Burning Forests: Tools for Tracking and Reporting Wildfire Damage
If you’ve seen reports of a wildfire in your region and you’re looking for open source data, NASA’s fire-tracking tool is often the first place to start. It provides a heat signature and an approximate location. But detection is only the first step in understanding what’s happened. In this guide, we
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If you’ve seen reports of a wildfire in your region and you’re looking for open source data, NASA’s fire-tracking tool is often the first place to start. It provides a heat signature and an approximate location. But detection is only the first step in understanding what’s happened. In this guide, we explore ways to analyse and report on the scale and severity of wildfires, including those in protected areas where ecosystems are often most fragile. We also examine how often fires recur in the same region over multiple seasons, helping to identify patterns in fire activity as climate change reshapes fire risk around the world.
Satellite imagery from Copernicus Browser will be used to visualise the spread of the fire, and vegetation health indices to assess burn severity. The datasets will then be combined in QGIS for more in-depth analysis. At each stage, suggestions will be offered for turning the data into clear, reportable findings.
Throughout this guide, a single case study will be used: Sicily’s Zingaro Nature Reserve. In 2025, wildfires swept across the region, destroying forests, grasslands and croplands. Located on the Capo San Vito peninsula, the reserve was so severely affected that sections remain closed today.
Visualising Scorched Earth
When investigating a wildfire, it’s important to narrow down whenit occurred and whereit spread. The Landsat and Sentinel-2 missions are well-suited to this task, providing regular free imagery of most of the Earth’s landmass.
Below are two sets of Sentinel-2 imagery showing conditions shortly before and after a fire on July 25, 2025,near Capo San Vito, Sicily. The top two images are true-colour, similar to what would be seen from an aeroplane window. The image on the top right shows an area of scorched earth on the eastern side of the peninsula, but the exact extent of the fire is difficult to determine because the colour of the ground has changed only slightly.
Satellite images of Capo San Vito, Sicily, showing before (left) and after (right) a fire on July 25, 2025. Top row: true-colour imagery. Bottom row: false-colour imagery highlighting fire damage in red. Source: Contains modified Copernicus Sentinel data 2025, processed with Copernicus Browser.
The bottom two images are false-colour and highlight the difference between healthy vegetation and burned areas. Such imagery is possible because Sentinel-2 captures bands of light outside the visible range, a technique known as multispectral imaging. In these images, the near-infrared (NIR) band is coloured green, and the shortwave infrared (SWIR) band is coloured red. Healthy vegetation mainly reflects NIR light, so it appears green, while burned areas mainly reflect SWIR light, so they appear red.
If you’d like to try Copernicus Browser without further explanation, you can go straight to the false-colour post-fire image here.
To follow along step by step, first open Copernicus Browser. Then to visualise Sentinel-2 imagery:
Zoom to the desired area on the map or use the search bar (San Vito Lo Capo, north-west Sicily)
Select the date of interest (‘2025-07-27’ selected below).
Select the layer of interest (‘True color’ by default; SWIR selected below).
Screenshot of Copernicus Browser. Annotations by Bellingcat.
By identifying the last available image before the fire and the earliest image after it in which the full burn area is visible, it’s possible to establish the location and timeline of the fire.
This allows us to report the following finding: “Satellite imagery reveals the extent of the damage caused by wildfires across the Capo San Vito peninsula on Sicily’s northern coast between July 20 and July 27, 2025.”
Extra exercise: Look up a recent fire (e.g., wildfires near Penco, Chile in January 2026), navigate to the affected location and try to visualise the burned area using Copernicus Browser.
Quantifying the Burned Area
The visibly scorched area can be measured using the Area of Interest tool (highlighted below), which allows users to draw a polygon on the map and calculate the total area in square kilometres. (Once drawn, keep the polygon in the editor, as it will be used again later.)
Estimated burn area of 53.97 km2 using the Area of Interest tool. Screenshot of Copernicus Browser. Annotations by Bellingcat.
Reportable finding: “The wildfire that swept across Sicily’s Capo San Vito peninsula in 2025 burned more than 50 km2 of the peninsula, according to Sentinel-2 data.”
Repeatedly measuring the burned area over time allows you to follow the progression of a fire. This method was used by Bellingcat when covering the Etosha National Park wildfire in late September 2025.
Extra exercise: Replicate the analysis of the Etosha National Park fire from this Bellingcat article.
Assessing Burn Severity
Some fires only affect surface vegetation, while others scorch the ground and cause long-lasting damage. Burn severity can be measured using an index called the Normalised Burn Ratio (NBR).
How Does the Normalised Burn Ratio (NBR) Detect Burned Areas?
The spectral response of a material describes how reflective it is to different types of light. The graph below shows the difference between healthy vegetation and bare soil in terms of the amount and types of light they reflect.
Reflectance data reproduced from the ECOSTRESS Spectral Library using Conifer for Healthy Vegetation and Black Loam for bare soil. Graphic by Bellingcat.
By focusing on the NIR and SWIR bands, where reflectivity differs significantly between healthy vegetation and bare soil left after a burn, an index can be calculated:
NBR = (NIR – SWIR) / (NIR + SWIR)
A high NBR indicates healthy vegetation, while a low NBR indicates burned areas.
Copernicus Browser doesn’t include a default NBR layer, but it can be added via a custom script, as shown in the screenshot below:
Load the script by clicking the green circular arrows to the right of the URL.
Click ‘Apply’ (you may need to scroll down).
Alternatively, you can skip these steps and go straight to the custom NBR post-fire image here.
Screenshot of Copernicus Browser. Annotations by Bellingcat.
The NBR layer displays positive values in green (healthy vegetation) and negative values in purple (burned areas), making the boundary of the scorched area much clearer than before.
To calculate the change in NBR in Copernicus Browser, use the Statistical Information tool (a free account is required to access this feature).
Within the date selector, choose a date a few weeks or months after the fire.
Using the Area of Interest polygon, select the ‘Statistical Info chart’ icon.
Set the maximum cloud cover to around 30% using the slider in the top right.
Select a date range that captures the available data surrounding the fire (July 20-27 shown below).
Identify when the fire occurred on the graph (this will be marked by a sharp drop in the NBR, as shown below).
Hover over the points on the graph immediately before and after the fire to display the mean value.
Composite of screenshots from within Copernicus Browser.
In this example, the pre-fire image had an average NBR of 0.11 and the post-fire image had an average NBR of -0.18. The NBR decreased by 0.29, which represents a moderate burn.
Severity Level
Change in NBR
Unburned
Less than 0.100
Low
0.100 – 0.269
Moderate
0.270 – 0.659
High
0.660 or greater
Burn severity table from the US Forest Service (page 38), simplified by Bellingcat.
Reportable finding: In late July, the fire, which scorched more than 50km2 of Sicily’s Capo San Vito peninsula, was deemed moderately severe according to the US Forest Service guidelines.
Extra exercise: Find a custom visualisation script of interest from this repository and explore what it does.
Wildfires in Conservation Areas
By focusing on protected sites such as nature reserves and national parks, we can begin to assess how wildfires affect areas of high conservation value. Controlled burns are widely used in agriculture and land management, but unchecked fires in protected areas risk eroding fragile ecosystems.
The proportion of the Zingaro Nature Reserve that was damaged by the fire can be estimated by combining the NBR image created in Copernicus Browser with a dataset from Protected Planet, a global map of protected areas that includes nature reserves.
QGIS, a program for working with geographic data, is well-suited for this type of analysis. Download and install QGIS on your computer. For help with this step, refer to the QGIS installation guide.
To download the NBR image from Copernicus Browser:
With the NBR visualisation selected, click the ‘Download’ icon.
Switch tabs at the top from ‘Basic’ to ‘Analytical’.
Change the image format to ‘TIFF (32-bit float)’.
Change the image resolution to ‘HIGH’.
Change the coordinate system to ‘Popular Web Mercator (EPSG:3857)’.
Toggle the ‘Clip extra bands’ switch to the off position (see image below).
Select the ‘Custom’ layer check box (and deselect any others).
Click ‘Download’.
Wait. It could take several minutes for the image to be generated and downloaded.
Screenshot of Copernicus Browser. Annotations by Bellingcat.
Once the image has downloaded, rename it to NBR.tiff to make it easier to work with.
Next, open QGIS and click ‘New Project’ in the upper left.
Load the image from Copernicus Browser by dragging and dropping the downloaded file into QGIS.
Useful QGIS Terminology
CRS – The Coordinate Reference System describes how the world should be measured and projected. Two of the most common are:
EPSG:4326 – WGS 84, which uses latitude and longitude as the unit of measurement.
EPSG:3857 – WGS 84 / Pseudo-Mercator, which uses metres as the unit of measurement.
Raster – a type of data that uses pixels to represent information (such as satellite imagery)
Vector – a type of data that uses points, lines, and polygons to represent information (such as a burn area polygon).
Processing the NBR Image
Next, we categorise each pixel in the NBR image as burned or unburned.
Previous analysis in Copernicus Browser showed that the Zingaro Nature Reserve’s NBR value dropped below zero only after the fire (before image: mean NBR value on July 20, 0.11; after image: mean NBR value on July 27, -0.18).
We can use this analysis to set a threshold; anything below zero will be categorised as burned.
The QGIS Raster Calculator lets us apply our threshold to the NBR image and create a new layer.
Open the Raster Calculator by selecting ‘Raster > Raster Calculator…’ from the menu bar at the top.
Screenshot of the QGIS Raster Calculator. Annotations by Bellingcat
The Raster Calculator lists the raster bands available in the project. In this example, there are five. These bands are set by the custom script we used in Copernicus Browser and are numbered as follows:
Red
Green
Blue
Pixel validity (not used in this example)
NBR index
To create a new raster layer that applies our threshold on the NBR index band:
Double-click the fifth band (ending ‘@5’) to add it to the expression box at the bottom.
Add < 0 using your keyboard (shown above).
Select the ‘Create on-the-fly raster instead of writing layer to disk’ checkbox.
Click ‘OK’.
The expression NBR@5 < 0 tells QGIS to categorise NBR index values as burned if they are less than zero.
The new layer shows burned areas as white (a value of 1), and unburned areas as black (a value of 0).
Screenshot of QGIS.
Extra exercise: Download an NBR image captured before the fire. Use the Raster Calculator to create a new layer that shows burn severity.
As before, drag and drop the downloaded file into QGIS. This time, the download is a zip file and contains many PDF files as well as the geodatabase file of interest. Scroll down to the bottom of the list and select the ‘gdbtable’ file with a polygon icon on the left side (see the blue highlighted row below), then press ‘Add Layers’.
Screenshot of QGIS. Annotations by Bellingcat.
This adds the nature reserve polygon as a layer in QGIS (and gives it an arbitrary colour). The nature reserve is almost completely contained within the white burned area, indicating it was heavily affected by the wildfire.
Screenshot of QGIS.
Quantifying the Burned Area in the Nature Reserve
To measure the proportion of the nature reserve that was burned by the wildfire, we will use the Zonal Histogram tool from the QGIS Processing Toolbox to count the number of unburned and burned pixels within the reserve polygon.
Open the toolbox with ‘Processing > Toolbox’, and a pane should open to the right. In the Processing Toolbox search field, look up ‘Zonal Histogram’ and double-click the result to open the tool.
To create a new layer:
Set the ‘Raster layer’ to the threshold burn area layer (NBR@5 < 0)
Set the ‘Vector layer containing zones’ to the nature reserve polygon layer (should start with ‘WDPA_’).
Click ‘Run’
Click ‘Close’
Screenshot of QGIS. Annotations by Bellingcat.
This will create a new layer called ‘Output zones’, which is a copy of the nature reserve polygon with pixel counts added.
Select the output layer in the lower left and click ‘Attribute Table’ in the upper right. (The attribute table is a spreadsheet-like view of the data contained in a layer.)
For the output layer, there is just one row because there is only one polygon. If the layer contained many polygons, there would be many rows.
The newly calculated counts are added to the end of the table, so scroll all the way to the right. Look for fields starting with ‘HISTO_’. Here, HISTO_0 is the count of unburned pixels (value of 0), and HISTO_1 is the count of burned pixels (value of 1).
Screenshot of QGIS. Annotations by Bellingcat.
To calculate the proportion of burned area, the number of burned pixels is divided by the total number of pixels.
Proportion = 57413 / (57413 + 2195) = 0.96318…
A value of 0.96318 means that just over 96.3% of the nature reserve burned.
Reportable finding: In late July, more than 95% of the Zingaro Nature Reserve burned in a wildfire, according to Sentinel-2 satellite imagery and Protected Planet data.
Tracking Past Wildfires
To assess the significance of an ongoing wildfire, it is important to place it in historical context. How does it compare with previous fires in the same area? Is it part of a seasonal pattern, or does it represent an unusually severe event?
With coverage dating back to 2008, the European Forest Fire Information System (EFFIS) automatically maps wildfires across Europe, North Africa, and parts of the Middle East.
Fire data can be requested directly from EFFIS using web form, with results delivered by email. For ease, you can also download Bellingcat’s archived copy of EFFIS wildfire data for Italy covering 2015–2025.
For this section, it is best to open a new QGIS project.
To view and analyse historic wildfires in the Zingaro Nature Reserve using EFFIS data:
(Optional) Add the OpenStreetMap layer from the XYZ Tiles category by double-clicking it.
Load the EFFIS data into QGIS. If prompted to select a coordinate transformation, click ‘OK’ to accept the default option.
Load the Protected Planet Zingaro Nature Reserve polygon as described earlier.
Open the Vector Intersection tool by selecting ‘Vector > Geoprocessing Tools > Intersection…’ from the menu bar at the top.
Screenshot of QGIS Annotations by Bellingcat.
The Intersection tool creates a new layer containing only the fires that affected the Zingaro Nature Reserve. To create the new layer:
Set the ‘Input layer’ to the EFFIS fires layer.
Set the ‘Overlay layer’ to the Zingaro Nature Reserve polygon layer.
Click ‘Run’.
Screenshot of QGIS Annotations by Bellingcat.
QGIS functionality can be extended through plugins, including Data Plotly, which adds data visualisation tools. To install Data Plotly, open the Plugin Manager by selecting ‘Plugins > Manage and Install Plugins…’ from the menu bar, then:
Search for ‘Data Plotly’ in the available list.
Select the plugin from the search results.
Click ‘Install Plugin’ to download and install it.
Screenshot of QGIS Annotations by Bellingcat.
Once installed, open the Data Plotly panel with ‘View > Panels > DataPlotly’. The panel should appear on the right-hand side of the QGIS window.
To plot a graph of historic wildfire activity within the nature reserve, configure Data Plotly as follows:
For ‘Plot type’, choose ‘Bar Plot’.
Set the ‘Layer’ to the newly created ‘Intersection’ layer.
In ‘X field’, type “year(initialdat)”. This expression extracts the year from the fire’s approximate start date, allowing events from the same year to be grouped together.
In ‘Y field’ enter “$area/1000000”. This expression calculates the burned area within the nature reserve in square kilometres.
Note: EFFIS data provide initial and final dates for each fire, which are approximate because they depend on the availability of satellite imagery. These dates should be treated as bounds for when a fire occurred, rather than as the dates when it started and ended.
Next, switch to the Layout tab in Data Plotly:
Untick ‘Show Legend’. Only do this for simple plots where a legend is not required.
Add a title and labels for the X and Y axes.
Finally, click ‘Create Plot’ and wait a few seconds for the chart to be generated.
Screenshot of QGIS Annotations by Bellingcat.
The chart shows that the Zingaro Nature Reserve has experienced several significant wildfires over time. However, in 2025, the data show that the fire burned a larger area within the reserve than the major fires recorded in 2020 and 2017.
Bar chart showing the burned area of the Zingaro Nature Reserve between 2015 and 2025.
Reportable finding: The Zingaro Nature Reserve has experienced three major wildfires since 2015. Of these, the 2025 fire burned a larger area within the reserve than those recorded in 2020 and 2017.
The tools and methods in this guide can be applied to wildfires in many other regions. By combining satellite imagery with environmental and historical datasets, it’s possible to move beyond detection and begin to quantify a fire’s impact. In doing so, you can also place individual incidents in context, revealing whether they are part of a recurring pattern or an unusually severe event.
Merel Zoet and Claire Press contributed to this report.
This guide contains modified Copernicus Sentinel data (2025), processed with Copernicus Browser, as well as data from the European Forest Fire Information System (EFFIS) of the European Commission Joint Research Centre.
