Application of airborne LiDAR in forestry in North America and Scandinavia. ForestrySA LiDAR initiatives

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1 Application of airborne LiDAR in forestry in North America and Scandinavia ForestrySA LiDAR initiatives Jan Rombouts Airborne LiDAR is a great data source for forestry Transect through LiDAR data set, Mt Bold plantation, SA

2 This talk 1. Airborne LiDAR in forestry in North America and Scandinavia 2. LiDAR initiatives in ForestrySA Airborne LiDAR in North America and Scandinavia Study tour made possible thanks to J.W. Gottstein Memorial Trust Fellowship The Gottstein Trust has created Fellowship and Award programs to enable people working in the forest products industries to acquire knowledge and skills, in order to benefit individuals, their employers and the industry as a whole. Three types of funding programs: Gottstein Fellowship World Forest Institute Fellowship Gottstein Skill Advancement Award

3 Itinerary USA, Canada, Norway, Sweden, Finland Companies pioneering operational use of LiDAR in forestry Universities and Research Institutes involved in LiDAR Research and Development Download report from Ontario, Canada: Enhanced Forest Resource Inventory Collaboration: Ontario Ministry of Natural Resources Tembec Inc (12 million ha license holder) Queens University, University of Québec Using low density LiDAR data and multi-spectral aerial imagery Completed 400,000 ha The full suite of applications: Operations Road and bridge engineering Hydrology and drainage, environmental management Harvesting planning Resource inventory Stand delineation & species identification Stand assessment (stocking, stand height, BA, volume and biomass) Cost savings: 1$ cubic metre harvested, pay back in 2 years (Ken Durst)

4 Source: Valerie Thomas Laser forest inventory has taken off in Norway Laser inventory acquired 10% market share country wide, and increasing Contributing factors Ownership patterns and role of government Forest Planning is outsourced to specialised service companies History of using photogrammetry in forest inventory Strong links between University and industry Prof Erik Næsset Prevista (accounts for 40% of planning services market): Laser inventory since in 2 contracts has laser component targeting most productive forest Product: Stand delineation, species mix, stand parameters N, BA, H, V Higher price ($12-16 vs $8-12 per hectare) of laser inventory is accepted because information is more precise and versatile

5 V=82 m 3 /ha Source: Eric Næsset, Finland: next generation forest planning systems Ministry of Agriculture and Forestry is responsible for regional data collection and management planning of 13.6 million ha of private nonindustrial forest Inventory of 1 million ha per year Cost management plan $30 per ha (field inventory $14 per ha) Forest Planning Strategy ( ): next generation planning system cost effective inventory consider LiDAR and aerial imagery Research and development program involving: Universities (Helsinki and Joensuu) TAPIO (Forestry Development Centre, MAOF) 13 Forest Centres, MAOF

6 Source: Juho Heikkilä, TAPIO Evaluation Reduced cost ($16-19/ha vs $30/ha, including management plan) Field measurement time reduced by 75-80% More accurate and up-to-date resource data Decision: implementation from 2010 onwards

7 Service companies develop remote sensing based forest planning capabilities Examples: Prevista, Norway (see above) FORAN Remote Sensing (Sweden) Arbonaut Oy (Finland) Lim Geomatics (Canada) Significant in-house development of IP and data processing systems Services range from advisory/data processing to all-inclusive Makes LiDAR based solutions accessible to forest industry Source: Tobias Jonmeister, FORAN

8 Research and Development Intensive research activity worldwide Focus on improvement (more accurate, cheaper) Optimising LiDAR data collection parameters Optimising field data collection Extracting information at the tree level Detecting suppressed trees Species recognition Crown measurement Focus on developing new capabilities Extracting product information Environmentally focused applications Large area inventory (strip sampling) Extracting product information - diameter distributions Finland, 100% spruce, CF operation, high density LiDAR data (6.4 strikes/m 2 ) Source: Peuhkurinen et al 2007: Forest Science 53 (6)

9 Source: Paul Treitz, Summary: LiDAR applications In operational use Operations (DTM) Road engineering Drainage harvesting Resource assessment Stand delineation Species composition stand N, BA, height, volume (spatially explicit) R&D and pre-operational Operations: Fire modelling Forest health monitoring Resource assessment Diameter distributions Individual tree assessments (species, height, crown) Large area inventory Biomass, carbon LAI Structural diversity Log product quality

10 Relevance to Australia? Substantial body of Australian research (especially in native forest) showing comparable levels of success in comparable applications Many pre-operational trials: Forests NSW, DSE, FT, ForestrySA, Few examples of operational uptake of the technology (eg Forest Plantations Queensland) Challenges and constraints Importance of product and quality information in Australian/NZ forest inventory LiDAR capabilities have not been conclusively proven Need for Australian research Is technically harder and more data intensive Availability of expertise and specialised tools As yet Australia does not have specialised service companies. Research effort is fragmented Software is not commercially available Radical change

11 Costs to be considered LiDAR data: cost is function of project area compactness of area of interest required data density System development Cost ($/ha) SA 2006 SA 2007 Overseas Collecting field data for calibration 0 1,000 10, ,000 1,000,000 10,000,000 Project area - log scale (ha) Data processing Developing expertise Improving the cost/benefit ratio Optimising LiDAR data capturing parameters Optimising field data collection Considering all possible applications in the business Coordinate data capture with third parties Considering down-stream benefits accruing from precision gains

12 ForestrySA is pursuing LiDAR Circumstances are ideal for LiDAR: Even-aged, coniferous forest, little undergrowth and topography Potential for multiple applications Resource scarcity increases value of yield regulation Benefits we are seeking: Cost savings Precision gains and spatially explicit information Reduced labour requirements Occupational benefits Strategy H a r d E a s y Operational Building DTM Site productivity assessment R&D Fertiliser decision support Hydrological DTM applications Inventory: volume by size class by quality Inventory: volume by size class Now Later

13 Site Quality (Site Productivity) mapping Site Quality = productive capacity of a forest site for radiata pine. Criterion: volume production (to 10 cm SED, under bark) at age 10. Applications Growth and yield prediction. Inventory stratification Silviculture: thinning and fertiliser regimes. Monitoring productive capacity (sustainability). Total Vol (m 3 /ha to 10 cm SED) SE Region Yield Table - (Leech 1978) Age (YEARS) I II III IV V VI VII Basically a volume mapping problem -> LiDAR! Predicting stand volume based on LiDAR variables 2002 campaign, 1 site, 1 plantation 2006 campaign, 3 sites, 6 plantations 2007 campaign, 5 sites, 7 plantations R 2 = R 2 = R 2 = 0.95 V10 (m 3 /ha) V10 (m3/ha) V10 (m3/ha) mean quadratic LiDAR height (m 2 ) mean quadratic LiDAR height (m 2 ) mean quadratic LiDAR height (m 2 ) Results for 3 LiDAR campaigns, 9 sites, 14 plantations aged 7-11, 5 soil groups, thinned and unthinned Tight, consistent, simple relationship

14 Using the Norwegian approach (with some variations) -> Site Quality maps LiDAR SQ map Conventional SQ map Field Plots Create any number of Site Quality classes or think Site Quality surface LiDAR SQ map Conventional SQ map

15 If growth < =10 m 3 /ha fertilise precision forestry Next Awaiting delivery of Digital Terrain Model for Green Triangle Going operational with Site Quality assessment Research into pre-harvesting inventory

16 Thank you