- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- Enabling AI / ML workflows with CREODIAS vGPUs
- Satellite remote sensing analyses of the forest
- Satellite-based Urban Heat Island Mapping on CREODIAS
- Old but gold - historical EO data immediately available and widely used on CREODIAS
- CREODIAS for emergency fire management
- AgroTech project as an example of how CREODIAS can be used for food and environmental research
- Monitoring Air Quality of Germany in Pre vs During COVID Lockdown Period
- Common Agricultural Policy monitoring with Earth Observation
- Applications of CREODIAS data
- Meteorological data usage on the CREODIAS platform
- Building added value under Horizon Europe with CREODIAS
- CREODIAS: Introduction to SAR Sentinel-1 data
- Land subsidence and landslides monitoring based on satellite data
- Satellite imagery in support of the Common Agriculture Policy (CAP) and crop statistics
- Useful tools for data processing, available on CREODIAS platform
- CREODIAS for hydrological drought modelling
- CREODIAS for managing Urban Heat Islands
- CREODIAS for Digitising Green Spaces
- CREODIAS for Air Quality
- Advanced data processors on CREODIAS
- CREODIAS for your application
- Solutions for agriculture with CREODIAS
- Earth Observation data for Emergency response
- Security Applications with Satellite Data
- Climate Monitoring with Satellite Data
- Water Analysis on CREODIAS
- CREODIAS for land and agriculture monitoring
- Solutions for atmospheric analysis
- Example of tool usage
- Processing EO Data and Serving www services
- Processing and Storing EO
- Embedding OGC WMS Services into Your website
- GPU Use Case
- Using the EO Browser
- EO Data Finder API Manual
- Use of SNAP and QGIS on a CREODIAS Virtual Machine
- Use of WMS Configurator
- DNS as a Service - user documentation
- Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
- Load Balancer as a Service
- Jupyter Hub
- Use of CREODIAS Finder for ordering data
- ESRI ArcGIS on CREODIAS
- Use of CEMS data through CREODIAS
- Searching, processing and analysis of Sentinel-5P data on CREODIAS
- ASAR data available on CREODIAS
- Satellite remote sensing analyses of the forest
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Integration Guides
- OGC API
- Custom Processing Scripts
- Legal Matters
- Partner Services
- About Us
Example of usage
CREODIAS for emergency fire management
Wildfires are one of the greatest threats to the ecosystems and the potential losses cover all its elements: wildfires affect soil cover, vegetation, fauna mortality, and the atmosphere, releasing greenhouse gases that affect the composition and its functioning, but also wildfires are a threat to human life and the economy, especially in places where the occurrence of wildfires is not a typical phenomenon.
The use of Copernicus data allows for cost-effective, accurate, up-to-date (in the near real-time), and efficient analysis of vast areas in the assessment of fire risk, as well as the analysis of the fire area and the extent of degradation of the natural environment. CREODIAS platform, which is a cloud computing environment integrated with approx. 30 PB of EO data, enables effective mapping of wildfires as well as assessment of environmental losses. This is made possible by direct and immediate access to near real-time satellite data, as well as the entire archive. The computing environment linked to the satellite data repository allows for the analysis of huge amounts of data without the need to download it into the user's infrastructure.
Fire hazard analysis
Fire risk management let us take preventive measures and efficiently manage a rescue operation in the event of a fire. Emergency services can receive precise information on the scale and location of the fire. For these fire detection purposes, a wide range of ready-to-use satellite-derived products is generated in a frame of the Copernicus programme. These include e.g.
- - Fire Radiative Power (FRP) standard product derived from Sentinel 3 that can be used to monitor the amount of burned biomass, as these measurements constitute an accurate, validated, and daily updated dataset with surveillance of the entire area.
- - NDMI Normalized Difference Moisture Index (NDMI) Moisture Index includes canopy stress analysis, productivity prediction and modelling, fire hazard analysis, and studies of ecosystem physiology.
- - Land Surface Temperature (LST) from the SLSTR products the temperature of the top surface when in bare soil conditions, and the effective emitting temperature of vegetation "canopies" as determined from the view of a canopy top. (SentinelCopernicus)
Burnt area analysis
The high spectral resolution of the Sentinel-2 satellites let us conduct an accurate study of the area exposed to fire.
Fig. 1 Comparison of the spectral response on healthy vegetation and burned areas. Source: U.S. Forest service.
Thanks to the use of simple remote sensing indicators, we can highlight the fluctuation of radiation reflection in these ranges and quickly determine the range of the area where the fire occurred:
- Normalized Difference Vegetation Index (NDVI) has been widely used as an indicator of land cover structure, biomass, or vegetation condition and vigor can also be used to study land cover changes, especially the consequences of natural disasters, including wildfires.
- Normalized Burn Ratio (NBR) and Burn Area Index (BAI) are the fire intensity indicators that provide information about the extent to which the area has been burnt and about the degradation of vegetation
Fig 2. Illustration of fire intensity versus burn severity. Source: U.S. Forest Service.
NBR index calclulations on the Jupyter notebook
In 2021 several hundred people were evacuated from their houses in an area of the city of Oristano, Sardinia. The fire destroyed 20,000 hectares of forests. Fire risk due to wind was defined as extreme.
Fig. 3 Wildfire on the true colour composition, CREODIAS, Sentinel2, Sardinia 2021.
Normalized Burn Ratio (NBR) is calculated as a ratio between the NIR and SWIR values to the point burned area. By creating a simple Python script to calculate the NBR index in the Jupyter notebook we can estimate the size of burn severity. The NBR indicator highlights areas where a fire has occurred.
Fig 4. Simple Python script used to calculate NBR index in the Jupyter notebook available on the CREODIAS.
Fig. 5 NBR Index made by implementing Jupyter notebook Python script on the CREODIAS, Sentinel-2, Sardinia 2021.
Solution on CREODIAS
CREODIAS providing both resources of computing power in virtual machines and access to the Copernicus services products, let users create a suitable environment for both public and private sectors to monitor or provide early warning of fires on a regional or national scale. By using satellite data users can find spatial patterns that may provide improvements in the emergency management and the analysis of the fire effects. CREODIAS data repository and cloud computing services enable users to automate processes and calculate big datasets to better understand the phenomena occurring in the natural environment, especially in hard-to-reach locations.
Author: Maciej Jurzyk, Earth Observation Product Specialist at CloudFerro