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Satellite imagery obtained by Bellingcat lays bare the extent of tree-felling at a Washington DC golf course that President Trump is reportedly seeking to transform into a championship-level course, despite moves to protect the site by conservationists. 

The extent and circumstances surrounding the rapid changes prompted a judge to call an emergency hearing earlier this week, banning any further changes until later this month.

Reports on Wednesday detailed that 430 trees were cut down at the East Potomac Golf Links  which is owned by the Federal government and managed by the National Park Service (NPS). This is despite a judge reportedly stating earlier this year that no more than 10 trees at the course could be cut down without first notifying the court. 

NPR reported that officials notified the court just before 5pm on Friday Oct. 9 of plans to remove up to 529 trees in phases starting  the next day on Saturday Oct. 10. That prompted US District Court Judge Ana Reyes to call an emergency hearing on Oct. 12. By that time, 430 trees had reportedly been cut down.

Reyes is reported to have said she was “furious” with the Justice Department for the lack of time afforded the court and plaintiffs before the tree removal was carried out.

She was also reported to have said that the government’s actions had cost it “all of its credibility with me in this case.”

Images taken at the course earlier this week show trunks and branches strewn out across fairways.

Just total devastation wrought on East Potomac Park this past weekend. 450+ trees. For an admin that professes to support “beautification” of DC, that’s sure not what we or anybody else who has seen what they did on Saturday would describe it as. East Potomac is (was?) a thriving, beautiful gem in the heart of Washington DC. A beautiful, public, accessible place that hundreds of thousands use annually. The assault on this place should infuriate every American. The impacts of what took place Saturday will take years to fix. We cannot sit idly by while this admin treats our public lands like they are for the taking.

— Save East Po (@SaveEastPo) October 13, 2026

But satellite imagery affords a broader look at exactly what sections of the course have been impacted.

The Image below from Planet Labs taken on October 8 shows the golf course before the tree-felling was carried out.

Planet Labs imagery captured on Oct. 13 shows large barren sections towards the south and west of the course, where trees were removed.

A before and after slider allows for easy comparison of the Oct. 8 and Oct.13 images.

Reuters reported that an organisation called The ​DC Preservation League said in a February ​2026 lawsuit that President Donald Trump was flouting ⁠environmental laws to “irreparably destroy a priceless piece of American history” so he could “convert a public recreation space into an exclusive, high-end golf course.”

Kevin Griess, an NPS official, reportedly stated at Monday’s hearing that tree removal was part of a 2019 plan that predates Trump’s aims for the course and sought to restore links-style vegetation.

Bellingcat contacted NPS as well as the Department of Justice but did not receive a response before publication.

The changes to the golf course are not the only ones President Trump has sought in Washington DC and the surrounding area since the beginning of his second term. In October last year  demolition began on the East Wing of the White House with the aim of building a new ballroom claiming it was “the safest building ever built.”  

Trump has also proposed a 250-foot-tall “Triumphal Arch” to be built just outside the Arlington National Cemetery in Virginia.

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Correction: This article was updated on October 15 to reflect that a judge told the US government not to cut down more than 10 trees before providing notice. The article previously said that no more than 10 trees could be felled without the court’s approval. The headline has also been changed to reflect this.

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 Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Satellite Images Show Extent of Trump Admin’s Tree-Felling at DC Golf Course appeared first on bellingcat.

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Satellite images obtained and analysed by Bellingcat show over five miles of new road constructed in Big Bend National Park in southwestern Texas, part of a US Customs and Border Protection (CBP) “Smart Wall” installation, which includes roads, barriers, lighting and cameras. 

The project was paused earlier this week after pushback from local residents, politicians and environmental groups.

The roads have been built adjacent to the Rio Grande, through the river’s floodplain and riparian forest, and include construction adjacent to Cottonwood Campground, Santa Elena Canyon, and Mariscal Canyon in the National Park.

The map above traces the new road construction in yellow. An unannotated version of the satellite image showing the new road can be found here while a before image of the same area where no road is visible can be seen here.

The new road extends to Cottonwood Campground, one of four campgrounds in Big Bend National Park. 

The road can be seen crossing the riparian forest and smaller drainages before joining the campground access road. Imagery also appears to show bright yellow construction equipment staging in the campground area, where bulldozers were previously seen.

In the southernmost part of Big Bend National Park, additional new road construction is also visible near Mariscal Canyon and connecting to Talley Road. 

CBP planning documents indicate that this section of the border will receive a four-to-six foot tall vehicle barrier in addition to the “smart wall” lighting and camera systems. Talley Road currently provides access to riverside backcountry campsites and hiking and boating access to Mariscal Canyon on the Rio Grande.

The map above traces the new road construction in yellow. An unannotated version of the satellite image showing the new road can be found here. A before image of the same area where no road is visible can be seen here.

CBP Commissioner Rodney Scott described the construction activities as “survey and design work” in an August 13 statement. He also stated that CBP is “building one new access road, improving existing roads, installing detection technology, and placing vehicle barriers in limited, strategic locations.”

However, by August 17 Scott had announced a “pause” of construction activities in the face of  bipartisan criticism of the project’s impact to the environment, recreation access, and the local economy

The Big Bend Border Patrol Sector sees the fewest migrant apprehensions of any part of the US southern border, according to CBP’s own data.

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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. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.

The post Satellite Imagery Documents New Border Patrol Road Construction in Big Bend National Park 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.

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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.

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.

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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.

A Bellingcat investigation has identified nine Facebook groups with a combined membership of more than 70,000 people, in which coded language has helped illegal wildlife dealers evade bans on the platform for years. Facebook says it prohibits any form of animal trading on its platform.

Investigating the operators behind all nine groups, Bellingcat identified six Facebook profiles that led back to a single broker in Jakarta, Indonesia. This investigation was carried out in partnership with Mongabay. You can read their report in English here and in Bahasa Indonesia here.

In an open Facebook group, brazenly titled “West Bogor Animal Selling and Trading Forum,” one member posts an advert for a vulnerable rhinoceros hornbill.

Commenting on the advert, another member warns: “Just be careful not to get caught.” 

“That’s the risk,” replies the seller. 

Under Indonesian law, the capture, trade, or possession of a rhinoceros hornbill is punishable by up to five years’ imprisonment or a fine of up to Rp100 million (US$6,000). (According to Statistics Indonesia, the average monthly wage in August 2025 was just over Rp3 million or US$180.)

Meta also states that the buying and selling of animals on its platforms is prohibited. However, in this group, along with eight others identified by Bellingcat, animals have been traded in plain sight for years, including wild and protected species. Three of the nine groups have been live on Facebook for at least five years. Four have been active for 12 months or more, and the remaining two were created in 2025.

All nine groups state in their “About” tab that they are based in or around Jakarta, the Indonesian capital. As one of the most biodiverse countries in the world, Indonesia is a hotspot for poachers and a key transit hub in the illegal wildlife trade.

A quick scan of these groups revealed a variety of protected species for sale, including Javan coucals, Javan scops owls, Javan langurs, binturongs, and both wreathed and rhinoceros hornbills.

In one of the most active groups, West Bogor Animal Selling and Trading Forum, more than 200 adverts were posted in a single week. Of these, 18 advertised vulnerable species, including these two infant silvery gibbons. 

With fewer than 2,500 mature individuals left in the wild, the silvery gibbon is considered endangered. Under Indonesian law, trading in this species can result in up to five years’ imprisonment or a fine of up to Rp 100 million (US$6,000).

Otters were also frequently posted in the group. Popular in the Southeast Asian pet trade, most otter species are protected due to declining numbers in the wild. However, because many of the adverts were for infants, it was not always possible to determine which otter species was being sold, and therefore whether it was protected.

“Using Codes So The Group Stays Safe”

Despite Facebook’s total ban on animal trading, including pets, in the group titled: Civet/Pet Buying and Selling in the Greater Jakarta Area, members were instructed in the “About” tab to “prioritise using codes so the group stays safe from being banned.”

Alphanumeric codes were used to discuss animal prices in eight of the nine groups identified by Bellingcat. According to the Indonesian news outlet Jateng Today, the use of pricing codes, intended to circumvent Facebook’s automated moderation systems, is not uncommon among animal traders on the platform.

Such codes use the letters A, B, and C to denote different Indonesian rupiah denominations. A stands for a Rp100,000 note (about US$6), while B represents a Rp50,000 note (about US$3). An accompanying number specifies the quantity, so A3 indicates three Rp100,000 notes.

In the post below, one member asks, “A2 dapet apa?” – “What does A2 (Rp 200,000; US$12) get you?”

The post received 69 replies, with members offering everything from otters to owls, civets and geckos.

The term “Wc” – a common shorthand in animal trading groups for “wild-caught” – was also frequently used across all nine groups. Under Indonesian law, even if a species is not listed as vulnerable or protected, capturing and selling wild animals without a permit is illegal.

Asked whether its moderation systems could detect cost codes (as text or embedded in images) or key terms such as WC (when found next to images of animals), Meta responded: 

“Bad actors constantly evolve their tactics to avoid enforcement, which is why we partner with groups like the World Wildlife Fund and invest in tools and technology to detect and remove violating content.”

The Operators

While investigating the operators behind all nine groups, Bellingcat identified six Facebook profiles that led back to one individual broker based in Jakarta. 

By navigating to the “People” tab in one of the groups, a list of admins and moderators appears, including an account referenced below as AB. Despite AB’s profile being locked, a search with the term “wa.” (WhatsApp’s click-to-chat feature) returned dozens of animal adverts alongside a phone number.

Using the phone number to search for AB’s historic posts, six out of the nine groups under investigation were found to have adverts for vulnerable species, including this advert for a binturong. 

Listed as vulnerable by the International Union for Conservation of Nature (IUCN), keeping a binturong, let alone trading it commercially, is prohibited under Indonesian law.

AB has also advertised this “Celepuk Wc”, a wild-caught scops owl, seen below. Although the species itself is not protected, selling a wild-caught owl in Indonesia without a permit (which are tightly regulated) violates Indonesian law.

By following the phone number shared by AB, five more Facebook profiles were uncovered. The six profiles frequently shared similar adverts, often within days of each other, for the same species, sometimes featuring a similar interior background, and always listing the same telephone number.

Late last year, one of the accounts referenced below as W, posted this wreathed hornbill, a protected species in Indonesia. 

Of the six profiles, only one, named Azie Soka Smithh has ever posted personal data, including a profile picture of a man with a child. 

Further investigation into Azie Soka Smithh confirmed their presence on other platforms, including Telegram and Instagram. However, their full legal name remained unknown. While searching for visual clues to their location, it became apparent that the vast majority of images had been tightly cropped, revealing little about their whereabouts – except for a handful of images that appeared to have been taken at the same location: a pet shop.

In the adverts shown below, a poster can be seen on the wall behind the cage displaying the shop name Station Sato Exotic and a phone number. Of all the images seemingly taken in the same shop, none featured species protected under Indonesian law. However, the long-tailed macaque shown below is considered endangered according to IUCN due to declining numbers in the wild. 

A Google search for the shop’s name and number returned a Google Maps listing for Station Sato Exotic. A man named “beni” had left a five-star rating as well as several dozen photos and videos of the pet shop’s interior, including one that appeared to show a man sitting next to an identical poster as seen in the animal adverts. 

According to beni’s Google account, his full name is Beni Abdul Hamid (translated from Arabic). His bio reads: “We sell various kinds of accessories, cages, animal feed, etc” (translated from Bahasa Indonesia).

Of the 16 photos and 25 videos posted by Beni, several showed a left hand holding animals up to the camera, with a distinctive mole visible on the wrist. A seemingly identical mole appeared in several of the adverts posted by the six Facebook accounts sharing the same phone number. Notably, the mole and wrist were not seen holding species protected under Indonesian law. However, the long-tailed macaque shown below is considered endangered according to IUCN.

Upon visiting Station Sato Exotic, our partners at Mongabay confirmed that Google reviewer Beni Abdul Hamid was in fact the owner. His son, Jordan Bastian, who was present on the day, told their reporter he now manages the shop on his father’s behalf.

Bastian confirmed that it was his wrist and mole in the adverts and that he had taken all of the photos inside the shop. However, he said he was not behind any of the six Facebook accounts and that they were most likely run by a local broker. He explained that his business relies on a network of brokers operating on Facebook and WhatsApp. He sends them photos of the animals he has for sale, and they handle sourcing and organising everything with the buyer in exchange for a cut of the profits.

“I’m a broker. I’m involved in marketing the animals, so I provide the photos,” said Bastian. “I don’t want to know about the buyer.”

When shown the Facebook account for Azie Soka Smithh, Bastian confirmed that the man in the profile picture was a local broker, but one who seldom visited the shop.

Station Sato Exotic Pet Shop also has an online presence on Tokopedia, a major Indonesian marketplace. The platform’s guidelines prohibit the sale of endangered species, but are not clear regarding the sale of other animals, including pets.

Of Station Sato Exotic’s 71 current listings, the large majority have been miscategorised. Animals are listed as tools, toys, aquarium decorations and books. They are also miscategorised as other species; for example, birds and squirrels have been listed as hamsters or reptiles.

One advert features a vulnerable cuckoo species, the Sunda Coucal. Endemic to Java and numbering fewer than 10,000, this bird has been listed as vulnerable since 1994.

Asked whether he had sold many animals via Tokopedia, Bastian said his account had been blocked after he was banned for selling squirrels. When shown the advert above for the Sunda Coucal, he said he was surprised to learn it was classified as vulnerable. Tokopedia did not respond to requests for comment regarding an advert for a vulnerable species appearing on their platform. 

On the sale of protected or vulnerable species more broadly, Bastian admitted he had in the past, but has since stopped, describing “the risk is big” and saying he prefers to “play it safe.” 

After contacting the local authorities for comment, three officers from the West Java Natural Resources Conservation Agency (BBKSDA) made a surprise visit to Station Sato Exotic, due to the shop having previously been reported for selling protected species. Head of Conservation Stephanus Hanny said that upon arrival, “We went inside and checked every animal… We did not find any protected species.” He added that even the sale of non-protected wildlife requires a permit, which the shop does not currently hold. However, since it’s not a criminal offence, Hanny said they could only issue the owners with a warning. 

Bellingcat also contacted the phone number associated with Azie Soka Smithh. The person replied, confirming they managed all six accounts but denied selling any animals, including protected and vulnerable species. “I’m just a hobbyist. An animal lover,” they said. 

Given that the account had been found advertising vulnerable and protected species for sale, the Indonesian Director General of Forestry Law Enforcement, Dwi Januanto Nugroho, said authorities would investigate. Asked how their team of investigators was adapting to the illegal wildlife trade growing online, Nugroho replied:

“Criminal behaviour continues to reproduce itself in order to survive. In fact, it can evolve faster than the law enforcement system itself. In response …cyber patrols and desk analysis via the operations room will continue to be intensified, while we further optimise support from volunteer networks, working partners, and public participation.”

After contacting Meta, all six accounts, including Azie Soka Smithh, and all nine groups, totalling 70,000 members, were shut down. Meta confirmed: “We removed the Facebook groups and profiles in question for violating our Restricted Goods and Services Policy.”

Merel Zoet and Claire Press contributed to this report.

Bellingcat is a non-profit and the ability to carry out our work depends 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 and Mastodon here.

The post How Wildlife Traffickers Are Using Coded Language to Sell Protected Animals On Facebook appeared first on bellingcat.