# Mapping gold mines under the French Guiana rainforest: return of experience with different mobile lidar systems

Thomas Dewez, Sébastien Linares, Silvain Yart, Florian Masson, Marie

Collignon, Lucas Rivera, Caroline Bedeau, Matthieu Chevillard

► To cite this version:

Thomas Dewez, Sébastien Linares, Silvain Yart, Florian Masson, Marie Collignon, et al. Mapping gold mines under the French Guiana rainforest: return of experience with different mobile lidar systems. EGU 2024, EGU, Apr 2024, Vienne (AUT), Austria. hal-04508900

HAL Id: hal-04508900

https://hal.science/hal-04508900v1

Submitted on 18 Mar 2024

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# Mapping gold mines under the French Guiana rainforest: return of experience with different mobile lidar systems

Thomas Dewez¹, Sébastien Linares², Silvain Yart¹, Florian Masson¹, Marie Collignon², Lucas Rivera¹, Caroline Bedeau³, and Matthieu Chevillard¹

¹BRGM, ²EMOPI - Cayenne, ³ONF - Cayenne

EGU2024-GM 3.2 - Vienna - 15 April 2024
## Context

# Where is gold exploited? How?

In northern South-America gold is found in the green rocks belt of the Guiana shield (~2 bn years old)

## Mining targets two gold sources

* **Secondary gold** in rivers (downstream from the source)

* The most common type of extraction

* Cut down the forest, scrape the sediments, sieve them, recover the gold

* **Primary gold** in bedrock

* Cut the trees, scrape the surface and dig open pits

* Drive shafts down in the rock and drill galleries (no need to cut down the forest)

Each target has specific associated shape and objects

Primary gold extracted from pits and galleries is the most difficult to spot for airborne law enforcement patrols

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<figure><img src="image_5.png" /></figure>

## Overarching aim

# How much gold is extracted illegally in French Guiana?

Go find, count and map gold mines in the rainforest !

And by the way, use lidar, it sees through the vegetation

Right, but here is the context :

* Highest above-ground biomass in the world (Amazonian forest)

* Remote regions of interest (walk, drive, sail, fly)

* Sudden localized thunderstorms (no internet to know in advance)

* Dark wet soils

To design a lidar acquisition scheme, you need to optimize :

* Desired information contents (what do you need to see?)

* Fitness for purpose (will you see? isn't it overkill?)

* Costs (won't it cost an arm?)
## Primary gold extraction sites

# Open-sky mining structures

### Geometric objects linked to the mine

#### Objects linked to ore extraction typical dimensions

* Access features (shafts and galleries) ~1m²

* Extracted material (stock piles and platforms from spoils) 1-30 m²

* Hoisting devices < 1m²

* Woodcraft equipment (boards) <1m²

#### Objects linked to gold recovery

* Rock processing units (crusher engines and support) 0.25-0.5m²

* Sluice with downstream sand cones 1 m²

#### Objects linked to workers

* « Comfort » structures (benches, roof frames) 1m²

Overall feature lengths in the range 1 cm to 10 m

<figure><img src="image_6.png" /></figure>
## Primary gold extraction sites

# ...and under the canopy

### Extraction sites

* Platforms, shafts and sheltering structure

### Logistics infrastructures

* Dirt tracks and narrow foot paths to reach working sites and living bases

* Accommodation (groups of rectangular cabin frames, possibly with tarpaulin)

* Furnitures (wooden benches, tables...)

Can these features be detected, counted and characterized ?

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Tested carriers and sensors

<figure><img src="image_13.png" /><figcaption>ALTOA</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>ALTOA</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>L'Avion Jaune</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>28° GG</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>BRGM</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>BRGM</figcaption></figure>

<table><thead><tr><th>Carrier</th><th>Plane</th><th>Helicopter</th><th>UAV VTOL Quantum<br>Trinity F90+</th><th>UAV DJI M300<br>Quadcopter</th><th>UAV Flyability<br>Elios3<br>Quadcopter</th><th>Zeb_Horizon</th></tr></thead><tbody><tr><th scope="row">Carrier<br>coverage</th><td>10<sup>6-8</sup> m<sup>2</sup> (10s km<sup>2</sup>)</td><td>10<sup>6-7</sup> m<sup>2</sup> (10 km<sup>2</sup>)</td><td>10<sup>6</sup> m<sup>2</sup> (1km<sup>2</sup>)</td><td>10<sup>5</sup> m<sup>2</sup> (0.1km<sup>2</sup>)</td><td>10<sup>4</sup> m<sup>2</sup> (1 ha)</td><td>10<sup>4</sup> m<sup>2</sup> (1ha)</td></tr><tr><th scope="row">Payload</th><td>100+ kg</td><td>10-100 kg</td><td>1 kg</td><td>1-2 kg</td><td>&lt; 500 g</td><td>1.5 kg</td></tr><tr><th scope="row">Acquisition<br>autonomy</th><td>Hours</td><td>Hours</td><td>Up to 90 min</td><td>20-40 min</td><td>9 min</td><td>20-30 min</td></tr><tr><th scope="row">Autonomous<br>Terrain<br>following<br>capability</th><td>Yes, inhabited aircraft</td><td>Yes, inhabited aircraft</td><td>Yes poor</td><td>Yes poor, better if<br>RTK works</td><td>No manual fight</td><td>Yes, operator driven</td></tr><tr><th scope="row">Lidar</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th scope="row">System</th><td>Dual Riegl<br>VQ780(+Ili)</td><td>Riegl VUX-1LR</td><td>Qube240</td><td>DJI L1</td><td>Ouster Rev-6/Rev-7</td><td>Velodyne Puck<br>VLP-16</td></tr><tr><th scope="row">Range</th><td>&gt; 1km</td><td>&lt; 1km</td><td>Optimal 50m</td><td>Optimal 50m</td><td>100 m</td><td>100 m</td></tr><tr><th scope="row">GNSS RTK<br>dependance</th><td>No, uses PPK</td><td>No, uses PPK</td><td>No, uses PPK</td><td>Yes, but PPK<br>possible</td><td>No, SLAM-based</td><td>No, SLAM-based</td></tr><tr><td colspan="7"><strong>Phase 1 : Site Detection</strong></td></tr><tr><td colspan="7"><strong>Phase 2 : Site Mapping</strong></td></tr></tbody></table>
<figure><img src="image_19.png" /></figure>

# Results

## Phase 1 : Site detection - Airborne lidar survey

Point density target : > 400 pts/m²

Performed : min 403 pts/m², median 630 pts/m²; max 948 pts/m²

25cm/pix DTM
(60% pixels with lidar point)

Points penetrated ~1m² pit
shafts aperture (36 pits)

Base camp structures with and w/o tarpaulin
Contained in ≤4m AGL

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<figure><img src="image_24.png" /><figcaption>Zeb Horizon</figcaption></figure>

©TD - BRGM

<figure><img src="image_25.png" /><figcaption>Flyability Elios 3</figcaption></figure>

©Silvain Yart - BRGM

# Results

## Phase 2 : Site mapping – Close range lidar

dGNSS acquisition fails under the canopy, the SNR is too high

BUT SLAM-based lidars keep working without GNSS signal

* Handheld mobile scanner Zeb Horizon

* Mapping stock piles
    → ore volume

* UAV Flyability Elios 3 (live video, lidar, thermal IR)

* Mapping shafts and gallery network
    → quartz veins thickness and density

<figure><img src="image_26.png" /></figure>
# Lidar mapping of illegal gold mines under the rainforest canopy

## Conclusions

Even the Amazonian rainforest is somewhat transparent to lidar

Inhabited carriers (**planes, helicopters**) are best suited (cost 30-35 €/ha)

* To **fly just clear** of rugged tree-covered topography

* To **adapt** to changing aerological conditions

Airborne lidar systems **detect mining sites** (including 1m² pit shaft conduits)

Ground-based systems **sucessfuly map active exploitation features** (including stock piles and galleries)

Observed penetration was better than **1 ground point for 50 lidar shots** in Dorlin (but only 1 in 200 in St Jean)

* Canoy-top density > 400 pts/m² achieved 0.25 m/pix DTM (largest data gap < 8m radius)

<figure><img src="image_27.png" /></figure>

©TD BRGM

<figure><img src="image_28.png" /></figure>

Gold-bearing quartz - Dorlin

© M. Chevillard - BRGM
And because all wouldn't happened without them

Acknowledgements

This work was financially supported by

- French Ministry of Environment (MTES/DGALN)

- Préfecture de Guyane – EMOPI

- BRGM

Fieldwork was conducted under the protection of Gendarmerie Nationale,
9e RIMA and 3e REI

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**This presentation**

Dewez, T.J.B., Linares, S., Yart, S., Masson, F., Collignon, M., Rivera, L., Bedeau, C., Chevillard, M., 2024. Mapping gold mines under the French Guiana rainforest: return of experience with different mobile lidar systems, in: EGU General Assembly Conference Abstracts. Presented at the EGU General Assembly 2024, Vienna, Austria, pp. EGU24-15904. https://meetingorganizer.copernicus.org/EGU24/EGU24-15904.html?pdf

**Reference**

Dewez, T.J.B., Chevillard, M., Le Maire, P., François, B., Aertgeerts, G., Roux, B., Plaetvoet, J., Houlès, M., 2022. Méthodologie d'acquisition de données LiDAR par drone et perspectives de couplages géophysiques. Application à l'exploration aurifère alluvionnaire en Guyane (BRGM/RP-72082-FR). BRGM, Orléans, France. https://infoterre.brgm.fr/rapports/RP-72082-FR.pdf