Boosting forest productivity

Forest productivity depends on soil and climate, tree monitoring and management and efficient harvesting technologies and other factors. Understanding the effect and interactions of these will help us close the gap between actual and potential productivity to create highly productive future forests.

Scion's research has been helping grow the New Zealand forest industry for over 70 years and supports New Zealand forest growers’ vision to boost the profitability of planted forests and double export earnings from $4.7 billion to $12 billion by 2022.

Improving timber yield and quality

Genetics, environment and forest management or silviculture all affect forest productivity and wood quality. We are studying their effects and interactions to improve and grow the productivity of New Zealand’s forest resources.

Much of this work was carried out under the Growing Confidence in Forestry’s Future programme, a joint initiative between Scion, the forest growing industry and the Ministry of Business Innovation and Employment to raise the profitability of commercial forestry investments. This research programme was concluded in 2019.

Defining timber quality

Timber should be stiff, stable, look good and be free of defects such as knots, resin pockets and intra-ring checking. Our work has focussed on how to define and measure wood quality using characteristics such as wood density, spiral grain and microfibril angle.

We have developed an automated system for measuring a range of parameters from discs sawn from logs. The DiscBot robot measures wood density using x-rays, microfibril angle, chemical composition using near infra-red light, wood stiffness using ultrasound and spiral grain angle using light transmission.

Data from the DiscBot; combined with detailed information on silviculture and tree genetics, will allow us to make recommendations for boosting forest productivity.


Understanding the effect of site & geography

Site latitude, altitude, climate, exposure, slope, soil type, and depth all affect forest growth. Using geospatial information about these we can build up a comprehensive picture of the growing conditions across New Zealand and look at their effects on tree growth and timber yield. The information can be used to select the species or 'germplasm' for the best results from any one site.

Soil and nutrition

See Healthy Soils

Forest management

Forest management, or silviculture decisions, affect tree growth. Our work concentrates on the effects of stocking rate (trees or stems per hectare), tree thinning and pruning, weed control and related activities.

We have developed software that allows a forest manager to model silviculture variables to achieve maximum yield and wood quality.

Find out more about Scion's Forecaster software

Permanent sample plots

Much of our work is underpinned by forest growth data from our Permanent Sample Plot (PSP) system. The PSP contains information about the effects that factors such as environment, genetics and silviculture regimes have on the growth of stands and trees.

Read about Permanent Sample Plots


Peter Clinton, Scientist, Microbial Ecology - Soil Systems

Remote sensing technologies

Advances in remote sensing technologies, unmanned aerial vehicles (UAVs) and computing power are making a precision management approach to forestry possible.

Scion uses UAVs to collect LiDAR (Light Detection and Ranging) data, multi-spectral imagery and hi-definition video. The pathways and systems that we are developing for data processing will translate these data into information forest managers can use to manage commercial forest estates remotely.

Based on forestry industry needs our research includes:

  • Assessing the potential of aerial imaging for automated delineation and mapping of harvested areas
  • Using high resolution imagery to assess tree survival following planting
  • Comparing the cost, accuracy, and utility of data from lower cost image-based point clouds and higher cost LiDAR point clouds
  • Assessing the viability of UAVs to acquire LiDAR and other data over discontinuous forests not suited to fixed-wing planes
  • Monitoring forest health
  • Monitoring environmental impacts and performance during harvest operations and road building
  • Assessing loss and planning recovery after storms, fires and other disasters.

There are significant opportunities for industry partners to work with Scion in identifying, testing, and evaluating these technologies.



Michael Watt, Scientist, Remote Sensing and GIS

Forestry operations

Robotics, remote control and teleoperations are some of the options we are exploring to improve the safety and efficiency of forestry operations, particularly steepland harvesting.

Our work alongside the forestry industry to improve the quality and quantity of information available, and the tools to manage planted forests more effectively and safely, includes:

  • Developing and improving forest inventory methods
  • Biometrics and programming
  • Remote sensing and Geographic Information Systems (GIS)
  • Empirical modelling

See also:


Harvesting costs account for about half the total cost of wood production in New Zealand. This is partly due to approximately half the current harvest coming from steep terrain (slopes over 20°). This is forecast to rise to more than 60% by 2025.

Felling and breaking out in these conditions are very high risk jobs. Workers are exposed to falling trees, swinging stems and dislodged debris. Scion is developing a range of advanced technologies to increase the efficiency and safety of harvesting on steep terrain.

Our research includes:

  • Mechanisation of harvesting operations to improve performance and safety
  • Forest engineering, including management tools and robotics
  • Human factors and ergonomics to improve work scheduling, and health and safety
  • Environmental impacts and mitigation
  • Cost benefit analysis of new equipment and practices in harvesting systems



Richard Parker, Scientist, Additive Manufacturing and Emerging Technologies

Peter Clinton, Scientist, Microbial Ecology - Soil Systems