2025 Hybrid OLI and GMUG Meeting
April 15 - 16, 2025
Mark your calendars for the Operational Lidar Inventory (OLI) and Growth Model User’s Group (GMUG) 2025 two-day joint meeting. This event provides an opportunity for information exchange on the operational or near-operational usage of remote sensing technologies in forest inventory and mapping applications as well as all things growth modeling related.
The 2025 Hybrid OLI and GMUG Annual Meeting will be held on April 15th and 16th at the Water Resources Education Center in Vancouver WA, USA. Both days will facilitate in-person and virtual participation.
Location: Water Resources Education Center 4600 SE Columbia Way, Vancouver, WA 98661
If you have a topic that you would like to present at OLI please email Jacob Strunk, and if you would like to present at GMUG please contact Weikko Jaross.
Social Opportunities (2x): April 14th AND April 15th, 2025, 6 PM, 13 Coins, Vancouver, WA
2025 Operational Lidar Inventory (OLI) Workshop Agenda
2025 Growth Model Users Group Meeting Agenda
The Growth Model Users Group Meeting (GMUG) returns on April 16th 2025 at the Water Resources Education Center (View Page). A shared Zoom link will be sent to registered participants.
We are continuing the past format of relatively informal presentations and discussions about all things growth model and inventory modeling related. This year, more time is allocated to presenters so they can demonstrate a skill or technique for how they got the job done.
GMUG 2025 engages luminaries of growth modeling and trailblazers of inventory modeling to discuss the past, present, and future of growth modeling. Invited participants include:
Forest Projection System: FBRI Updates - Dan Opalach (Forest Biometrics Research Institute) Dan and the FBRI team have been busy with publishing and applied research projects with promising applications. Dan will review FBRI accomplishments in 2024 and important FPS developments in the works.
FVS – Mark Castle (Forest Vegetation Simulator Group, USFS) The FVS Group is working to integrate the new NSVB biomass equations in FVS. Mark will give us an update on the scope and timeline of this project for 2025.
Geospatial Estimation of Forest Relative Density. Emmerson Chivhenge (UofMaine) Forest tree-size density metrics such as stand density index (SDI), maximum SDI (SDImax) and relative density (RD) are estimated using Forest Inventory and Analysis (FIA) data and mapped leveraging on the TREEMAP 2016 raster.
Nonstandard Diameter Growth Calibration For ORGANON (FVS?) David Marshall
Forest Management with AI and Machine Learning. Jaslam Poolakkal (University of Idaho) This talk explores artificial intelligence for modeling stand density index maximums and site index for use in the Forest Vegetation Simulator (FVS).
Expiration Dates: Fact or Fiction? Extending the shelf-life of forest inventory. John Young (WY) Throughout the 20th century, several methods have been developed which use novel information to update yield projections during periods of non-measurement. Interestingly, their utilization in forest inventory has been limited. A canvas of available methodologies and an assessment of the value of information provides insight into effective applications. Can their incorporation extend the lifespan of forest inventories?
Modeling census level inventories – Bruce Ripley (University of Idaho) The adoption of Individual tree detection (ITD) inventories presents new challenges with inventory management. Bruce will be presenting on his many insights gained through modeling very large tree lists with the Forest Vegetation Simulator (FVS).
Open Discussion – Data Processing, Q&A (Weikko Jaross Moderator)
15 Minute Break
GitHub Utilities – Greg Johnson (GJ Biometrics LLC) Greg will provide a sneak peek into his stash of shared utilities, and touch on his updates to the FVS Acadian variant.
CIPSANON Updates– Doug Mainwaring (Center for Intensive Planted-forest Silviculture) Doug and crew have been busy in the field and lab this year. Doug will brief us on updates and the future for CIPSANON.