Change Detection - IPT (beta)

Harris Change Detection - Powered by USGS Landsat 8 data
  • Select between multiple change indices
  • Detect burnt areas
  • Visualize urban changes
  • Up to 10 m resolution
  • On demand production
  • Powered by Sentinel-2 and Landsat 8 data
  • During beta phase: 1 € per request

Harris Change Detection provides to you maps which highlight and quantify differences between Sentinel-2 or Landsat 8 images. A broad range of methods is available to identify and describe changes e.g. due to construction in urban areas or due to forest fires. This service releases remote sensing experts and non-experts from downloading imagery, setting up processing infrastructure and handling large satellite imagery.

Click one of the buttons on the right to configure your order. Depending on which data basis you want to use please select Sentinel-2 or Landsat 8. After you configured your service as described copy the configuration ID from the pop up window, press the OK-button, paste the ID into the remarks field on this page and proceed with your order. You will receive the results as geotiffs or shapefiles ready to download shortly after you have completed your order. Please contact us for details.

Please login to select options

Change Detection - IPT



Powered by 

Landsat 8, Sentinel-2

Operated in cooperation with 

EOcloud of Cloudferro

On demand 



Sentinel-2: 10 m, Landsat 8: 30 m

Access Mode 



Shapefile, GeoTIFF, PNG



Delivery time 

Typically within 30 minutes


Recommended use 

The Harris Change Detection encompasses a broad range of methods used to identify, describe and quantify differences between images of the same scene at different times or under different conditions.

Harris Change Detection is a product that provides insurance companies, property owners, real estate, construction companies, research institutions with a better insight into the changes through time for a selected area.

The image difference and indices options can be used for a wide range of applications such as land-use and land-cover (LULC) changes, landscape management (urban and forest areas, wetlands) and field monitoring.

Harris Change Detection is a cost-effective solution for detecting landscape changes quickly and effectively. Save time and extra costs from on-site surveys, image processing or searching and downloading available satellite imagery. Configure your customized change detection within a few clicks and your ready-to-use map will be available in minutes.

Product details 

The Harris Change Detection encompasses a broad range of methods used to identify, describe, and quantify differences between images of the same scene at different times or under different conditions. Please find a few examples below.

  • The "Image difference" option produces the differences between any pair of initial state and final state images. The difference is computed by subtracting the initial state image from the final state image.
  • Spectral indices are combinations of surface reflectance at two or more wavelengths that indicate relative abundance of features of interest. Vegetation indices are the most popular type, but other indices are available for burned areas, man-made (built-up) features, water, and geologic features.
    • The "Normalized Burn Ratio" option (NBR) is used for highlighting burned areas in large fire zones greater than 500 acres. The formula uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths. The NBR was originally developed for use with Landsat TM and ETM+ bands 4 and 7, but it will work with any multispectral sensor with a NIR band between 0.76-0.9 µm and a SWIR band between 2.08-2.35 µm.
      NBR = (NIR-SWIR) / (NIR + SWIR)
    • The "Normalized Difference Built-up Index" option (NDBI) highlights urban areas where there is typically a higher reflectance in the shortwave-infrared (SWIR) region, compared to the near-infrared (NIR) region. Applications include watershed runoff predictions and land-use planning.The NDBI was originally developed for use with Landsat TM bands 5 and 4. However, it will work with any multispectral sensor with a SWIR band between 1.55-1.75 µm and a NIR band between 0.76-0.9 µm.
      NDBI = (SWIR - NIR) / (SWIR + NIR)


  1. Lopez Garcia, M., and V. Caselles: Mapping Burns and Natural Reforestation using Thematic Mapper Data. Geocarto International 6 (1991): 31-37
  2. Key, C. and Benson, N.: Landscape Assessment: Remote Sensing of Severity, the Normalized Burn Ratio; and Ground Measure of Severity, the Composite Burn Index." In FIREMON: Fire Effects Monitoring and Inventory System, RMRS-GTR, Ogden, UT: USDA Forest Service, Rocky Mountain Research Station (2005).
  3. Zha, Y., J. Gao, and S. Ni: Use of Normalized Difference Built-Up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing 24, no. 3 (2003): 583-594.

Please note 

  • The product is still a beta version. Please let us know if you encounter any problems at We are happy to get your feedback.
  • Only one configuration ID per order is possible!
  • Within the current beta version, the area per request is limited to 500 skqm.

Pricing / Licensing 

The pricing is based on numbers of request and is independent of the size of the AOI and the configuration of the change detection. CloudEO Terms & Conditions apply.