What is HPR data in forestry?

HPR stands for Harvester Production Records. It is a standard for storing logging machine production data in a structured file format.

The power of these records lies in the ability to connect detailed measurements from the processing machine with precise location data from GPS. In practice, this means linking the position of the logging machine or harvester head with information about each harvested tree and the products produced from it.

There are two types of HPR data:

• Cabin-located, which records where the machine was during harvesting
• Precision tree-located, which records the exact position of each harvested tree

The precision tree-located data is significantly more valuable.

Why is HPR data becoming more important now?

The main reason for the increased importance of HPR data is that precision-located data has become widely available, making it much more usable in practice.

With this type of data, traditional plot measurements can often be reduced or avoided, lowering inventory costs by approximately 30%.

Why is HPR data especially valuable for forest inventory and decision-making?

It produces huge, standardized datasets that allow AI modeling for timber measurements on a large scale. Field plot-based datasets will always be too small to allow training of sophisticated AI methods.

HPR datasets are large enough to support this.

What kinds of questions can HPR data help answer?

HPR data can support several types of forestry analysis, including:

• Inventory modeling
• Yield analysis at the product level
• Identifying which trees were harvested and which were left
• Determining the exact harvested area
• Track the entire route of the harvester in the forest

Using the logging machine tracking data, harvested areas can be reported with relatively light post-processing.

What are the main limitations or challenges?

There are several challenges when working with HPR data.

GPS disturbances, including those caused by global or regional conflicts, can introduce positioning errors.

In addition, the datasets are very large and often contain errors. Detecting and managing these errors is not straightforward, especially when working with millions of records.

Handling these issues properly is critical, as removing data incorrectly can introduce bias into the results. This is one of the key areas where Arbonaut’s vast experience in big data and biometrics comes in handy.

What role will HPR data play in the future of forestry?

HPR data will enable more adaptive and dynamic forest resource management.

By improving alignment with market requirements, reducing environmental impact, and increasing both cost efficiency and prediction accuracy, it is set to play an increasingly central role in decision-making across the forestry value chain.

And its full potential is still unfolding. For instance, could we assess the timing of forest management decisions with proper analysis of HPR data, which would in turn improve and automate decision-making in the future?

What is needed to fully utilize HPR data at scale?

HPR data is generated by cut-to-length (CTL) logging machines. Only these machines feed the log through the processor head knives and produce HPR data.

To fully use HPR data for inventory purposes, local data is essential. This requires CTL machines to operate within the target area, as well as local datasets to train accurate models for specific geographies.

It is worth mentioning that only one CTL machine in the target geography is enough to facilitate HPR, instead of replacing the entire machinery fleet. When these conditions are in place, HPR data can be reliably used at scale.

What are Arbonaut’s HPR-based products?

Arbonaut provides effective tools to intake HPR data and store it in a geospatial database. Additionally, there are tools for querying the most common errors in the data and cleaning it for further use. HPR data can be used directly from the database, for example, to outline past operations and support timeliness of forest information.

However, the greatest added value comes from using the HPR database as a large training set for AI. This approach allows for the use of highly local training data in forest measurements.

For example, imagine harvesting a mature spruce stand with some mix of pine and silver birch. If training data from three similar stands within 5 km radius is available, the error levels in your estimated diameter distribution for each species will be minimal compared to relying on smaller, more remote plot samples.

This enables a new level of value maximization in the industrial process: fine-tuning harvester value matrix to optimize the log utilization, selecting the best stands for harvest to meet the industrial process needs, and even purchasing the stands to match roundwood raw material needs.

Key takeaways

• HPR data links tree-level measurements with precise location data, enabling a new level of detail in forestry
• Its growing availability is driving real use, including AI-based inventory modeling and cost reduction
• HPR data supports inventory modeling, yield analysis, and accurate mapping of harvested areas
• The main challenges are data quality and positioning accuracy
• Providers such as Arbonaut are successfully integrating HPR data into customers’ forest inventory modeling and operational planning