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 when it occurred and where it 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.

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.

These images were created with Copernicus Browser, a free browser-based tool from the European Space Agency for accessing and working with Sentinel imagery. It allows users to browse the Sentinel-2 catalogue by date and visualise different band combinations. You don’t need an account to use the browser, but signing in enables additional features.

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:

1. Zoom to the desired area on the map or use the search bar (San Vito Lo Capo, north-west Sicily)
2. Select the date of interest (‘2025-07-27’ selected below).
3. Select the layer of interest (‘True color’ by default; SWIR selected below).

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.)

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.

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:

1. Select ‘Custom’ in the layer selector.
2. Switch from the ‘Composite’ to the ‘Custom’ tab.
3. Check ‘Load script from URL’.
4. Paste this URL: https://bellingcat-scripts.ams3.cdn.digitaloceanspaces.com/NormalizedBurnRatio.js 
5. Load the script by clicking the green circular arrows to the right of the URL.
6. 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.

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).

1. Within the date selector, choose a date a few weeks or months after the fire.
2. Using the Area of Interest polygon, select the ‘Statistical Info chart’ icon.
   
   
   

3. Set the maximum cloud cover to around 30% using the slider in the top right.
4. Select a date range that captures the available data surrounding the fire (July 20-27 shown below).
5. Identify when the fire occurred on the graph (this will be marked by a sharp drop in the NBR, as shown below).
6. Hover over the points on the graph immediately before and after the fire to display the mean value.

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

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:

1. With the NBR visualisation selected, click the ‘Download’ icon. 
2. Switch tabs at the top from ‘Basic’ to ‘Analytical’.
3. Change the image format to ‘TIFF (32-bit float)’.
4. Change the image resolution to ‘HIGH’.
5. Change the coordinate system to ‘Popular Web Mercator (EPSG:3857)’.
6. Toggle the ‘Clip extra bands’ switch to the off position (see image below).
7. Select the ‘Custom’ layer check box (and deselect any others).
8. Click ‘Download’.
9. Wait. It could take several minutes for the image to be generated and downloaded.

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.

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:

1. Red
2. Green
3. Blue
4. Pixel validity (not used in this example)
5. NBR index

To create a new raster layer that applies our threshold on the NBR index band:

1. Double-click the fifth band (ending ‘@5’) to add it to the expression box at the bottom. 
2. Add < 0 using your keyboard (shown above).
3. Select the ‘Create on-the-fly raster instead of writing layer to disk’ checkbox.
4. 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).

Extra exercise: Download an NBR image captured before the fire. Use the Raster Calculator to create a new layer that shows burn severity.

Adding Conservation Area Data

Download the Zingaro Nature Reserve dataset from Protected Planet by selecting ‘Download > File Geodatabase’.

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’.

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.

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:

1. Set the ‘Raster layer’ to the threshold burn area layer (NBR@5 < 0)
2. Set the ‘Vector layer containing zones’ to the nature reserve polygon layer (should start with ‘WDPA_’).
3. Click ‘Run’
4. Click ‘Close’

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).

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:

1. (Optional) Add the OpenStreetMap layer from the XYZ Tiles category by double-clicking it.
2. Load the EFFIS data into QGIS. If prompted to select a coordinate transformation, click ‘OK’ to accept the default option. 
3. Load the Protected Planet Zingaro Nature Reserve polygon as described earlier. 
4. Open the Vector Intersection tool by selecting ‘Vector > Geoprocessing Tools > Intersection…’ from the menu bar at the top.

The Intersection tool creates a new layer containing only the fires that affected the Zingaro Nature Reserve. To create the new layer:

5. Set the ‘Input layer’ to the EFFIS fires layer.
6. Set the ‘Overlay layer’ to the Zingaro Nature Reserve polygon layer.
7. Click ‘Run’.

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:

8. Search for ‘Data Plotly’ in the available list.
9. Select the plugin from the search results.
10. Click ‘Install Plugin’ to download and install it. 

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:

1. For ‘Plot type’, choose ‘Bar Plot’.
2. Set the ‘Layer’ to the newly created ‘Intersection’ layer.
3. 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. 
4. 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:

5. Untick ‘Show Legend’. Only do this for simple plots where a legend is not required. 
6. Add a title and labels for the X and Y axes.
7. Finally, click ‘Create Plot’ and wait a few seconds for the chart to be generated.

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.

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.

To learn more about fire detection, see Bellingcat’s guide to NASA FIRMS.

To explore QGIS further, visit the Bellingcat toolkit entry on QGIS.

---

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.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Burning Forests: Tools for Tracking and Reporting Wildfire Damage appeared first on bellingcat.

At least 1,719 people are reported to have died after two devastating earthquakes struck northwestern Venezuela last week.

The final casualty count is expected to rise significantly.

Some media outlets report resident’s growing frustration with the Venezuelan government and its recovery efforts.

Sky News on June 29 reported that the United Nations Coordinator for Humanitarian Affairs in Venezuela was preparing for as many as 10,000 deaths. 

Social media posts, news reports and drone footage have been shared in recent days, proving vital sources for many Venezuelans (both in the country and living abroad) who are searching for information about loved ones who remain missing.

Social media pages have been set up listing many of those who are yet to be accounted for. Others have contacted Bellingcat asking if apartment blocks relatives were staying in are still standing. 

Bellingcat has received satellite imagery from Planet Labs PBC that shows one the worst affected areas in the country, including collapsed buildings and apartment blocks in La Guaira.

Readers can move laterally and vertically to observe the full image in the interactive below as well as zoom in on specific areas to assess the damage. A share button on the top right will copy a shareable link to the zoomed in area.

Scroll and zoom to see damage throughout the affected Venezuelan coast. Toggle between English and Spanish. Share a link to a specific location by clicking the button on the top right. The before imagery is from Jul 30, 2025 and Dec 12, 2023. After imagery is from Jun 27, 2026. SkySat imagery via Planet Labs PBC.

The high resolution image covers a 14-mile stretch of Venezuela’s northern coast from the towns of Catia La Mar to Caraballeda, which have been among the worst impacted. 

Other areas to be significantly impacted but not included in the imagery above include Caracas, Maracay, Valencia, Barquisimeto and Yaracuy.

We have compared the satellite imagery we obtained with previous images captured before the earthquake to identify which parts of this 14-mile stretch of coastline to show changes since the quakes. 

Readers can toggle between the imagery captured on June 27 (five days after the Jun. 24 quakes) and a composite of reference images taken on Jul. 30, 2025 and Dec. 11, 2023 (before the quakes).

Zooming in on specific areas reveals the scale of the damage.

For example, several buildings seem to have been flattened in the below before and after images showing the Playa Grande area.

Before imagery (left) of Playa Grande is from Feb 27, 2026. Imagery from after the earthquake (right) is from Jun 26, 2026. SkySat imagery via Planet Labs PBC.

The Planet Labs imagery also confirms significant destruction in the town of Carabelleda.

Before imagery (left) of Carabelleda is from Jun. 19, 2026. Imagery from after the earthquake (right) is from Jun 27, 2026. SkySat imagery via Planet Labs PBC.

Another area, Macuto, has been significantly impacted as well.

Before imagery (left) of Macuto is from Mar 20, 2026. Imagery from after the earthquake (right) is from Jun 27, 2026. SkySat imagery via Planet Labs PBC.

Footage taken on the ground and posted to social media also displays the devastation.

A minute-long video filmed on a 500-meter section of José María España Avenue in Carabelleda shows as many as a dozen collapsed buildings, most of them high-rises. This drone footage gives an aerial look of the destruction of at least six apartment blocks in the same area.

Another video shared on social media showed a collapsed hotel in Macuto, between Carabelleda and La Guaira.

Other open source information about the damage in cities such as Caracas, Valencia and beyond can be found on this site where individuals are uploading images and videos detailing damage.

While international rescuers continue to arrive in Venezuela, the threat of aftershocks remains.

Reuters also reports that engineers fear many buildings that remain standing could be vulnerable and are requesting an audit of state housing.

---

Carlos Gonzales, Jake Godin and Miguel Ramalho contributed to this report.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Satellite Imagery Shows Scale of Venezuela Earthquake Damage appeared first on bellingcat.

This article is the result of a collaboration with The Sunday Times. You can find their corresponding piece here.

Every Friday evening, the brochure says, players can compete to win cash prizes in one of the world’s fastest-growing racquet sports. The padel club in Dubai’s west is the picture of modern wellness culture: climate-controlled courts, a private sauna and ice bath, and one-on-one coaching. The promotional image shows a bearded man in mid-swing, eyes locked on the ball. He wears matching activewear and a golden tan. The poster boy for padel is a talented player who once finished runner-up at an international tournament. He has also spent the past decade living in the shadows.

Ian Thomas Dixon is a key figure in the Kinahan cartel, the Irish organised crime group that authorities say has evolved into a US$1.5 billion transnational network involved in drug trafficking, money laundering and arms smuggling. Investigators have connected the cartel to Iran’s intelligence services and the Lebanon-based militant group Hezbollah. Its feuds with rival gangs have been linked to at least 18 murders across four countries.

Dixon, 36, along with the Kinahan Organised Crime Group’s senior leadership – Christy Kinahan, 69, and his sons Daniel, 49, and Christopher Jr, 45 – was sanctioned by the US government in 2022. Authorities allege the Irishman acted as a trusted lieutenant to Daniel Kinahan, who is said to manage the cartel’s vast drug trafficking operation by helping move bulk cash across Europe, arranging payments and keeping tabs on money owed by a narco-trafficker.

Bellingcat and The Sunday Times can today reveal how Dixon’s racquet sport hobby has left behind a digital trail that led to the most recent footage of him since those sanctions were imposed – the first time he has been pictured publicly in almost a decade. This investigation also uncovers the alias Dixon has used in Dubai and exposes the first open source links to an underworld associate who was recently extradited from the Gulf state and jailed in Scotland.

It comes as cartel leader Daniel Kinahan awaits extradition to Ireland after his arrest in Dubai on foot of a warrant issued by Irish authorities. The arrest, in April, followed an extensive policing and diplomatic effort from international law enforcement.

In March, investigations by Bellingcat and The Sunday Times exposed the first photographs of Daniel Kinahan and his father in years and also revealed that the cartel’s “friend”, former UFC fighter Mounir Lazzez, was connected to US sanctions against Iran.

The latest findings give an unprecedented glimpse into the recent activity of a key cartel associate who, until now, has largely flown under the radar.

Family Ties

When cartel founder Christy Kinahan moved to Spain after his release from an Irish prison in 2001, it wasn’t long before his new home became a hub for the gang. His sons, Daniel and Christopher Jr, soon followed him to the Costa del Sol – as did their younger cousin, Dublin native Ian Dixon.

From the late 2000s onward, Dixon worked for businesses linked to the crime family in the south of Spain. One of these was The Auld Dubliner, a pub in Estepona that reportedly served as a base of operations for the cartel. In 2010, the pub was raided and temporarily closed by authorities as part of Operation Shovel, a years-long multi-national police investigation into the cartel’s drugs and arms-trafficking activities.

Dixon would also work as a trainer at MGM Marbella, the boxing gym co-founded by Daniel Kinahan that would go on to represent some of the biggest pro boxers in the world. The company, which was renamed MTK Global, shut down after the US sanctions on the Kinahans were imposed in April 2022.

In 2016, Dixon was arrested by Spanish police investigating the murder of Irish criminal Gary Hutch. The previous year, Hutch had been gunned down while out for a morning jog in a gated community on the Costa del Sol.

Dixon was released without charge, and another Kinahan cartel associate was later sentenced to 22 years for his role in the murder. The killing sparked a feud between the Kinahans and the rival Irish Hutch gang  that resulted in at least 18 deaths. 

Dixon and other key Kinahan members fled to Dubai in the wake of the deadly feud.

Ian Dixon has no known convictions. But his alleged role in the Kinahan Organised Crime Group was laid bare when the US sanctioned him. Authorities said Dixon managed finances and moved bulk currency for Daniel Kinahan and also kept tabs on the debt owed by a narco-trafficker. 

The sanctions notice also said Dixon controlled Hoopoe Sports LLC, a Dubai firm that listed a number of pro boxers among its clients and reportedly received more than $4 million for bouts involving former heavyweight champion Tyson Fury. Boxing promoter Bob Arum told Yahoo Sports the money was for consulting fees owed to Daniel Kinahan.

Dixon lived in an exclusive gated community in Dubai, according to the 2022 sanctions notice. Online listings show that properties like his Spanish-inspired villa are worth up to $2.7 million.

Passion for Padel

Padel is an increasingly popular racquet sport from Mexico best described as a combination of tennis and squash. According to the sport’s governing body, it has more than 17.5 million weekly players across 150 countries and the UAE, where Dixon lives, has the second-highest number of padel courts in Asia. It was on these courts in late 2024 that Dixon played in the master final of the Asia Pacific Padel Tour (APPT).

APPT rankings show Dixon registered for the tournament under the name “Ian Thomas”. Like his cartel leader relative Christy Kinahan, who used his first and middle names as an alias on his Google review profile, Dixon had dropped his surname.

Finding a Fugitive – How we Located Dixon

Bellingcat found the padel club promotion showing Ian Dixon after running images of the cartel associate through a publicly available facial recognition search engine. Among the results was a link to a graphic designer’s online portfolio, which included the advertisement for the padel competition. The original photo had been posted on the sports club’s Instagram page in late 2023, with the caption: “Elevating fun, one swing at a time!” Dixon was not named.

We searched for additional open source evidence and located online profiles for a 36-year-old Irish padel player named “Ian Thomas” who had taken part in a number of matches in Dubai in recent years. One profile shows he played 16 ranked matches between September 2024 and April 2026 – the most recent being the week after Daniel Kinahan’s arrest. But the accounts did not include profile pictures.

Bellingcat searched for footage showing the padel events and venues listed on the profiles. It returned multiple social media posts and live-streams clearly showing Ian Dixon at the same events where “Ian Thomas” was registered as playing. Dixon can also be heard speaking with a Dublin accent and at one point is seen with a close relative of Daniel Kinahan.

Dixon and his doubles partner played four games over the December 13-15 weekend, eventually placing second after losing in the final. The Irish cartel associate is captured on film after the match receiving a silver medal and commemorative racquet.

The Asia Pacific Padel Tour was held a month after senior Kinahan cartel figure Sean McGovern was arrested in Dubai on foot of an Interpol red notice. McGovern was extradited to Ireland last year and earlier this month jailed for 24 years for directing the activities of a criminal organisation in relation to murder and attempted murder. 

The tournament was live-streamed to YouTube via webcams set up on two courts. Dixon was captured throughout the three-day event, both playing on the court and mingling with others in the background. The hour-long male amateur final, which Dixon lost, is viewable in its entirety.

Dixon also posed for photos during the tournament, but it appears he did have some reticence about appearing on social media. In two images from a different padel event hosted at the same venue a few months later, Dixon’s face had been covered. However, a third photo was not edited, confirming that it was Ian Dixon.

Kingpin in the Crowd

Among the people Dixon was seen with at padel events in Dubai was Stephen Jamieson, a Scottish criminal who was recently jailed for his role in a multimillion-dollar drug trafficking operation.

Dixon greeted Jamieson with a fist pump during the Dubai APPT tournament in December 2024 on the day the Irishman played in the amateur final.

Dixon was also pictured with Jamieson at a family day padel event just weeks before the Scottish criminal’s arrest. (Bellingcat is not publishing details of that event to protect the identity of family members.)

Jamieson, who has multiple convictions, was extradited from Dubai last year and is serving a six-year prison sentence in Scotland on organised crime and drug charges. The case against him was built around intercepted messages he had sent via the defunct encrypted communication network EncroChat – a network the Kinahans have also used – to direct drug shipments.

The Sunday Times reports today on the Kinahan cartel’s deeply entrenched links to organised crime in the UK, where it is known to control much of the illicit drug market. It said the footage showing that Dixon and Jamieson know each other could indicate an underworld connection, since cartel cadres do not associate with rival operations.

Three of the seven alleged key Kinahan cartel figures have been arrested since the US sanctions were imposed. Johnny Morrissey, arrested in Spain in 2022, was later bailed and subject to a travel ban. Sean McGovern was jailed earlier this month and Daniel Kinahan awaits extradition to Ireland after his recent arrest in Dubai. Garda Commissioner Justin Kelly, of Ireland’s police force, recently said the investigation into the Kinahan cartel was ongoing and that authorities were continuing to focus on the other members of the gang.

Ian Dixon did not respond to questions from Bellingcat.

---

Connor Plunkett, Peter Barth, Beau Donelly and John Mooney contributed to this article. 

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Poster Boy: Sanctioned Kinahan Cartel Lieutenant Found Playing Padel in Dubai appeared first on bellingcat.

Between February 2022 and September 2025, Bellingcat staff and volunteers collected, geolocated, and shared more than 2,500 incidents of civilian harm following Russia’s full-scale invasion of Ukraine. 

As part of this effort, Bellingcat tested a new machine learning model intended to rank Telegram social media posts on their likelihood of containing incidents of civilian harm. 

This novel methodology dramatically reduced the search and selection time required, freeing researchers to focus on verifying incidents of civilian harm – not just searching for them. 

This piece documents our methodology, ethical considerations and lessons learned in the hope that others researching similar topics can benefit from our work. 

Open source research into civilian harm is still a relatively new field and it presents many challenges – one of the biggest is organising and sorting through the huge volume of user generated content being produced to find what is relevant. 

Machine learning, a form of artificial intelligence that uses algorithms to identify patterns from large amounts of data and make predictions, can make this task more efficient.

With ongoing conflicts involving large amounts of civilian harm occurring in Sudan, and much of the Middle East, this guide aims to offer those covering these conflicts an example of how machine learning can be used to help find and sort incidents. You can also access the Code Notebook for our model here.

We defined “civilian harm” not just as civilian deaths or injuries resulting from armed conflict, but also the broader and delayed effects on civilians from mental trauma, loss of livelihood, displacement, destruction of infrastructure and more. This definition was informed by the Protection of Civilians book on civilian harm. 

Initial Telegram Dataset 

Each Telegram post containing civilian harm which had already been manually verified by researchers was used to build an initial dataset of confirmed cases of civilian harm, which data scientists call positive instances. We collected a total of 5,848 unique URLs for these Telegram posts. For our manual collection we reviewed posts on relevant Telegram channels, working through oldest to newest posts each day. Assuming that a given post made it to our geolocated incidents list, it meant the researcher who flagged it also looked at the posts that appeared before and after it on Telegram and did not flag those ones, so we selected the 10 posts surrounding the verified civilian harm post as our additional dataset of posts that did not contain civilian harm. After excluding any deleted or duplicate posts, we ended up with 48,545 non-civilian harm posts, our negative instances. 

The choice to overrepresent negative instances aims at better reflecting the real world and increasing data available for model training. 

We enriched each URL with metadata from the Telegram API, such as the time of publication, reactions or textual content. As some of these posts had been deleted, we completed the missing data points with previously preserved versions from our Auto Archiver database, only available for the positive instances.

Feature Engineering

Training a machine learning model requires numerical data, as these models compute a prediction score based on mathematical operations.

We built these by converting raw data from our initial dataset, such as keywords signalling potential civilian harm, into numerical scores (or “features”) that the model could interpret, with the aim of increasing the model’s ability to identify patterns. This process, known as feature engineering, can significantly improve model results because it allows data scientists to suggest explicit context knowledge. 

A full list of features we used to train the model can be found in the code notebook accompanying this piece. Many features were directly inspired by researchers’ input from their experiences manually screening cases of civilian harm by sorting through a set number of Telegram channels and inspecting each post individually.

Several of the features used were directly built from the metadata contained in each Telegram post including media_type, day_of_week; or binary ones: forwarded, edited and reply_to. 

Other features included engagement information: views, forwards, total_reactions, and even individual features for most used emojis including the reaction_crying_face to count emoji.

Converting Text to Numbers 

To embed the experience from the manual collection process, researchers put together a list of keywords both in Ukrainian and Russian that, to them, signalled posts likely to  show civilian harm. For instance, “Шахед” and “КАБ” translated to “Shahed” and “Guided aerial bomb” respectively. We created a numerical feature to count their frequency. 

In addition, we included several generic English-language keywords which meaningfully signalled potential civilian harm, such as “injured”, “school affected” and “hospital affected” that were only used for generating semantic similarity scores. 

A semantic similarity score is a calculation used to determine the proximity in meaning between different words and phrases. To get the semantic similarity between the post text and each of our keywords, we represented each in a list of numbers via a Sentence Transformer model, which converts words into numerical representations called vectors that a computer can understand. 

We then calculated the level of similarity between each vector using cosine similarity, one of the most popular methods for measuring similarity between two pieces of text.

Due to how embeddings work, this calculation results in a figure on a scale from -1 (no semantic proximity) to 1 (same meaning). For example, the words “hurt” and “injured” would have a high similarity score, while “residential” and “injured” would have a negative score as the words are not semantically similar. 

Finally, to enable the model to identify the relevance of each post to civilian harm in Ukraine, we used a multilingual text transformer from the BERT family of language models to represent the entire post’s text as a vector of 768 numerical values. This model can efficiently represent text from many languages in a way that captures meaning: the same sentence in different languages will generate similar embeddings, and trained machine learning models can detect patterns in the embeddings. 

It is important to note that for this initial prototype of a civilian harm detection model, we did not include any features derived from media content such as photos and videos, although that would be a logical next step in attempting to improve model performance.

Selecting, Training and Evaluating Models

With 54,393 rows of 893 numerical features each, we selected four machine learning algorithms to train our predictive models. 

We chose Logistic Regression as a baseline algorithm due to its simplicity. We also selected three other “best in class” models, Random Forest, XGBoost, and LightGBM. These choices centred on the interpretability of the models and their ability to work on tabular data of this size. For example, we avoided neural networks due to a lack of interpretability and because those models work best with a larger dataset. 

To genuinely assess the performance of the trained models, we split our dataset into three parts:  

• A training set – the data the models were trained on (60 percent of the full dataset’s rows)
• A validation set – used for an intermediary evaluation when tuning model parameters (20 percent of all rows)
• A test set – hidden for the final performance assessment, so the models were evaluated on unseen data (remaining 20 percent of rows)

We used a stratified split to divide the dataset instead of a random split. This method ensured the proportion of positive instances (i.e. confirmed cases of civilian harm) remained consistent across all three sets at about 11 percent.

To measure the performance of machine learning models, we ran them through the test set and measured the number of correct and incorrect predictions. Models output a likelihood between 0 and 1 that each Telegram post contains civilian harm, and we tried to find a cut-off threshold that leads to a good balance between flagging almost every post (0.1) or flagging very few (0.9). 

There are two main types of evaluation metrics to gauge a model’s prediction power. Recall asserts what fraction of positive instances (i.e. known civilian harm posts) were correctly flagged as such. Precision measures the fraction of posts flagged as civilian harm that are indeed civilian harm posts.

During the training phase, we tuned the models to maximise average precision (PR-AUC), a metric that summarises precision across all recall levels. While this method also accounts for precision, it prioritises recall, which is preferable for this use case as it steers model selection to reduce the number of civilian harm posts that are skipped. 

The following table sorts models from best to worst PR-AUC against a baseline of a coin-flip predictor. ROC-AUC and F1 are two other evaluation metrics included as sanity checks. Simply put, ROC-AUC measures the probability of ranking two instances, one negative and one positive, correctly; F1 balances precision and recall equally and its best cut-off threshold value.

From these results, we selected XGBoost as our final model as it had the best scores when compared across all metrics.

Interpreting the Model

Because these models are interpretable, we can understand which features are the most useful when predicting whether a post includes civilian harm. The above table shows the top 10 features that most strongly signal the XGBoost model to make a decision:

• semantic_keywords_similarity: the semantic proximity between the post text and manually selected keywords “casualties”, “damage” and “civilian harm”
• bert:  the model was able to discern meaning from the text with the same strength as some of the other features in this list – there are three cases of this in the top 10
• reaction_crying_face: reactions with crying face emojis on the post
• group_of_messages: whether a post contains multiple media files
• keywords_in_text: the number of custom Ukrainian or Russian keywords in the post

These results generally tally with what you might expect when selecting Telegram posts for instances of civilian harm, including that posts that generate a lot of emotional engagement and posts using keywords about civilian harm were among those most likely to contain content related to this topic. Not all models had the same top features as XGBoost. In fact, for the Random Forest model the most important feature was the number of crying face emojis present in a post, a soft pattern highlighted by researchers when this methodology was first imagined.

LLM Results and Comparison

Retroactively, we decided to run a sample of the same test dataset through different large language models (LLMs) to gauge their ability to make these same predictions. 

We aimed to include an LLM-generated score as an extra feature for our trained models, which would be captured as relevant if it correlated with the correct predictions. 

To start, we selected two local models, the 1B and 4B variants of Gemma 3 from Google DeepMind, and two cloud-hosted models, Gemini 2.5 flash and Gemini 3.5 flash. With this selection, we hoped to compare results across a wide range of models’ expected performance. 

We generated a 400-row stratified sample (preserving the same proportion of real civilian harm instances) from the test dataset used for the custom models. For each of the four LLM models, we ran two tests: one where only the Telegram post message was sent, and another including both the message and the engineered features (excluding the text embeddings, as the model had direct access to the text). In the prompt for each model, we asked for a score between 0 and 1. We then evaluated the results as we did for the custom models. 

The above table shows that LLMs can indeed extract value from the engineered features. All four LLMs surpassed the baseline Logistic Regression model in our tests, yet none of them performed better than the other custom-trained models, and XGBoost remained the one with the highest PR-AUC. 

Still, Gemini 2.5 Flash performed better than its newer version 3.5 and even achieved a slightly higher best F1 score than any other model. While this is a good result, for the flagging of civilian harm posts, the PR-AUC remains the crucial metric, as it captures the model’s ability to identify infrequent instances of civilian harm while minimising false positives.

Ethical Considerations

Introducing an instrument of automated decision-making into a process of detecting civilian harm brings inherent ethical questions. These include automation bias, or how humans tend to blindly place faith in machine-generated recommendations; algorithmic bias, or how the results of these models echo the same patterns present in the training data, including under- or over-representation of types of civilian harm. 

The decision to test an automated methodology for this particular project came from the fact that there were limited resources for both steps in the process – the detection of potential civilian harm and its actual verification. Historically, we built an enormous backlog of unverified incidents because a lot of time had to be spent on monitoring the most recent events so that potential evidence would be captured and preserved as soon as possible. 

The automation of this process also reduced the exposure of researchers to a significant amount of unpleasant and distressing visual and text content, reducing the burden of exposure to traumatic content. 

For this project, we tried to ameliorate the ethical challenges with a number of strategies including randomly flagging posts not captured by any model, monitoring which features models relied on to make decisions, and by doing historical comparisons of patterns in data. 

Additionally, as stated above, for this initial prototype of a civilian harm detection model we did not include any features derived from the media content itself. In the future, it would be a logical next step in attempting to improve the model performance, to include the media from the posts – but using AI to review actual media comes with additional ethical challenges such as model bias.

Because of the opaque ownership of many LLM companies and their generative nature, the use of LLMs for an extra feature presented additional ethical challenges including privacy and safety concerns considering the sensitive nature of the data. Our model did not rely on LLMs, though we retroactively ran a sample through it. 

How the Model Fits into the Bigger Picture 

After selecting this model, we created a user interface where researchers could view a list of Telegram posts sorted from most to least likely to contain indications of civilian harm. The user interface was designed for quick triage and integration, where a positive confirmation from researchers would instantly send the post to the Auto Archiver (Bellingcat’s tool for preserving digital content) and then transfer it to ATLOS (our internal collaborative verification platform). Bellingcat staff and volunteers could then manually verify incidents. Researcher input was constantly stored so that this data could be used to improve the model in the future. 

Preliminary feedback indicated that the AI model was useful. Not only were we able to reduce time and harm from scouring through dozens of war reporting Telegram channels, researchers also reported that the stream of new posts being added to the verification backlog were capturing real and diverse cases of civilian harm. 

We recognise this model has much room for improvement and is a work in progress. Even though it can illicit diverse civilian harm posts, further tests and improvements (such as improved feature engineering and continuous evaluation) are needed before it can confidently be deployed.

Despite the focus on civilian harm and Telegram (highly popular in Ukraine and Russia), this pipeline is generic and can be adapted to other conflict monitoring tasks. How easily this can be done does depend on how open the social media platform is and whether it is possible to scrape posts from it. Apart from that, it is easy to incorporate new features and data, and cheap to automatically retrain, test and deploy models as the system receives more human input.  

Looking forward, sorting through overwhelming amounts of data in a conflict will continue to be challenging. Hopefully, this methodology can help newsrooms, conflict monitoring organisations, and others find the balance between ethical considerations and resources in order to carry out open source investigations on civilian harm and human rights violations. 

---

Editor’s note: This article was updated on July 3, 2026, to include a line outlining that the model described is a work in progress.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post How to Use AI to Help Find Civilian Harm appeared first on bellingcat.

Warning: Includes graphic descriptions of animal harm and images of animal parts from the outset.

A Bellingcat investigation has uncovered a Myanmar-based wildlife trafficker who has operated openly across social media for at least six years, claiming to have sold tiger bones, rhino horn, elephant skin and other products from protected and endangered species to customers in Myanmar, China and Thailand.

By analysing hundreds of adverts and customer conversations, Bellingcat traced more than US$21,000 in sales, identified cross-border shipments linked to multiple payment accounts, and geolocated the dealer’s home address. The seller frequently used graphic images to convince buyers that his wildlife products were genuine, sharing footage of animals before and after they were killed as proof of authenticity.

Following this investigation, Meta removed 10 Facebook accounts, WeChat suspended three accounts and revoked their payment functions, TikTok and YouTube each removed one account, and authorities in Myanmar and Thailand said they would examine the findings further.

On December 21 2022, a Facebook account shared a reel of a tiger cub lying unconscious beneath the caption: “Time for winemaking”, followed by several laughing face emojis. Four days later, the same account shared another reel, this time of an adult tiger lying motionless on an orange plastic sheet as a man approaches with a knife.

Two separate videos posted to Facebook by the account known as MB.

The account behind both videos belongs to Mei Ba (hereafter MB), a self-described Traditional Chinese Medicine (TCM) doctor based in Myanmar. In TCM, plants and animal products are used to prepare remedies based on established medicinal formulas. These remedies can take many forms, including herbal teas, simmered concoctions, ingredients steeped in wine, and pills. TCM is also sometimes associated with pseudoscientific beliefs, such as the idea that consuming an animal’s organ can nourish the corresponding organ in the human body.

A Bellingcat investigation has found that MB frequently advertises the trafficked parts of critically endangered and vulnerable species to customers in Myanmar, Thailand and China. An analysis of social media posts published by MB over six years found references to sales involving parts of bears, elephants, leopards, musk deer, otters, pangolins, rhinos, seahorses and tigers, all of which are protected species.

Profiting from Protected Species

For at least six years, MB has used ten different Facebook accounts under versions of his name to advertise animal parts and products for sale, including those derived from vulnerable and protected species. 

MB has advertised tiger body parts and products, including skins and wine made from bones. Myanmar’s wild tiger population was last estimated at a minimum of 22 animals (in a 2019 study published before the civil war). Licensed tiger farms, described as being similar to zoos, also operate in the country. However, Myanmar law prohibits the killing of any tiger, wild or captive. 

Bellingcat has also identified adverts offering leopard body parts, including skin, bones and gallbladders, as well as a product described as a “whip”, a euphemism for a penis. Many of the adverts explicitly stated that the body parts came from wild animals. The Indochinese leopard is classified as critically endangered, with fewer than 800 mature individuals believed to remain in the wild across mainland South-East Asia.

MB has advertised body parts and other products derived from Asian elephants, an endangered species, including skin sold in pieces and powdered form, as well as their genitals.

One of MB’s more graphic posts showed a recently killed and butchered moon bear – a type of Asian black bear which is classified as a vulnerable species. MB has also advertised various bear body parts for sale, including paws, heads, gallbladders, bile and fat.

Whole rhino horns, as well as bracelets and medicinal products made from rhino horn, have been advertised by MB. Although rhinos have been extinct in Myanmar since the 1980s, the country remains a known transit route for rhino products moving from India to China and elsewhere in South-East Asia, suggesting the items advertised by MB originated outside Myanmar.

Rhino products as advertised by MB on Facebook. Labels added by Bellingcat.

Under Myanmar law, anyone convicted of killing, possessing or trading a “completely protected species,” or its parts, faces a minimum prison sentence of three years and a fine under a conservation law introduced in 2018. Completely protected species advertised by MB include Asiatic black bears, elephants, leopards and rhinos.

Bellingcat contacted multiple Myanmar government authorities for comment regarding MB’s wildlife trade. The Myanmar embassy in London confirmed receipt of Bellingcat’s request and said it would consult the relevant authorities in Myanmar. 

Convincing Customers

Counterfeits are common in the illegal wildlife trade. Buffalo horn is often carved to resemble rhino horn, while cattle penises are passed off as tiger parts. Much of MB’s promotional strategy therefore focuses on persuading buyers that his products are genuine. 

To market rhino horn, MB has posted images of the items on scales or held up against a light, which he claims demonstrates the texture of genuine horn. For tiger bones, in one post he said that a patch of skin would be left attached to demonstrate their authenticity. 

MB has also posted videos of recent leopard and tiger kills. He has shared footage of live tigers in cages followed by images of the same animals being butchered for their skins, bones, skulls, claws and fangs.

Bellingcat only analysed open source evidence, including social posts, customer conversations, visible transactions and shipping receipts. Therefore, the authenticity and composition of the wildlife products advertised or sold could not be independently verified.

Nevertheless, given the volume and graphic nature of these posts, MB’s ability to operate on Facebook for at least six years raises questions about why his accounts remained active up until Bellingcat contacted Meta.

Evading Platform Moderation

For years, MB has openly advertised his business on Facebook, posting hundreds of adverts across ten profiles and various groups. He often uses coded language, including Chinese-language euphemisms in his comments. 

For example, he uses “eraser” (橡皮) to refer to “elephant skin” (象皮), a Mandarin homophone with characters that are also visually similar, shown below.

MB also uses pinyin, the phonetic system for spelling Mandarin Chinese words using Latin letters. For example, in one post, he abbreviates the pinyin word for “rhino” (xīniú) as “X”, advertising “X horn powder”, and uses “Y” (pinyin: yào) as shorthand for “medicine”. He also frequently uses animal emojis, including tiger, elephant, rhino, deer and bear, to refer to products derived from those animals without naming them directly.

Asked why MB had been able to operate for so long without being banned and how it detects common evasion tactics such as coded language, Meta responded: “Bad actors constantly evolve their tactics to avoid enforcement, which is why we partner with groups and invest in tools and technology to detect and remove violating content.”

Cross-Border Trade

To map the scale of MB’s business, Bellingcat analysed more than 500 screenshots of customer conversations spanning May 2021 to May 2026. Originally taking place on Facebook, Viber and WeChat, these exchanges were later reposted by MB on Facebook.

Often blurred or cropped, the material appears to have been shared as part of a strategy to present MB as a trusted seller who reliably delivers to customers. However, given the content frequently included shipping labels with names and addresses, product descriptions, and price discussions, Bellingcat was able to trace part of MB’s customer base and income. Notably, only content MB chose to repost was available for analysis, meaning the findings represent only a sample of his overall activity. 

Across the dataset, Bellingcat identified more than 150 transactions totalling US$21,000. The United Nations estimated Myanmar’s annual per capita income at between $300 and $430 in 2023.

Bellingcat analysis of MB’s digital footprint showing revenue earned per species by MB. Currency in US$. Trade values are estimated based on yearly average black market exchange rates.

Based on delivery records, Bellingcat identified 119 deliveries within Myanmar, 27 to mainland China, and nine to Thailand. Within Myanmar, shipments were most often sent to shared pickup points in cities or towns, whereas those to China were more frequently sent directly to individual addresses. 

Shipments within Myanmar most often involved small quantities of powders or medicines, such as 3 to 7 g of rhino horn powder or 5 g of bear gall bladder. By contrast, deliveries to China more often included whole animal parts such as rhino horn or tiger bones, or larger quantities of products, including 500 g to 1 kg of elephant skin powder or 10 to 40 bottles of medicine.

MB’s highest-value recorded transaction was to a customer in Yiyang, Hunan Province, China and involved two tiger femur bones weighing just over 2.5 kg. Sold in July 2022, the bones fetched 23,500 Chinese Yuan (US$3,494). The customer was asked to provide a screenshot as proof of payment. MB later reposted this on Facebook, alongside a photograph of the bones wrapped in cling film and a shipping label for Deppon Logistics attached. 

Bellingcat contacted Deppon Logistics for comment, sharing the image and tracking number shown below, but received no response at the time of publication.

Under Chinese law, buying, transporting, or selling protected or endangered species can, in the most serious cases, carry prison sentences of more than 10 years. Asian elephants and wild tigers are listed as “Class 1” protected animals. A permit system does exist for the use of captive-bred tigers, although it remains controversial. 

Under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix I, international trade in any endangered species, including Asian elephants and tigers, as well as their derivatives such as skin powder, scales and bones, is prohibited. China, Myanmar and Thailand are all parties to the treaty. 

Bellingcat contacted the General Administration of Customs of China for comment on MB’s wildlife sales to China but did not receive a response at the time of publication.

Bellingcat also found evidence of nine deliveries to Thailand, including a tiger penis and several TCM powders said to contain dog, yak and seahorse, all species regulated by Thai law and protected under CITES.

The Thai Natural Resources and Environmental Crime Division told Bellingcat that it already monitors packages falsely declared as traditional medicine but found to contain protected wildlife parts or ingredients derived from them. Following Bellingcat’s findings on MB’s activities, the division said it would investigate further.

A large proportion of MB’s exports to all three countries were bottles of TCM powders or tablets. For example, a “kidney replenishing medicine” was purported to contain deer, dog, gecko, praying mantis, seahorse and yak, while a “prostate medicine” was said to include deer, seahorse and dog. 

Under Chinese law, all packaged TCMs must carry labels clearly displaying the manufacturer’s name, full ingredient list, production and expiry dates, and information on side effects and safety. None of these details were present on MB’s labels.

Multiple banks accounts

Over six years of reposted conversations with customers, Bellingcat observed MB requesting payments to at least 15 different accounts, including seven via WeChat Pay, two via Alipay and six via third-party bank accounts, which MB described as belonging to friends or family.

For example, in May 2022, a customer purchased 1kg of elephant skin powder to be shipped to Chiuchow, Guangdong Province, China. MB told the customer his WeChat account could not currently receive the payment and instructed them to send the funds to his “sis” [female associate], shown below.

Both WeChat Pay and Alipay’s terms and conditions prohibit the use of their services to receive payments for illegal activities.

After being contacted by Bellingcat, WeChat suspended three accounts, revoking all payment functions and removing associated content. 

Alipay did not respond to Bellingcat’s request for comment.

Identifying MB

MB’s brazen online activities include operating at least ten Facebook profiles, two TikTok accounts, one WeChat account and one YouTube channel. He is the sole administrator of a Facebook group with nearly 1,000 members. Across these platforms, he has amassed some 12,000 followers. 

While using variations of “Mei Ba” (MB) across most of these accounts, he also uses the Chinese name “Mei Xiangfu” on his personal WeChat account. The name “May Kyin Phu” also appears alongside payment QR codes when customers are asked to transfer funds. Below is a QR code for Myanmar’s largest bank, KBZ, which includes what appears to be MB’s legal name: U May Kyin Phu, with “U” used as an honorific equivalent to Mr.

Since 2019, MB has used the same ID photo as his profile picture across multiple platforms, as well as his wedding photo as his TikTok profile picture and Facebook banner image. His wife, identified in the wedding photo and in other images posted by MB, has also been found advertising vulnerable and protected species via her Facebook account.

With more than 3,100 followers, she frequently reposts MB’s adverts while also sharing screenshots of conversations with customers. For example, in one WeChat exchange later reposted to Facebook, she discusses the cash sale of five “real” tiger penises.

From MB’s posts, Bellingcat geolocated a house in Lashio, eastern Myanmar, used to photograph animal parts for sale in his advertisements. MB has posted content from this property since 2020.

In the images below, what MB describes as “leopard gallbladders” can be seen hanging from a balcony, revealing the layout of the property’s backyard and the rooftops of adjacent buildings. Although Lashio is not covered by Street View, the distinctive roof visible in MB’s images matched with user-uploaded photographs on Google Maps.

Bellingcat contacted both MB and his wife for comment on the findings of this report. Both were reached via WeChat and appear to have received the request, but did not reply at the time of publication.

---

Data visuals by Galen Reich, Graphics by Merel Zoet, Editor, Claire Press.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Rhino Horn, Leopard Skin and Tiger Claws Sold Openly on Facebook appeared first on bellingcat.

This article was co-published with Signal Ohio and STAT.

In high school, Ashley Delgado dreamed of becoming a doctor and one day buying her father a Rolls-Royce. “She wanted to heal people,” said her father, James Taylor. She had a high GPA, Taylor added, and did especially well in science and Latin.

In her mid-20s, Ashley suffered a leg injury and was prescribed OxyContin. The painkiller marked the beginning of a yearslong descent through addiction — from prescription opioids to methamphetamine, then heroin, and finally, fentanyl.

With her family’s support, Ashley spent time in a rehabilitation facility in her hometown of Cleveland, Ohio, and in recovery she moved into a sober living home. But on an early summer morning in 2023, Ashley’s body was found on a dead-end street just outside the city. One sandal was missing. Tucked inside her bra was a folded scrap of paper containing a tan powder. She was 29.

“I have lost my father, my grandmother — that hurts,” Taylor said. “But when you lose your child, that’s the worst thing on the planet, because they’re not supposed to go before you.”

Toxicology tests would later show a mix of substances in Ashley’s system, including protonitazene and metonitazene, powerful synthetic opioids from a little-known class of drugs known as nitazenes. Her death was ruled accidental.

Before his daughter’s fatal overdose, Taylor had never heard of nitazenes. Developed in the 1950s as potential painkillers, the drugs never reached the market because they were deemed unsafe for medical use. He was shocked to learn they could be up to 40 times more potent than fentanyl and 500 times stronger than heroin.

Nitazenes are predominantly sold online, both on the clear web and dark web, and are often laced into other substances to increase their potency. Experts say this puts unsuspecting users seeking more common drugs, such as oxycodone, fentanyl, or stimulants like cocaine, at risk of fatal overdoses.

The US Drug Enforcement Administration (DEA) started tracking nitazene-related seizures around 2014, but it wasn’t until 2019 that it saw a marked increase. Since then, federal authorities have scheduled dozens of nitazenes as illegal substances, launched undercover operations, filed indictments, and imposed tariffs on China, where many of the laboratories manufacturing and supplying nitazenes and fentanyl are known to reside.  

Yet, figures provided to Bellingcat by the United Nations Office on Drugs and Crime (UNODC) show the United States has reported 26 different kinds of nitazenes since 2019 — the second highest number globally, after Canada.

And data from the Centers for Disease Control and Prevention (CDC) on nitazene-involved overdose deaths suggest that cases continue to rise. More than 1,100 fatalities have been confirmed through the CDC’s State Unintentional Drug Overdose Reporting System (SUDORS), but experts believe the number of Americans who have died from them since 2019 could be as high as 2,000. 

Alex Krotulski, the director of the Centre for Forensic Science Research and Education in Pennsylvania, told Bellingcat that deaths are underreported because nitazenes were not routinely tested for.

“There are only limited forensic toxicology labs that test for nitazenes, so if a nitazene was present and the lab didn’t test for it, the number wouldn’t appear in SUDORS,” he said. “Also, for labs that do test for nitazenes, they have missed cases prior to their testing.” The most recent years for which there is CDC data, 2023 and 2024, show they were the deadliest, with 747 confirmed deaths.

In this months-long open source investigation, Bellingcat combed through dozens of criminal court proceedings, filed national, state, and county-level Freedom of Information requests, and obtained scores of medical examiner reports to produce the most detailed account yet of how nitazenes are infiltrating US borders and destroying lives. 

The investigation found that, despite efforts to curb their spread across the country, nitazenes are proliferating online. It also shows that, by the time nitazenes reach American users, they are almost always mixed with several other drugs, including methamphetamines, cocaine and, most notably, fentanyl. 

As of this year, 48 of 50 US states have reported nitazene seizures.

Less Fentanyl, More Nitazenes

Fentanyl is by far the biggest opioid killer in the US. With more than a quarter of a million deaths since 2021 and about 200 fatalities a day, fentanyl is one of the country’s most urgent public health crises. But drug experts warn that nitazenes can be even more potent and are being mixed with fentanyl and other substances, creating increasingly lethal combinations.

“We’re always concerned about fentanyl being mixed in with other drugs — cocaine, meth, heroin,” said Frank Tarentino, Associate Chief of Operations for the DEA’s Northeast Region. “You add nitazenes to that and it makes it exponentially more dangerous and frightening for drug law enforcement, parents, caregivers, educators, and the young.”

Data obtained from the DEA’s National Forensic Laboratory Information System (NFLIS) show reports of confirmed seizures of nitazenes rising sharply — from 43 positive tests in 2019 to almost 2,000 in 2024 (the most recent year for which figures are available). By March this year, more than 8,000 nitazene reports had been recorded since 2019. But experts said that not all laboratories can test for nitazenes — which come in many forms including powders, pills, and sprays — and many don’t feed into the NFLIS system, meaning these numbers are almost certainly an underestimate.

We asked the DEA for a breakdown of reports of nitazenes by state. Ashley Delgado’s home state of Ohio stands out. NFLIS data from 2019 to 2024 indicate that more than a third of all positive nitazene laboratory reports nationally are linked to Ohio. 

Separate data from the CDC shows Ohio has also recorded the highest number of nitazene-related overdose deaths in the US since 2021. In 2020, there were just four fatalities linked to the drug; in 2021 that number rose to 90. Between 2022 and 2024, according to government data, there were 200 more deaths.

“It is a risk to our community,” said AmandaLynn Reese, chief programme officer at Harm Reduction Ohio, a non-profit that supports people who use drugs. “There’s been several instances of nitazenes being reported within the community, and I think we’re going to see more of that, especially as we’re seeing less fentanyl.”

To learn more about what was happening in Ohio, Bellingcat filed a public records request for county-level figures to the state’s Bureau of Criminal Investigation (BCI). The data shows that the counties of Scioto, Butler and Cuyahoga — areas long affected by the opioid crisis — account for almost half of all nitazene detections across the state, by weight.

In the 2000s, Portsmouth in Scioto County became known as the “pill mill capital” of America due to widespread overprescribing of opioids. More recent data continue to show Scioto with one of the highest rates of drug overdose deaths in the state. In Cuyahoga County, which includes Cleveland, drug-related mortality rates tripled the national average in 2022. 

Two years ago, Ohio’s Governor Mike DeWine issued executive orders to schedule nine different nitazenes and legalised the use of tools to test for drugs including nitazenes. 

The reasons why Ohio has been so hard hit are still not fully understood. “Ohio’s geography has long been a suspected contributing factor,” said Erin Reed, director of RecoveryOhio, a statewide initiative coordinating Ohio’s response to addiction. The organisation cited a 2001 article pointing to Ohio’s unique geographic and infrastructural features — including vast land, air and sea transportation networks — as key reasons for the state being a hub for drug trafficking. 

Local organisations like Harm Reduction Ohio are pushing for more drug-checking services, education, and greater accessibility to testing strips and life-saving medications like Naloxone, a drug that is used to reverse an opioid overdose. “People are going to use drugs,” Reese said. “We don’t know the supply, but those are ways you can engage in your drug use to increase safety and reduce harm.”

Dealer’s Choice

Bellingcat obtained medical examiner reports from Cuyahoga County for all nitazene-related deaths in 2023 and 2024, which provide an insight into how the drugs are being consumed. The autopsy records show that 45 people — 31 men and 14 women aged 29 to 72 — died after taking nitazenes over the two-year period. Among them were university graduates and former athletes, an Army veteran, an ironworker and an addiction counselor.  

Just before Christmas in 2024, a young man from Cleveland died after taking drugs that included etonitazene. A couple of weeks earlier, the body of an elderly woman was found in her home after she ingested drugs that included metonitazene and protonitazene. In the summer, a mother of two children in her thirties consumed a similar lethal mix. All except one of the 45 deaths was ruled accidental. 

And in every instance, nitazenes were detected alongside fentanyl, and often with a cocktail of other drugs such as heroin, cocaine, methamphetamine and benzodiazepines. The reason for this wide variety, Tarentino, the DEA agent said, is that dealers often mix nitazenes into other drugs to make them more powerful and addictive, and ultimately to give them a competitive edge. 

“It becomes a brand,” he said. “The unfortunate circumstance that we find ourselves in is that the dealer’s choice becomes a deadly decision.” Not only are these mixtures deadly — they can also be highly profitable. 

Court records analysed by Bellingcat show nitazenes have been sold at prices ranging from roughly US $4,000 to $12,000 per kilogram. But Tarentino said the DEA’s internal estimate puts $12,000 at the lower end of the range, with prices going up to as much as $40,000. Given their potency, even small quantities can be diluted into hundreds of thousands — or potentially millions — of doses once mixed and pressed into pills. “A little bit can go a long way,” Tarentino said, “and they can make a lot of money.”

A Freedom of Information Act request to the US Customs and Border Protection (CBP) revealed that in 2024 and 2025 — the only years for which the agency has monitored nitazenes separately — 41 consignments of the drug were intercepted. The data shows that most of these shipments arrived by mail, primarily from mainland China, Hong Kong and the United Kingdom. The quantities tended to be small, ranging from less than 1 gram to almost 700 grams. 

But that’s not always the case. An analysis of US federal court records linked to prosecutions of nitazenes indicates that roughly 90 kilograms of material containing nitazenes in different forms (powder and pills) have been seized over the past three years. Nearly two-thirds of that amount, about 60 kilograms, stem from a single case.

In that case, prosecutors allege that a man named Valkar Singh drove a blue Maserati from Canada into the US carrying six industrial-sized buckets with more than 100,000 pills containing  isotonitazene. According to court filings, Singh transported the drugs to a Bronx, New York address, where he was arrested by undercover law enforcement officers. 

Tarentino, who is familiar with the Singh case but could not comment on it specifically, said a lot of work is being done to prevent drugs being smuggled across the Canadian border. “Canada has become a major concern, but also a major partner in trying to stop the synthetic opioids that are coming into the United States,” he said.

Lawyers for Singh, who has pleaded guilty and is awaiting sentencing, declined to comment.

The scale of the alleged seizure makes this case an outlier. Of 46 federal cases identified by Bellingcat between 2021 and 2025, the next highest nitazenes seizure was about 9 kilograms. By comparison, data provided by the European Union Drugs Agency shows roughly 18 kilograms of nitazene-related seizures (pills, powder, liquid) across the EU between 2019 and 2023. 

“It’s very large,” said Jared Brown, scientific affairs officer at the UNODC. “One hundred thousand pills is probably at the limit of what we hear about in terms of maximum types of quantities that get seized.” 

The evidence suggests that most buyers are individual dealers who purchase relatively small quantities online, rather than organised criminal groups. “It’s street-level or mid-level dealers [in the US] that are introducing the nitazenes into the drug supply, not the big drug traffickers,” said Philip Berry, a visiting senior lecturer at King’s College London who formerly worked in counter-narcotics at the UK Home Office.

Court documents show that buyers can easily find nitazene suppliers online: on dark web marketplaces, standalone chemical supplier websites, or even on social media platforms. The suppliers often market the drugs by listing their chemical identifier and social media contact details. Often, the ads include an image of a young Asian woman striking a pose. 

Buyers are often individual dealers who contact sales representatives via encrypted channels and negotiate a deal. In those conversations, representatives will sometimes disclose how they claim to evade customs, for example by declaring the product as cosmetics or electronic accessories.

A detailed account that illustrates this modus operandi comes from the 2023 case against a man named Will Catis in Florida — the state with the second highest number of confirmed nitazene reports. Court documents show that a basic internet search led Catis to multiple nitazene advertisements listed by Jiangsu Bangdeya New Material Technology Co., LTD, a Chinese company sanctioned by the US Treasury for offering illicit substances for sale, including fentanyl and protonitazene.

Catis purchased approximately four kilograms of nitazenes from Jiangsu Bangdeya in batches no larger than 500 grams. The drugs were sent via the US Postal Service to Deerfield Beach, Florida. Once received, Catis mixed the nitazenes with other drugs, pressed the substance into a brick and sold it to other drug traffickers. Catis was jailed for 12 years after pleading guilty to possessing and intending to distribute nitazenes.  

One court case from Florida describes how a couple who lived in a converted garage bedroom in Hernando County bought nitazenes through the mail from Chinese companies they contacted online. Jacob Spinoza and his girlfriend Veronica Jo Barback regularly abused the drugs and distributed them locally, according to court documents. Both pleaded guilty. Spinoza was sentenced to nine years in prison, and Barback received a three-year sentence.

Another notable case reveals how a man allegedly ran a drug trafficking operation from a prison in Ohio. Investigators said Brian Lumbus Jr worked with a middleman, Giancarlo Miserotti, who contacted drug manufacturers in China to get nitazenes shipped through Italy to avoid custom checks. Once in Ohio, the plan was to distribute the drugs to other states, court documents said. 

But law enforcement agents were listening in on conversations between Lumbus and other members of the drug network, who expressed caution about the potency of nitazenes. “Man, we got to be careful … somebody died,” Lumbus said in one phone conversation, according to court documents. “Ohhh … it was too strong,” Miserotti responded. “I think the ratio of the pink [metonitazene] was thick.” 

Lumbus is awaiting trial. Miserotti was arrested in Italy in 2023 and sentenced to more than 13 years in prison. 

Arms Race

Enforcement actions have targeted the online marketplace ecosystem. In June 2025, Archetyp Market, a major dark web platform used to sell drugs, was dismantled in a coordinated operation involving Europol. US authorities have also indicted several China-based companies and individuals accused of offering nitazenes and related synthetic opioids for sale. Still, advertisements for nitazenes continue to litter online markets, constantly adapting to new regulatory regimes.

In July 2025, China placed the majority of nitazenes under national control. Tightened regulations — both in China and the US — have tried to stem the flow of nitazenes. But drug experts warn that manufacturers are already exploiting loopholes in China’s regulations by marketing chemically similar synthetic opioids known as “orphines.” 

Jared Brown, of UNODC, said orphines are also thought to come from China and are about as powerful as fentanyl. “Orphines have just enough of the molecule difference that it isn’t covered by the core definition that China has made,” he said. 

This is not the first time Chinese synthetic opioid manufacturers have adapted to regulations. In 2019, China banned all fentanyl-related substances, including some major precursors. The number of distinct fentanyl analogues reported to the UNODC subsequently plummeted, while reports of nitazenes quickly picked up. Now that China is clamping down on nitazenes, orphines are on the rise. More than 150 cases involving orphines were reported in the US between 2024 and 2025, the majority of which are in Illinois.

“Always adapting, always changing – we call them ‘shape shifters’,” said Tarentino. “They’re this global Hydra that are always changing, evolving and adapting to their environment and taking full advantage of all of these different loopholes and vulnerabilities that exist.”

---

Reporting for this story was supported by the Fund for Investigative Journalism.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Super-Potent Synthetic Opioids Spread Across US Amid Fentanyl Crackdown appeared first on bellingcat.

On February 15, 2026, the bulk carrier, Grumant (IMO: 9385879) was pictured at the occupied Ukrainian Port of Feodosia on the Crimean peninsula. Satellite imagery suggests it had already been there for several days.  It appeared to stock up on grain before departing on a two-month-long journey eventually docking at the Port of Benghazi in Libya on April 18.

While there have been previous reports of grain shipments from occupied Ukraine arriving in Libya, this is only the second time a Russian ship has been observed delivering what the Ukrainian government describes as “stolen” grain to the country. The previous case involved the Damas Wave which travelled in January of last year to the port of Misrata which is under the control of the UN-recognised Government of National Unity (GNU). In addition to satellite imagery, Bellingcat deployed a new technique that analysed Grumant’s heading data which was contained in AIS information provided by Lloyd’s List Intelligence, to help confirm Grumant’s presence in Feodosia. 

Bellingcat has been tracking smuggled Ukrainian grain shipments as they find new markets, five of the ships we previously identified have since been sanctioned by the EU while another was sanctioned by the US Department of Treasury.

Lloyd’s List Intelligence has previously reported on the expansion of Russia’s grain smuggling operations, beyond the occupied port of Sevastopol to include Feodosia port. 

According to the Ukrainian activism, journalism and hacker group, Kiborg News, Grumant used deceptive shipping practices to deliver grain to Latakia, Syria in 2024. The report included several of Grumant’s shipping manifests, which showed it had repeatedly exported grain from Occupied Crimea to Syria. 

Heading Data Helps Locate Grumant

It is standard maritime practice that ships broadcast Automatic Identification System (AIS) messages which include a ship’s position, heading, and draught (among other information).

Because of longstanding Global Navigation Satellite System (GNSS) interference in parts of the Black Sea, the position data transmitted by an affected ship’s AIS system is often unreliable.

Between February 7 and February 19, 2026, data from Lloyd’s List Intelligence shows the Grumant transmitted 29 AIS messages, with unreliable positions in the vicinity of Feodosia. We know these positions are unreliable as they are erratic and some of them report the ship as being positioned on land.

However, according to the IMO, the heading data transmitted by a ship’s AIS system must come from an onboard compass. A compass is unaffected by GNSS interference, meaning it is a more reliable source of information in these conditions.

Over the same dates, all 29 AIS messages reported the ship’s heading as 267 degrees or 268 degrees. The Port of Feodosia has a heading of 267.5 degrees. The close agreement between the ship’s heading and port heading strongly suggests that Grumant was moored at the port between February 7 and February 19, 2026.

We conducted an extra check of the heading data by reviewing satellite imagery available of berth 1 at Feodosia Port, which suggests that the same vessel was present on several days between February 6 and February 18. Imagery on Feb. 6 shows the port was empty in the morning and occupied in the afternoon. Grumant exited the area of GNSS interference on February 21, and berth 1 at the port was captured on satellite image on February 22 and appeared empty. The low resolution satellite imagery is only used as an additional check to see if a vessel is at the berth.

Bellingcat checked all vessels transmitting AIS in the vicinity of Feodosia Port and found that Grumant was the only one that consistently transmitted a heading matching the Port of Feodosia over the period of interest.

We shared our research with Charlie Brown, a former US Naval Officer and Senior Advisor at United Against Nuclear Iran where he focuses on maritime sanctions enforcement and the tracking of illicit shipping. Brown told Bellingcat that while satellite imagery of vessels remained key for identification, when looking for reliable data in a spoofing environment it made sense to look at the various elements of AIS data to try and find some accurate information, despite GNSS spoofing.

“It’s quite standard for the independent gyro compass to be providing the heading […] I think the majority would not [be subject to spoofing] so it’s a good methodology to parse out the particular data and then make some inferences from that.”

“It’s neat to think of what can be derived from data that would otherwise be dirty or wrong. So there’s still some elements of use in there.”

He added that in theory there are probably some compasses that are subject to spoofing as well. 

He told Bellingcat that it was fair to say the heading data of the Grumant supported identification, but stressed the need to cross-reference with other data sources. 

While in this instance it has been possible to use AIS data to help verify the location of Grumant, it is relatively unusual to have access to this information. 

Ships that call to the occupied territories frequently disable their AIS transponders to do so.

This activity, known as “dark port calls”, is a common tactic for those engaging in illicit or sanctioned trades. 

Grumant does not transmit AIS messages from February 8 to 11, but this is the longest gap in data (see diagram above), with intermittent messages coming through after that point.

It is unclear why Grumant continued to transmit AIS during the period it was loading in Feodosia. 

A review of Lloyd’s List Intelligence data from January 2025 shows that on a previous voyage to the Black Sea the Grumant operated “dark” for 59 days.  

Visual Identification

On February 15, 2026, high resolution imagery showed Grumant docked in the Port of Feodosia. We compared it with other recent images of Grumant to confirm the match. 

The ship in the satellite image has a grey-coloured deck, which is uncommon enough for it to stand out. Many bulk carriers have cranes (including the ships we previously covered such as Krasnodar, Zafar and Zaid), Grumant does not have any. It also has seven hatches (openings for the grain) and a bright yellow front mast that matches the mast of Grumant (see the image of it transiting the Bosphorus). We can match the Grumant in the Feodosia image, not only to pictures of the Grumant shot from the ground, but also to the satellite image from Benghazi.

The length and breadth of the ship also matches that of the Grumant; 180 metres by 22.90 metres. 

Libya’s Relationship with Russia and Ukraine 

Libya has complicated internal dynamics with essentially two administrations in charge of different parts of the country – the Government of National Unity (GNU) in the west and the Libyan National Army (LNA) in the east.

In recent years, Russia has backed the LNA’s General Khalifa Haftar, based out of Benghazi, in the east of the country. But Jalel Harchaoui, a political scientist specialising in Libya with the Royal United Services Institute (RUSI), stressed that the two sides of this conflict, the LNA and the UN-recognised GNU, are not currently fighting. Instead they are in a flawed, multi-year truce.

Therefore, the east-west divide isn’t as clear-cut as during the civil war. While all shipments going to Benghazi and Tobruk are overseen by the LNA, not all shipments going to the city of Misrata (which is run by the GNU) are meant for the GNU-dominated part of the country. 

Harchaoui told Bellingcat: “the Tripoli government is in some regards pro-Ukraine, but if there’s business that can be done with Russia through the very opaque port of Misrata and all the right people get paid, the business is going to take place.”

That observation is potentially significant given at least one previously tracked vessel that went from occupied Ukraine to Libya docked in Misrata.

This was not the case of the Grumant, however, which arrived in an LNA-controlled part of the country. It is not known from open sources alone if the authorities in Libya or at the port in Benghazi knew the grain carried by Grumant had come from occupied Ukraine.

Bellingcat contacted the Benghazi-based LNA government and representatives of the Tripoli-based GNU government via the Libyan Embassy in The Netherlands. We also contacted the Port of Benghazi, Port of Imzir in Turkey as well as the Ukrainian and Russian authorities. Representatives of the LNA did not respond to requests for comment before publication, nor did the Port of Benghazi or Port of Izmir. The Libyan Embassy in The Netherlands replied to Bellingcat after publication, stating that Benghazi and eastern Libya are not under the authority or administrative control of the Government of National Unity and therefore they are not currently in a position to comment on Bellingcat’s findings.

Ukraine Continues to Pursue the “Shadow Grain Fleet”

“The port of Feodosia, located in the temporarily occupied Autonomous Republic of Crimea, is not under Ukrainian control, and any commercial activity conducted there is illegal,” the Ministry for Development of Communities and Territories of Ukraine and the Ministry of Foreign Affairs of Ukraine told Bellingcat in a joint response. 

They told us the loading of grain exported from the temporarily occupied territories is an illegal act and Russia was using ports as logistics centers to export stolen Ukrainian agricultural products.

“The expansion of such routes to third countries, in particular to North Africa, demonstrates Russia’s ongoing efforts to circumvent international sanctions and monetize resources stolen from the occupied Ukrainian territories.” 

The Ukrainian Ministry of Foreign Affairs sent information about Grumant’s (IMO: 9385879) “illegal activities” to the diplomatic missions in Great Britain, the Republic of Turkey and the Republic of Tunisia over the course of March to May this year, the ministries told Bellingcat. 

Ukraine is continuing to pursue legal action against Russia’s “shadow grain fleet” they told us. For instance, earlier this month a Swedish court approved the transfer of the Russian “shadow grain fleet” vessel CAFFA to Ukraine for investigation after it was arrested in Swedish waters. 

This case has set a new precedent, going beyond sanction and fines previously handed out to such vessels, and allowing for the detention and confiscation of a shadow fleet vessel in European jurisdictions, the ministries said.

According to Russian court documents Grumant’s previous owner Murmansk Shipping Company was dissolved and “Decision/Reshenie” LLC were listed as the International Safety Manager and operator of Grumant. Decision/Reshenie were also listed as the operator of Grumant in another court document, from an unrelated case. 

Bellingcat attempted to contact Decision/Reshenie to ask about Grumant’s grain shipment from Feodisia Port to Benghazi Port, but they had not responded at time of publication.

---

Youri van der Weide, Galen Reich, Yörük Işık and Bridget Diakun contributed to this report.

Cover image: Planet Lab image shows Grumant anchored off Izmir, Turkey on February 27. Credit: Planet Labs PBC.

Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Heading Off: New Technique Helps Track Grain Smuggling Expansion to Libya appeared first on bellingcat.