Information technologies for forestry
Software tools and visualisations to support all aspects of forest planning and management.
Geospatial & bioinformation technologies
Integrating layers of data related to the people, places, environment and processes of forest into practical management tools is one area of our research.
Our team of ecologists, and geospatial and environmental analysts use Geographic Information Systems (GIS) to spatially analyse and visualise data from a variety of sources, such as aerial photographs, hyperspectral imagery and LiDAR.
Geospatial tools add value to existing data and statistical models. We can create models that give results across a land surface, allowing more powerful insights than graphs or empirical results. Dozens of map surfaces can be created for a single forest stand or for New Zealand as a whole. Each surface (or layer of information) can be turned on or off to explore relationships between data (for example, mean air temperature and wood density).
Bioinformatics is the science of managing and mining genomic data sets.
Remote sensing techniques allow us to acquire data from a distance. Some of the options we are using include:
- Satellite data
The challenge is how to make best use of these new data to augment or replace existing forest resource assessment procedures and improve efficiency of forest valuation. Some of the uses we are exploring include:
- Aerial imaging of cutover areas, with the potential for automated delineation and mapping of harvested areas
- High resolution imagery to assess survival rate following planting
- Use of image-based point clouds to derive canopy heights and terrain models as a low cost alternative to LiDAR
Unmanned aerial vehicles
Scion has invested in advanced unmanned aerial vehicles (UAVs), which can be used to collect LiDAR data, multi-spectral imagery and hi-definition video.
We are now developing the software and systems to translate these data into useful information for forest managers. Our focus is on identifying technologies that can add value to the forest industry, and on developing easy-to-use and robust algorithms and tools for forest managers.
- Comparisons between data from lower cost image-based point clouds and higher cost LiDAR point clouds
- Assessing the viability of UAV platforms to collect LiDAR / imagery over discontinuous forest resources not suited to conventional fixed-wing campaigns
- Monitoring contractor performance
- Monitoring and documenting environmental impacts and performance of harvest operations
- Loss assessment and planning for value recovery after catastrophic weather events
Our investment in equipment and research presents an opportunity for industry partners to work with Scion in identifying, testing, and evaluating these technologies.
ContactRobin Hartley, Manager of UAV Operations, Geospatial Scientist, UAV Pilot email@example.com
Forecaster is a desktop forest growth and quality decision support system developed by Scion and Future Forests Research.
Forecaster predicts the impacts of site, silviculture and genetics on tree and branch growth and wood properties. It can be used to estimate wood value, internal rate of return and net present value. Forecaster incorporates traditional models and models from recent research to provide the most reliable results. Scion actively adds new models and updates existing models as new information becomes available.
Forest managers, planners and silvicultural planners can:
- Create scenarios that simulate stand growth and yield to determine the best outcomes.
- Schedule silvicultural activities such as pruning and thinning operations.
- Produce simulated log yield tables for valuation or crop typing considering the interaction of genetics, sites and management practices.
Scion’s software, modelling systems and decision support tools harness data from over 70 years of forest science and management research.
- Permanent Sample Plot (PSP) system: a comprehensive database containing over 40 years of growth data from 30,000 plots located in forests throughout New Zealand.
- Empirical growth and yield models for forest planning and forecasting purposes.
- Models showing the variation in productivity and its drivers across New Zealand.