The Challenge

Stretching 600 kilometers along Germany’s western border lies a vast network of concrete bunkers and fortifications built in the 1930s and 40s. Known as the Siegfried Line (or Westwall in Germany), this defensive system comprises approximately 20,000 distinct structures, half of which still exist in some form today.

For decades after WWII, these structures were systematically destroyed or buried. But attitudes have changed. Today, they’re protected as historical monuments, and volunteers working with German federal monument services are documenting, monitoring, and securing them before they deteriorate further or pose safety risks.

Patrice Wijnands, a geomaticist and volunteer coordinator, has been mapping these fortifications for 30 years. Five years ago, he discovered QField, and it transformed how this massive conservation effort operates.

The fortifications present unique challenges. Many were filled with sand in the decades following the war. Now, that sand is settling and draining away, creating new gaps and hazards.

“The sands with which these bunkers had been filled up is now filling in and it opens new gaps, new holes in the surface. That is especially the thing we are tackling because we not only map these objects, we also get back to them every few years.”

The scale is immense: tracking safety risks, determining protective measures, and monitoring changes across a zone 600 kilometers long and up to 30 kilometers wide. Some structures near populated areas require fencing. Others in remote woodland need only warning signs. The key is knowing which is which, and tracking how conditions evolve.

From Paper Maps to Digital Collaboration

For 15 years, volunteers mapped using paper maps, each person working on their own island. Patrice would receive photocopied maps and Excel spreadsheets, spending hours trying to reconcile different symbologies.

“What does that mean when they made a cross on the map? How does that fit with their Excel sheet that they maybe sent me in 2005?”

Patrice began using specialized GIS software in 2010, but found it difficult to share projects. In 2017, he started experimenting with QGIS. Then in 2019, he discovered QField.

“I started using QField in 2019 and I felt that this was the state of the art after the tools I used before.”

Scaling Up with QFieldCloud

Within six months, Patrice began distributing QField projects to other volunteers. But as the network grew—especially during COVID-19—manually distributing projects became unsustainable. Then QFieldCloud arrived, solving the scalability problem completely.

“I just create these projects. I put them onto the cloud. People can gather them. They go into the field. The only thing I need to explain to them is how to map, what data are to be collected. They learn that within a few minutes. They upload these data and synchronize again with the QField cloud. And I can see these data in the next minutes again already here on my desktop.”

The project by numbers:

• 600 kilometers: Length of the fortification zone
• 20,000 objects: Total fortifications mapped today
• Scalable to 100+ volunteers: Thanks to QFieldCloud

Keeping It Simple

Volunteers work with point data, adding observations to a monitoring layer rather than editing the base map. This eliminates GPS accuracy issues—critical when working under forest canopy where positioning might be off by 50 meters.

“Every person visiting an object adds a point. That means if next year somebody else is getting there, he adds a new point.They only add their observation and the interpretation is something for me and for the people post-processing this data later on.”

The system captures photographs, condition assessments, and observations about changes since the last visit. Because volunteers often work in areas with poor mobile coverage, Patrice includes offline maps and advises volunteers to synchronize at home.

Learning QField: Minutes, Not Months

New volunteers become productive quickly. Patrice typically provides initial training through a single online or face-to-face session, and volunteers are mapping independently within weeks.

“I have seen people going into this without even having a GIS system before and they work with this after a few weeks as if they have never done anything else before. It does not need some software in which you need to spend a lot of time to learn it. That’s my job. And all the people outside in the field, they don’t need to understand that in that depth.”

This ease of adoption means Patrice can focus volunteers on what matters: understanding the history and significance of what they’re documenting.

Data That Serves Multiple Purposes

The database serves various stakeholders with different needs. Some structures can be made public for educational purposes. Others must remain confidential due to safety concerns, property rights, or ecological considerations.

Federal monument agencies receive data they need. Property owners get information about structures on their land. Ecologists access data about habitat. And the public gains access to appropriate historical information.

The system is also adaptable. Patrice has successfully tested the data model on other historical fortifications, demonstrating the approach could be applied to different types of monuments.

The Difference Open Source Makes

For Patrice, the open-source nature of QGIS and QField proved essential. Unlike proprietary alternatives he’d used previously, these tools could be freely distributed without licensing concerns.

“With QGIS there came the add-ins which made it powerful out of the box. You did not need any licenses. This is open source, it is easily distributable, everybody can download it and install it on their mobile device.”

When Patrice needed QFieldCloud access, he contacted OPENGIS.ch. The response was immediate and supportive — the only request was that the project link back to OPENGIS.ch on their webpage.

“I responded with creating a complete page about using this software. It’s the best thing I can do also for OPENGIS.ch in promoting their software and promoting this cloud service.”

More Than Just Mapping

For Patrice and the volunteers, the project serves purposes beyond safety and historical preservation. These tangible remnants of the Nazi era provide opportunities for communities to engage with history in meaningful ways.

“It is also a contribution to maintaining democracy. Here along the western border, everybody can get in touch with that history with concrete remnants around their own village.”

When history is local—visible in the woods near home rather than distant and abstract, it becomes personal. People become curious about their own community’s past.

“The people understand that it is a part of their identity and they are willing to view it also as a part of identity even if it has a dark history. It’s not my fault but it is my responsibility for maintaining it.”

Looking Ahead

What once required Patrice to manually process data from each person now scales effortlessly across dozens of contributors spread across hundreds of kilometers.

“I can in that way suit not 10 people, it can be 20, it can be 50, it can be 100. It doesn’t matter. That makes the process scalable.”

Technology hasn’t just made the work more efficient, it’s made an entire category of conservation and historical preservation possible that simply wouldn’t exist otherwise.

“Now I can just give someone a project of all objects in 20 kilometers around their home. We can collect data in a standardized way. You end up with a standardized database that you can start to query, that you can start to use for interesting analysis.”

About the Project:

The West Wall (Siegfried Line) fortification mapping project is coordinated by volunteer monument preservation groups working alongside German federal monument services, documenting and monitoring approximately 10,000 remaining WWII-era fortifications across a 600-kilometer zone along Germany’s western border.

The goal is simple to describe, but powerful in practice: create maps that show where people live and what matters to them. Rivers and mountains, hunting grounds, important forest resources, and the borders people recognize as their lands. The maps are built with the community, in their language where possible, and based on their understanding of the landscape.

This is not only useful for planning and storytelling. In at least one case, a community map was used in court as evidence of long-standing connection and use. The community could show place names and key sites in a way that helped to prove that they have been living in those areas for a long time.

Why mapping needs more than satellite imagery

Satellite imagery and topographic maps are helpful starting points. They show rivers, ridges, clearings, and settlement patterns. But they do not capture meaning.

A satellite image can’t tell you where ancestors are buried, where a place is considered sacred, which part of the forest is used for hunting, or which areas people avoid for cultural reasons. Those details live in stories, memory, and daily practice.

The reality in the field: connectivity comes and goes

A second challenge is technical but very real: internet access. Many of these villages have unreliable connectivity, and “offline” can mean days or weeks, not just a few hours between the office and the field.

That matters because community mapping usually involves large basemaps and plenty of edits from multiple devices. If you can’t reliably push data back and forth online, you need a workflow that works without it.

How QField is used in the workflow

The team prepares a QGIS project and packages it for mobile fieldwork using QFieldSync. Then QField is used for collecting features and attributes directly on Android devices in the field. A key part of this project is that distribution and syncing often happens by cable, not through the cloud.

1. Co-designing the project with each community

Before fieldwork begins, the team sits with the community and adapts the project to what they want to map.

Different communities care about different things. A village close to a river may want specific river sections and names recorded in detail. Another community might focus more on forest resources or cultural sites. Together, they decide:

• What feature types to include
• How features should be represented (point, line, polygon)
• Which attributes matter (names, local terms, notes, photos)
• What should be captured in the local language

This step is not only about data structure. It is about decision-making. Many communities have had mapping done to them by outsiders. Here, the community defines what the place is called, what counts as important, and what should be left private.

2. Moving projects and data without the internet

Because basemaps are large and connectivity is weak, the team transfers projects to devices using a cable. Community members collect data in the field and return to a local computer where the data is brought back into the QGIS project.

Later, when a stable connection is available, the consolidated dataset can be pushed to a database or cloud service for wider viewing and backup. But the core mapping work does not depend on that connection.

This also changes how versioning is handled. In the “industrial” workflow, you often assume you can sync frequently, resolve conflicts quickly, and keep changes small. In this context, the gaps are longer and the differences between versions can be bigger. The team keeps multiple versions and, when needed, compares and fixes conflicts manually to protect data quality.

A moment of real impact: giving the map ack immediately

One of the strongest moments in the interview was what happened when the community saw the collected data on their own phones. At first, the idea might sound like giving people a basemap. But it was more than that. It was their own collected layers, their own names, their own places, and their own priorities, visible immediately. It became a local “Google Maps,” except it reflected their world rather than someone else’s.

That created interest fast. The mapper described an evening when many community members showed up with smartphones, asking to get the map on their devices. For the project team, that was a clear sign the mapping wasn’t just a report deliverable. It was something people wanted to use and keep. QField made it possible for every community member to carry community data in their own pocket. So far, QField has been used in two communities, and its success ensures that it will be used again.

Being careful about what is share

The project is also very deliberate about data protection. Not everything that is mapped should be published. Sensitive cultural sites, graveyards, and other locations can be exploited if they are made public. Even among neighboring communities, boundaries can be contested, and publishing one version can create conflict.

For that reason, the team does not plan to publish all data to the web or to OpenStreetMap. There is interest in selectively sharing non-sensitive layers, such as river names, but only with consent and clear boundaries.

The final deliverable that matters most: a printed map

Digital maps are essential for fieldwork, but the “finished” outcome for many communities is a high-quality printed map, with a thoughtful layout and space for metadata and stories.

A printed map does something phones do not. People can spread it out, gather around it, point to places, and talk. It feels permanent. It can be stored, shared, and used as proof in a way that carries weight locally.

For the BMF and the Communities, the printed map is not an afterthought. It is one of the main goals, alongside the longer-term aim of keeping community data organized in a comparable way across multiple mapping projects.

The main objective was to allow operators to access in the field the graphic and alphanumeric data on trees, shrubs, hedges, turf and street furniture elements in offline mode both in reading and editing mode with the return of these data in GINVE.CLOUD via a synchronisation procedure.

For this purpose, it was decided to exploit the potential offered by QField and the GeoPackage database.

Preliminary project activity

Initially, a procedure was set up in GINVE.CLOUD to generate a Geopackage and the corresponding QField project file from the GINVE.CLOUD platform.

The Geopackage produced was structured to allow the management and mapping of data, supporting the insertion of point elements, lines, polygons and photos. In addition, form fields with customised attributes and value maps, value relations and check boxes were prepared in order to simplify data input by users.

In particular, the trees layer has been prepared to allow the management of forms for the collection of the following data: - Identification data - Dimensional and Qualitative - Notes-Other data - Damage - Interventions - Interference

Themes and labels were customised for each layer to make them similar to those in GINVE.CLOUD.

In addition to customising the data fields, special procedures for displaying the base map were created. Google Maps and OpenStreetMap were used as the base map, but the structure was prepared to allow the use of other raster maps so that they can be displayed and managed in QField.

Data Entry

The data entry activity refers to the possibility of entering data relating to the position of the element on the map, the compilation of the element’s master data sheet, with photography and planning of interventions.

The graphic data were entered using both automatic positioning via GPS and manual positioning using the positioning functions offered by QField. The alphanumeric data were entered by filling in specific survey sheets with differentiated data according to the element selected.

The collected data was then imported and synchronised with GINVE.CLOUD through a specific procedure that allows the verification of data (even from multiple operators) and alerts the user of any problems encountered, providing details of the error in order to facilitate its correction by the operator. This procedure also allows the import of photos that have been taken byQField and their automatic storage in the Cloud.

Results

Graphic and alphanumeric data were exported directly from GINVE.CLOUD into QField for immediate mobile use and management by operators. QField’s feature of being completely offline usable coupled with the possibility of predefined filling in of certain fields allowed data entry activities to be speeded up, reducing the possibility of human errors occurring and allowing users to be more efficient during census activities. Any conflicts with data in GINVE.CLOUD were handled during synchronisation by allowing the operator to choose and validate which data should be stored. Data imported from QFields are immediately available to all GINVE.CLOUD operators.

Thanks to QField, it has been possible to achieve new goals, enabling users of GINVE.CLOUD to use a high-performance and intuitive solution that provides continuity to the activities carried out in the field while guaranteeing maximum operational efficiency.

In addition, the integration enabled us to achieve the following objectives: - Use of maps and offline data on Smartphones and Tablets - Increased speed in data entry activities- Full compatibility with GINVE.CLOUD - Direct import of Geopackage from GINVE.CLOUD- Portability of data in QGIS - Data usable on other GIS platformsThe integration with QField represents an important step in the growth of GINVE.CLOUD and demonstrates its high readiness for interfacing with modern open source applications that make use of innovative, state-of-the-art technologies.

The Problem: Naturalists Without the Right Tools

Felix’s story begins in the environmental consulting world, where ecologists and naturalists face a common challenge. These professionals spend their days in the field documenting biodiversity—cataloging birds, plants, and other species for environmental impact studies. While they’re experts in their scientific domains, many struggle with the technical aspects of data collection.

“They’re using QGIS because they have to, but they’re not great at it. Usually it’s just to make maps—they make horrible maps”.

When Felix left his previous company, former colleagues approached him with a familiar pain point. They had lost access to their company’s field data collection software and needed something they could use independently. Their attempts with QField hadn’t been successful, and they were willing to pay for a solution.

The Open Source Gamble

Initially, Felix built a custom QGIS project for QField as a one-off paid commission. But as more people began asking for similar solutions, he faced a crucial business decision. Rather than trying to sell individual licenses, Felix made what seemed like a counterintuitive choice: he released everything as open source.

“In the naturalist world, they don’t have a lot of money to invest in this kind of technology,” Felix reflects.“It was a bit too much for them to put money on this when they could just do it by themselves.

But Felix’s open source decision wasn’t altruistic—it was strategic. He realized that giving away the software could become his best marketing tool.

The Trust-Building Business Model

Felix’s approach flips the traditional software business model on its head. Instead of selling licenses, he uses his open source QField project as a demonstration of expertise and a trust-building mechanism.

Here’s how it works: Step 1: Enable data collection - The open source project helps naturalists collect field data efficiently, solving their immediate technical challenge. Step 2: Build relationships - Users know Felix as the creator of the tool that solved their problem, establishing him as a trusted expert. Step 3: Monetize expertise - When users need help processing data, creating visualizations, or training their teams, they naturally turn to Felix for consulting services.

“That was to me like the first advertisement for my job,” Felix explains.“A lot of people contacted me afterward because they had trouble with the data when they got it back on QGIS and had to forward it to other companies.”__

Finding the Right Audience

Rather than competing in the crowded general GIS market, Felix identified a specific niche where he could add unique value. His target audience is highly specific: French naturalists and ecologists working in small companies or as freelancers, who need to collect biodiversity data using Latin species names.

“I had more knowledge in this specific domain than other GIS specialists, and I had more GIS knowledge than anyone in this field,” Felix notes. This positioning allows him to command premium rates because he understands both the technical and domain-specific challenges his clients face.

The results speak for themselves. Despite targeting a narrow audience, Felix’s latest project version has garnered 500 downloads—impressive numbers for such a specialized tool.

Marketing Through Value

Felix’s marketing strategy centers on demonstrating value rather than making sales pitches. On LinkedIn, where his target audience is most active, he shares completed projects and discusses his open source work.

“It’s way easier to advertise when it’s not just random pictures saying ‘I will make this blah blah blah,’” Felix explains.“You get a project that I made and you can check by yourself if it’s good or not.”

This approach resonates because it addresses a common freelancer challenge: building credibility without seeming pushy. The open source project serves as a portfolio piece that potential clients can actually test and evaluate.

The Community Effect

What started as a business strategy has evolved into genuine community building. Felix has partnered with Augustin, a botanist, to create a Discord server where users can suggest improvements and discuss features. The next version of their project will be developed collaboratively rather than by Felix alone.

“That’s actually a switch—it’s a bit of democracy, the way we can improve collectively,” Felix says. This community-driven approach not only improves the software but also strengthens the network of professionals who might become future clients.

Looking Forward: Plugins and Possibilities

Felix is particularly excited about QField’s plugin platform, which he sees as opening “a Pandora’s box for all kinds of applications.” He envisions integrating tools like PlantNet’s AI-powered species identification, creating workflows where users can photograph a plant, get AI-assisted species identification, and seamlessly incorporate that data into their field surveys.

To conduct ecological surveying more efficiently than has been previously achieved through traditional non-digital techniques, using QField as a medium for standardised and flexible field data collection.

Project Requirements

To facilitate effective field work, regular consultation with the Ecology team is required to ensure that QField projects are prepared appropriately. Firstly, basemaps comprising aerial imagery and proposed development plans are prepared by georeferencing and constructing pyramids in QGIS. Point, line and polygon vector layers are then created to support digitisation and associated data collection. Using a range of widgets, attribute fields are customised to meet the requirements of the Ecology team. These include:

Style

drop-down list of selectable styles tailored for conducting ecological surveys

Scale

drop-down list of selectable scales for point and line feature

Info

space for inserting a feature description

Image

location for images to be captured and stored

Geometry

contains self-populated geometry values, e.g. area, perimeter, length

Timestamp

records the time and date that a feature was created/last modified

Figure 1 - Overview of the traditional workflow in ecological surveying at Delta-Simons compared with the workflow created using QField. Single-headed arrows indicate the direction of one-way communication, whereas double-headed arrows indicate two-way conversation.

Result

Delta-Simons are saving approximately 40% of the time previously consumed by traditional ecological field data collection.

QField has significantly improved the ecological surveying workflow by: – Removing the need to recreate GIS outputs from physical drawings – Reducing the overall amendments required for GIS outputs – Improving communication channels (see Figure 1.) – Improving the accuracy and quality of data

The Project: Salamander Conservation in BUND Saxony

Working for BUND (Bund für Umwelt- und Naturschutz-Börgerland) in Saxony, a regional environmental protection organization with approximately 10,000 members, the project coordinator leads a team focused specifically on monitoring fire salamander populations and their habitats.

This five-year conservation initiative receives funding from both the EU (80%) and the state of Saxony (20%), with specific objectives to track fire salamander populations and assess their environments. What began as a solo monitoring effort has expanded to include four dedicated field workers who collect data systematically across numerous streams.

“Fire salamanders are indicators of environments that have remained largely untouched for extended periods and streams with clean water,” explains the project coordinator, who has a background in environmental monitoring. “We’ve discovered they can adapt to various conditions, which tells us important information about our local ecosystems.”

QField in Action: How the Team Collects Data

The BUND team has divided approximately 150 streams—totaling around 90 kilometers of waterways—into 100-meter monitoring segments. Field workers visit each segment twice during the first half of the year, which corresponds to the 2-5 month period when fire salamander larvae are present in the water.

Using QField, the team documents: – Fire salamander larvae counts in each 100-meter stream segment - Current water conditions (flowing, drying out, or dry) - Presence of food sources like gammarids and other aquatic invertebrates - Visual habitat assessments

Additionally, the team conducts annual water quality measurements for each stream, testing for nitrate, nitrite, ammonium, phosphate, and pH levels.

“When the field workers enter a new 100-meter segment, they can see it directly on the map in QField,” notes the coordinator. “They make a point for each segment, allowing us to visualize where larvae are abundant and where they’re absent.”

From Paper to Digital: The QField Advantage

Before implementing QField, data collection was a cumbersome process involving Garmin GPS units and handwritten notes with personal shorthand.

“I always worked with shortcuts and tried to type information as concisely as possible,” recalls the coordinator. “Therefore, other people looking at my notes couldn’t understand anything.”

This older method presented significant challenges: - Difficulty onboarding new team members - Limited data standardization - Time-consuming post-field work to translate cryptic notes - Challenges with photo documentation and location accuracy

QField, the team now has: - Standardized data collection forms - Real-time visualization of segment boundaries - Precise GPS positioning - The ability to include photos directly with observations - Seamless synchronization across the team

From Paper to Digital: The QField Advantage

Beyond the core team, the BUND project has created a simplified QField project specifically for volunteers. This parallel system allows community members to contribute meaningful data while maintaining the scientific integrity of the professional monitoring program.

“It was kind of a pain when volunteers just sent me pictures from fire salamanders and said where they found it. It’s a lot of work and time that I don’t have,” explains the coordinator. “Now with QField, we’re making a short video about how to use it and collect data, which I’ll send to everyone who’s sent me pictures over the last five years.”

The simplified volunteer project contains fewer fields but includes the ability to upload photos, creating a more structured citizen science program that feeds directly into the conservation work.

Conservation Impact: Why It Matters

The data collected through QField helps BUND understand the health of fire salamander populations and their habitats, which in turn informs conservation decisions.

“If we want to make changes in the environment to help the fire salamander, we pretty much just need to hold the water in the area. Our problem is that the streams are drying out,” explains the coordinator.

The information gathered identifies streams with only small populations (2-3 larvae) that are most vulnerable to extinction if conditions worsen. This allows BUND to prioritize conservation efforts where they’re most needed.

By making the data collection process more efficient and standardized through QField, the team can cover more ground, collect more consistent data, and ultimately make better-informed conservation decisions—helping ensure that this iconic amphibian continues to thrive in Saxony’s forest streams.

The Wadden Sea in Denmark, Germany and The Netherlands is of outstanding importance for many breeding bird species. Annually, the Schutzstation Wattenmeer participates in the Wadden Sea wide trilateral monitoring and assessment program (TMAP) and monitors the number of breeding birds in more than 100 monitoring areas in Schleswig-Holstein / Germany. For a number of species we monitor a significant share of the entire German breeding population.

Sketch of the project and monitoring areas of Schutzstation Wattenmeer (yellow).

Most fieldwork is carried out by annually changing volunteers which usually do not have much experience. Good supervision during the monitoring period in spring is thus very important. At the same time the amount of collected data is a significant bureaucratic challenge.

Until 2018 printed paper maps were used to collect the data in the field. Major disadvantages of the analogue system were:

– in the field was rather difficult without GPS positioning. – all results had to be counted and transferred to data tables and GIS manually, transmission errors were likely (about 18,000 observations are collected every year). – data could only be reviewed after the monitoring period and unlikely observations could not be checked directly.

For this reason, we have implemented a digital monitoring workflow using the power of QField, the advantages of a cloud storage system and the computational power of R. Most tasks are now fully automatized in R. Via the cloud data from all areas can be accessed and evaluated with daily topicality.

Sketch of the data transmission system. Field observations are logged in QField on a tablet and uploaded into a cloud storage. Data from all areas are accessed and automatically treated by an R script.

In spring 2019 we tested our system with seven tablet devices spread over seven of in total 12 different monitoring stations.

Project preparation

On a desktop computer we set up a QGIS project containing a high-resolution aerial image as background layer for orientation in the field. For the monitoring data we created a custom Geopackage database with predefined dropdown columns and entry restrictions. Additionally, we added predefined walking paths to guide the volunteers and to further standardise our monitoring.

Sketch of the QField Interface. For data entry we used a geopackage file with custom dropdown list and entry restrictions.

Logged observations are clearly laid out in QField.

We used an additional synchronisation App that automatically uploaded the field data from the tablet to a Google Drive cloud after fieldwork. For data download, automatized backup, data review and export we wrote a R script.

After data was automatically synchronized with the cloud the results from all the different areas can be reviewed via a custom R script.

Also visual review of the collected data is possible via R..

The general concept of QField as a simplified field application of QGIS turned out to be very useful for our work with volunteers. While we can set up a project with a high level of customization including all our needs in QGIS, field workers only need to understand the basics. A big advantage: unwanted changes are almost impossible in QField.

Field work

During field work orientation was much easier on the tablets compared to printed paper maps especially in the extensive salt marshes. Data entry was pretty fast thanks to the possibility to automatically reuse the last entered value. Logging observations on the tablet only took a little bit extra time in comparison to paper maps.

The field kit

The field kit

Evaluation and future

We had no software problems during a testing period in spring 2019 and everything worked as planned. In an evaluation survey all participants stated that they preferred using the tablet rather than the analogue paper maps for field work. The use of the custom QField project was evaluated as straightforward and easy.

In total more than 18.000 data points were collected in the field. Due to automatized data treatment we saved a huge amount of office time and avoided transmission errors. Also, data collected with tablets and GPS-positioning will be of much higher spatial accuracy. In the future we will thus fully switch to tablet based fieldwork.

Acknowledgment

We thank the Ernst-Commentz Stiftung, the Europäischer Tier- und Naturschutz Stiftung and the Adolf und Hildegard Isler Stiftung for generously supporting our project. Additionally, we want to thank the developers of QField and R for offering fantastic open source software. It is great that, due to free software, such projects can be implemented by a comparatively small conservation society.

Ghana’s Tano Offin Forest Reserve, one of the country’s most biodiverse areas supporting 569 species including seven globally threatened ones, faced a critical threat. Satellite imagery revealed that without intervention, the forest was projected to lose 56 hectares annually to illegal farming and logging activities. Traditional monitoring methods using “pen and paper” were insufficient to respond quickly to emerging deforestation threats detected by remote sensing. The Alliance of Bioversity International and CIAT , working with local partners including the Ghana Forestry Commission, needed a way to rapidly ground-truth satellite alerts and engage local communities in real-time forest monitoring. The challenge was particularly complex given the rural setting where many community members had limited literacy and technology experience.

The Solution

The project team implemented an innovative community-based monitoring system using QField on Android tablets. Twelve young farmers were trained as volunteers to verify deforestation alerts generated by the Terra-i satellite monitoring system, which uses freely available Sentinel 1 and 2 Copernicus satellite data to detect forest changes fortnightly.

The QField workflow was designed for simplicity:

- Volunteers receive GPS coordinates of deforestation alerts on their tablets - Working in groups of at least two for safety, they navigate to the alert locations - Using a simple QField form, they confirm whether deforestation is occurring - They identify the cause (farming, illegal logging, or other activities) - Photos are captured directly in the form - Data is synchronized back to researchers and the Ghana Forestry Commission

“The idea is not to confirm whether it’s deforestation or not - we can see forest clearing from satellite imagery. The goal is to stop deforestation from expanding. If it’s monitored when it’s just started, then a farm won’t grow from one hectare to 10 hectares.”

Accountability Team members

The Result

After just one year of implementation (July 2023 to July 2024), the project achieved extraordinary conservation outcomes across 1,044 hectares.

Environmental Impact:

- 71% reduction in deforestation compared to business-as-usual projections - 38% reduction compared to historical deforestation rates – Annual deforestation dropped from a projected 56 hectares to just 16 hectares - 6,911 tons of CO2 equivalent conserved - 65,303 cubic meters of water recharge protected annually - 39 hectares of biodiverse forest safeguarded

Community Empowerment:

- 423 households from 6 communities engaged in forest conservation - 12 young individuals received geospatial training and technology skills - Communities achieved $2,000 USD in Payment for Ecosystem Services rewards for health clinic construction

Accountability Team member checks alerts

Scaling Conservation Impact

The success has demonstrated the potential for expanding this model across West Africa and beyond. The Payment for Ecosystem Services approach, combined with QField’s accessible technology platform, provides a replicable framework for community-led forest conservation.

“If we expand it to another forest in Ghana or West Africa with this payment for ecosystem services model to motivate communities’ self-management of natural resources, then QField feels like a good technological platform to enable them to do that.”

The project showcases how mobile GIS technology can bridge the gap between satellite monitoring and community action, turning local residents from potential forest threats into empowered conservation guardians.

Coordinator helps check alerts

The Challenge: Managing Conservation at Scale

ZIP’s mission is breathtaking in scope: to completely remove invasive predators from New Zealand as part of the nationwide Predator Free 2050 initiative. These predators devastate native ecosystems, killing an estimated 25 million native birds annually.

In South Westland alone, their project area spans 114,000 hectares of challenging terrain. This extraordinary achievement represents a vast predator-free area, protected on all sides by mountains, rivers, the ocean, and a network of remote reporting traps and detection devices.

As a frame of reference - “The scale of the project area is extraordinary: 10 times the size of New Zealand’s largest predator-free island, and over 30 times the size of the largest fenced sanctuary.”

Waitangitahuna River on the left and Whataroa from Mt Price

Tracking 20,000 Devices in the Field

The scale of data management required for this operation is immense. ZIP manages approximately 20,000 field devices through QField in their South Westland project alone—including trail cameras, AI cameras, bait stations, and cage traps. Each device requires ongoing servicing, maintenance, and data collection.

For each device location, the team needs to record three types of information: Actions: Deployment of equipment and setup details Events: Servicing information and maintenance records Properties: Operational data such as bait take, lure status, and battery levels

Before QField, this information was captured using Garmin GPS devices and paper notebooks—a cumbersome process that created significant delays in data processing and limited the effectiveness of rapid response operations.

The Evolution to Digital Data Collection

ZIP’s journey to QField began with a creative but limited solution—using Garmin GPS units to manually record data in JSON format within text fields. While innovative, this system showed its limitations as operations expanded from a 400-hectare pilot site to 12,000 hectares of challenging terrain.

“The GPX capture was starting to show its limitations, and using notepads to record data was pretty much showing its limitations,”

After researching digital field data collection options, ZIP chose QField for its seamless integration with their existing open-source GIS stack (QGIS, PostgreSQL, and GeoServer), its robust offline capabilities, and the rich functionality it offered.

“The ability to control symbology and just the amount of data that could go out in a packaged map on the phone was fantastic,”. “It worked so much better than Garmins and paper notes.”

QFieldCloud: Transforming Response Times

The introduction of QFieldCloud revolutionized ZIP’s field operations. Before QFieldCloud, data synchronization involved physically collecting phones from field staff every two weeks, manually connecting each device to a computer, and processing the accumulated data—causing significant delays in data availability.

“The data turnaround was around about two weeks. You record on your phone for two weeks and then sync it,”. “There was a bunch of things that happened in the back end, like SQL scripts to refresh symbologies.”

Now, with QFieldCloud, field staff can sync their data daily, providing near real-time information flow critical for responding to predator detections.

“If we’ve cleared an area of predators and then we get a predator appearing inside our block, we have to be able to react to that very quickly. If it’s a possum, if you’re not onto that possum within a couple of weeks, it’s likely moved 20 or 30 kilometers from where you found it. Being able to respond as quickly as possible is sort of the key to success for us.”

This rapid data flow has transformed their ability to coordinate teams and respond to incursions efficiently.

Field Team Coordination

ZIP has approximately 80 staff members across the organization, with about two-thirds working in field-based roles. At any given time, around 35 active QField users are collecting and accessing data across multiple project sites.

When responding to a predator detection, teams work intensively in a targeted area. QFieldCloud enables staff to see exactly what has been done and what remains to be done, eliminating duplication of effort and improving coordination.

“The ability to go out, sync their data, and then go out the next day and go, ‘okay, this is the section that’s left to do’—that’s been game-changing,”.

Field staff also use QField to access critical information in the field, including: - Trap and bait station locations - Device deployment details - Health and safety information (tracks, wasp nests, mine shafts) - Predator sighting locations

Looking to the Future

As ZIP continues to expand their operations—including beginning work on Stewart Island (Rakiura), a 170,000-hectare project—the scalability of QField becomes increasingly valuable.

The organization remains committed to the open-source technology stack that supports their conservation work. “We’re very appreciative of the open-source stack that supports all of our work,”. “The amount of data that we’re housing—our detection records are in the tens of millions—and that’s all on PostgreSQL and GeoServer.”

📷 Photos taken by Chad Cottle