October 24, 2023

Project Sapling, Sierra Leonne

Project sapling case study

Bluepoint takes pride in having played a key role by supplying robust and durable loop lock/self-tie tags to the exciting Project Sapling in Sierra Leone. Here's the story behind it:

Global attention is increasingly turning to large scale reforestation as a means of mitigating the causes and consequences of climate change and reversing environmental degradation. However, reforestation investment in some areas has historically been hampered by difficulties in verifying and monitoring activity on the ground. This has also been compounded by scandals in the ‘carbon offsetting’ market.

So, how can you prove investment in tree planting is actually resulting in ‘trees in the ground’, when verification is so difficult?

Under the UK Aid Frontier Technologies (FT) programme, a project was established in late 2022 to investigate and trial specific technical innovations to support reforestation verification. The project revolves around two methodologies: (1) local communities becoming custodians of the forest and therefore ensuring its sustainability; (2) reliable and objective web-browser-viewable verification based on data fused from mobile phones and drones of the tree saplings and plantation area respectively. Providing a clear and intuitive verification system to those wishing to invest in reforestation could help unlock additional investment in this sector. 

An earlier phase of the project saw drone-mapping tested and evaluated in Cornwall (UK) to assess the technology’s ability to map large areas quickly and efficiently. Building on the learning produced, our next sprint centered on proving that the data necessary to provide objective verification of reforestation activity could be captured effectively ‘in the field’, even in the most difficult of contexts, using drones and a specialised smartphone app.

The first live field trials occurred in Sierra Leone May 2023, with the objective of deploying and testing the mobile app and drone data capture in extreme remote context.

How Remote?

For the tech innovation to be applicable across all contexts, it must be operable in remote areas devoid of modern external infrastructure such as wi-fi and mobile cellular networks. The choice of pilot site needed to reflect this. The site selected was a community in eastern Sierra Leone, just south of the Loma Mountains.

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Field test site, Loma Mountains in Sierra Leone.

Travel to the community took 12 hours by Land Cruiser from the capital, Freetown. Unsurprisingly, the journey was less than straightforward and resulted in 50% of the convoy vehicles being stranded at the side of the road en-route from damage.

The road was typical of that found in many remote regions of Africa. We were told that it is impassable in the rainy season, cutting communities off from outside access for months on end by anything other than people on foot or occasional bike.

The Verification Tech — Proving Planting Activity

The verification solution comprises two parts: (1) Data collection (2) Data Fusion and Representation.

Individual Tree Level Data


In order to verify and potentially model a newly planted and growing forest, data about each individual sapling needs to be captured, ideally at the point of planting. This will allow the tracking of the plant’s full life and reduce implementation risks, such as double counting or errors with date records.

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Getting ready for planting. The verification system collects data about each individual sapling.

A research exercise was completed that investigated different existing ‘carbon verification’ standards. This revealed not just differences in the data collected between the different standards, but an opportunity to blend the best of each to create a new standard that is simple, intuitive and comprehensive — to become best in class!! This was distilled to a data requirement for each sapling to include (1) planting date (2) precise location (3) who planted it (4) photographic evidence of the planting (5) species information (6) a unique identifier for the tree.

The App

One of the key innovations of the project is the use of a specialised smartphone ‘app’ to capture key information about each individual sapling as it is being planted. Capturing this data provides evidence of activity (which is then verified and monitored by drone) as well as further downstream analysis. The identified data capture fields (above) were used to provide framework objectives for the developers and coders, who designed the data structure and technical issues such as file formats, data flow and upload compatibility. Crucially, the technical principles to fuse the data from the app, with drone imagery, was established. The decision to develop the app exclusively for the android platform, excluding compatibility with iOS, appears in hindsight to have been a good decision and saved development £cost, as the options for ruggedised smartphones running android are significantly wider and cheaper than iOS.

Field research in an earlier sprint by our implementation partners had shown that both the digital and text literacy levels of people living in remote communities in Sierra Leone varied considerably, even within the same community. Therefore, the app design principle was adopted to minimise the required literacy level of the user to the lowest possible, whilst maintaining product viability. This would mean designing it to be graphically driven, follow a linear process with few ‘options’, and be extremely simple to use. This approach would broaden the potential ‘user base’ and support social inclusion when implementing the system into other communities beyond this pilot. As the design evolved, there was a need to test the interface with the intended users. Unfortunately, communication with the remote community was challenging, hampered by lack of internet, phone and poor road transport links. The decision was therefore made to use the team at TCS as a proxy for the community workers, having built an understanding of local capabilities and capacities through previous projects. Being based closer to Freetown meant that they were ideally located to provide feedback on the app design, virtually. Collaboratively and through an iterative process of consultation, development and refinement, the design was finalised. Further, the technical functionality of the app was tested in different parts of the UK to ensure the GPS tracking and QR code reading worked reliably.

On completion, the app was uploaded onto ruggedized Android mobile smartphones in preparation for field testing in Sierra Leone.

Some screenshot examples showing the graphical nature of the app.

Field Implementation 

In a nursery behind the school building, the community prepared 2,000 saplings for planting within the vicinity of the village. Each sapling was individually bagged, supported by a cane or stick, around both of which went a loop lock ‘tree tag’ marked with a unique QR code. The specialist self-tie tree tags, provided by Bluepoint, used under this Sapling protocol are long-life and recyclable, with a toughened resin ‘ink’ for the QR code to ensure reliable scanning by smartphone.

As the community planted the saplings, working in individual teams, the data from each sapling was collected via the smartphone app. The process took less than 30 seconds each time, which fell to 15 seconds with practice.

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Reading the QR code on the sapling tag

Objective Aerial Verification — An Eye In The Sky


In parallel with the mobile app data capture at the ground level, higher-level objective verification and surveillance / monitoring is required to maintain security and credibility of the process. This was fulfilled by the use of a drone.

Drones have the advantage of being an ‘eye in the sky’ able to view large areas of land completely independently of the terrain and ground constraints such as rivers, forests and impassable tracts. Using onboard sensors and camera(s), drones can rapidly conduct ‘aerial audits’ with the images ‘stitched’ together to form an orthomosiac — a big picture view that can be zoomed into for detail.

All drone flights were successful, mapping 14 hectares at a GSD resolution of 2cm/pixel, taking just under 4,500 images of this remote, rural area of Sierra Leone. The data was captured and stored on micro SD cards in the drone, ready for transferring to a specialist computer for subsequent processing.

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