ForestView® - University of Idaho Example
Collaborating Partners
Since 2004, Northwest Management Inc (NMI) has worked collaboratively with University of Idaho (U of I), and since 2019 the U of I Experimental Forest (UIEF) staff, to increase the research and applications of technology across the forestry supply chain. From the development of student curriculum, repeated field verification datasets, and the processing of multiple Digital Inventories that support biometrics research, to supporting publications in the areas of G&Y, fire effects, harvest planning and user validations, this collaborative team is excited about the future.
Northwest Management Inc’s role in this team is collection, QC and processing of LiDAR datasets into GIS and SQL compatible Digital Inventory® products covering forest health, forest inventory, silviculture, fire risk, avoided cost, harvest operations, wildlife, and more across the UIEF. All LiDAR datasets are verified through the collection of georeferenced field data and in 2022, submeter 4-band imagery. NMI is excited to support this collaborative effort and how the Digital Inventory® products are being applied by research faculty, graduate students, and the UIEF managers in their efforts to further our understanding of LiDAR uses and accuracies within forest inventory and operational forestry.
The University of Idaho Experimental Forest
The UIEF is a mixed-conifer temperate forest representing many of the merchantable conifer species commercially available throughout the inland west. The forest has a diverse range of stand structures resulting from years of multi-use management for timber, research, and education purposes. Currently, there are multiple Digital Inventory® vintages across the UIEF beginning in 2019. The following are examples from within the highlighted area of the West Hatter Creek watershed.
High-Resolution Lidar scan (20ppm) in the West Hatter Creek unit of the UIEF
ForestView® georeferenced individual trees colored by tree-height from tallest down to 6-feet
ForestView® derived individual tree gross volume totals by timber stand polygon across the landscape.
ForestView® “matched” individual-tree height-change measurements following multiple lidar flights, 2 growing seasons apart in time.
ForestView® 10-meter site index/productivity map aggregated from individual-tree height-change measurements.
This next image demonstrated a comparison of the variance and error differences between population measurements with Lidar and sampling from traditional methods.
Publications
February 15, 2022
December 21, 2021
July 20, 2022
Aaron M. Sparks, Mark V. Corrao, and Alistair M. S. Smith
May 27, 2022
April 6, 2022
2020 Lidar Inventory Canopy Height Model
The Digital Inventory® can be accessed through the ForestView® portal and custom selections of trees or stands can be made by a user.
Use the Slider to see before and after images
Species Distribution
This is the 2021 Digital Inventory® for the UIEF with single-trees colored by height. It is an interactive layer laid over-top the canopy height model (CHM). Several summary statistics are displayed automatically for each selection of Height, DBH, BA, and Volume.
Use the Slider to see before harvest and after harvest images of forest change
Species Distribution
Gross Board FT by Species
ArcOnline 3D viewer format
The UIEF has also been published into an ArcOnline 3D viewer format where the positions and single-tree metrics for the entire forest can be interacted with on a base map of Google Earth Imagery. Multiple copies of the digital forest can be uploaded in this viewer and used to simulate silvicultural prescriptions or post fire mortality by removing trees within one copy to visualize either “harvested” or “burned” conditions.
Measures Change Over Time
The following images are from the same location on the UIEF. The middle images are of the 2020 Lidar while the righthand images are of the same location in the 2021 Lidar. The upper Left image displays the height change in meters for the trees remaining after two harvest operations as expressed growth of the un-harvested trees.
Individual Tree Change and Growth Rate Detection
Height Change (meters)
UofI CHM 2020 Lidar
UofI CHM 2021 Lidar
Logging Path Detection from Multi-year Lidar
Height Change (meters)
UofI DEM 2020 Lidar
UofI DEM 2021 Lidar
These additional images describe the near-surface detectable changes between the 2020 and 2021 Lidar acquisitions. The first image on the left displays near-surface changes in elevation that coincide with harvest operations and equipment pathways.