heavy rainfall, the green areas indicate moderate rainfall.  

     

    Development of precipitation maps 

    To create a precipitation map in QGIS, raster data representing precipitation values is needed. Therefore, the data is added to a raster layer in the QGIS project. 

    Steps to create a raster layer to map precipitation data: 

    1. Obtain precipitation data: First, obtain precipitation data from a reliable source in a compatible format. Common formats for precipitation data include GeoTIFF (.tif), NetCDF (.nc), or ASCII grid (.asc) files. 
    2. Open QGIS: Launch QGIS on your computer. QGIS is open for download: Download QGIS  
    3. Add Raster Layer:  
    • Go to the "Layer" menu and select "Add Layer"> "Add Raster Layer". 
    • Or click the "Add Raster Layer" button in the toolbar. 
    • Alternatively, use the shortcut Ctrl+Shift+R. 
    1

     

    1. Browse for Precipitation Data: In the "Data Source Manager" dialog that appears, navigate to the directory where your precipitation data is stored. 

    1. Select precipitation data file: Select the precipitation data file for the map. Make sure to choose the correct file format that matches the data (e.g., GeoTIFF, NetCDF, ASCII grid). 

    2

     

    6. Add Layer to the map: Once the precipitation data file is selected, click "Open" or "Add" to add the raster layer to your QGIS project. 

    7. Display the precipitation map: Now that you've added the precipitation data as a raster layer, you can visualize it on the map canvas in QGIS. Depending on the spatial resolution and coverage of your precipitation data, you may need to zoom or pan the map to view the data effectively. 

    3

     

    Once the precipitation data is added as a raster layer, the map layout can be customized to include legend, latitude and longitude factors, title, and other properties using the Print Layout functionality.  

    1. Open Print Layout: Go to the "Project" menu and select "New Print Layout" to create a new print layout. Give your layout a name and click "OK". 

      4

       

      5
    2. Add Map to Layout: In the print layout view, click on the "Add Map" button in the toolbar, then click and drag to create a rectangle where the map is to appear on the layout. 

      6

       

      7

     3. Add Legend: Click on the "Add Legend" button in the toolbar, then click and drag to create a rectangle where the legend is to appear on the layout. 

    4. Add Title: Go to the "Layout" menu and select "Add Item" > "Label". Click and drag to create a rectangle where the title is to appear on the layout. Double-click on the label element to edit the text and customize the font, size, and style. 

    5. Add Other Elements: You can add additional elements such as scale bars, north arrows, text boxes, images, and annotations using the "Add Item" menu in the toolbar.  

    8

     6. Add Latitude and Longitude Grid: Go to the "Layout" menu and select "Add Item" > "Map Grid". Click and drag to create a rectangle where the grid is to appear on the layout. ​ 

    9

     

    • Configure Grid Properties: Double-click on the grid element to open the "Item Properties" panel. Here, you can configure various properties of the grid, including: 
    • Grid Type: Choose between "Frame and Annotations", "Grid Lines", "Annotation Only", or "Frame Only" depending on the desired appearance. 10

       

    • Interval Units: Choose the units for the grid intervals (e.g., degrees for latitude and longitude). 
    • Interval X and Y: Set the interval for latitude and longitude gridlines. 
    • Annotation X and Y: Choose whether to annotate the gridlines with latitude and longitude values. 11

    • Customize Appearance (Optional): You can further customize the appearance of the gridlines, such as line style, color, and labeling options, using the options available in the "Item Properties" panel. 12

    Export and save: the layout can be exported to various formats such as PDF, image files, or print directly from QGIS. 

    13

    Future steps

    Steps 5-13 will be developed once step 3 (site selection) has been developed: 

    • Determine if enough water can be stored during the rainy season to last the dry season.
    • Determine seasonal river location and river width: determines the optical data that can be used (due to spatial resolution) 
    • Sediment load of the river 
    •  outcrops in bed and bank:  Ideally don’t want to have to excavate >5m deep    
    • Existing scoop holes on the river existing far into the dry season (indicates good water storage already) 
    • Vegetation on the banks (indicates localized water source) 
    • Slope of riverbanks (shouldn’t be too shallow) 
    • Slope of riverbed (ideally 1 - 5 %) 
    • Stream length 
    • Catchment area (from DEM) 
    • Location of faults and fractures  

    Finally, for the implementation of the rainwater harvesting plan, sediment samples from the selected seasonal river need to be studied. 

    Relevant publications
    Related space-based solutions

    Flood modeling for melting glacier - need for input

    Identify upstream potential pollution sources - in development

    Surface water extent river course - in development

    Construction of sand dams for Samburu County - in development

    Rainwater harvesting in Samburu County – in development

    Determining optimum sites for rainwater harvesting - in development

    Vegetation classification for land of Maori communtiy - in development

    Water suitability map (Samburu County, Kenya) - in development

    Keywords (for the solution)
    Climate Zone (addressed by the solution)
    Dry
    Habitat (addressed by the solution)
    Region/Country (the solution was designed for, if any)
    Relevant SDGs