Yes – we recommend that drone operations are carried out by people with proper training and good knowledge of local drone regulations. If you want high‑quality results, you should use professional‑grade drones with high‑resolution cameras, not simpler consumer drones.

Another option is to use professional drone pilots. We work with a broad network of qualified pilots and can recommend operators if needed.

Current forest inventories are mostly sample‑based, with field crews measuring trees and other parameters on site. This is time‑consuming, expensive, and prone to significant measurement errors. As a result, many important decisions about the forest and its environment are based on rough estimates rather than solid facts.

Conifer Vision’s drone‑based AI analysis delivers tree‑level accuracy and provides a unique combination of lower cost, higher flexibility and much richer detail – from seedlings all the way to mature stands throughout the entire forest life cycle.

Yes, we can. Our data can be seamlessly integrated with just about any customer forest management systems, GIS-systems or other forest application that holds your forest data or forest plan.

 

Contact us to get available options.

Satellites only provide low-resolution images, resulting in sparse species identification and statistics-based metrics. Studies from SLU (Swedish Agricultural University) show that satellite‑based estimates of forest condition can have errors of several tens of percent at national scale. In particular, dense, high‑biomass forests tend to be systematically underestimated, while more open forests are overestimated.
 
Satellites give superior coverage for macro-perspective analysis on a regional or country level whereas drones are superior for detailed analysis on a local level.

However, the lack of species identification and spectral information from LiDAR means RGB and multispectral cameras still have a strong advantage when it comes to seeing tree species, health status and disease.

Traditional forest data collection methods like field surveys and national laser scan (LiDAR) are slow, costly and infrequent, and satellites trade detail for coverage and only provide coarse information. Field surveys require manual measurements that can take weeks or months, and national laser scans are updated only every few years.

Drones offer near real-time data collection with high‑resolution imagery that can be updated as often as needed, in some cases even daily on selected areas. This enables quick detection of changes in the forest and faster, better‑informed decisions. When AI is applied to drone data, imagery can be automatically processed to identify individual trees, measure their characteristics and detect potential threats like damage, disease or stress.

The combination of modern drone technology and AI provides a level of detail and update frequency that traditional methods cannot match, making forest monitoring more efficient and timely. It opens up new possibilities for proactive forest management, such as early detection of bark beetle outbreaks or targeted harvesting and conservation efforts.

Our technology stands out through a unique mix of precision, scale and automation at low cost. Advanced AI, trained on a wide range of forests , lets us measure individual trees with high accuracy while covering very large areas, under strict standards for measurement quality.

We deliver results through an automated platform that turns your drone data into clear, ready‑to‑use forest insights, keeping inventory costs low compared to traditional methods. Backed by proven technology and experience from working with the forest industry, our solutions cover the full forest lifecycle – from seedlings to mature stands – giving you faster, richer and more reliable decision support at a fraction of the cost of conventional inventories.

Our technology is currently optimized for Nordic forests, but can be adapted to all forest types through our automated AI training methods. Our models are finely tuned to Nordic species, stand structures and management practices today, giving highly reliable results in boreal and hemi‑boreal conditions.

Because we use automated training pipelines, our system can rapidly be adapted to new regions, species mixes and management styles without starting from scratch. As we collect data and collaborate in new areas, the AI can learn local conditions, making the same platform applicable to everything from intensive production forests to mixed, natural and protected stands.