1. Precision Forestry Cooperative Lidar Projects
Van R. Kane, Monika Moskal
Precision Forestry Cooperative
University of Washington

2. Our Mission: to develop advanced technologies to improve the quality & reliability of information needed for planning, implementation, & monitoring of natural resource management, to ensure sustainable forest management.
1999 WA state funded Advance Technology Initiative (ATI) $500K/bi-biennium (includes 2 FTEs)
L. M. PFC since 2006 (Moskal Directorship since 2012)
Current biennium 1:10 return on investment (+$5 million); 75% out of state $

3. Operational Research Laboratory – S. Toth
Develop quantitative decision support tools to aid forest and natural resource management
Forest management planning: spatially explicit harvest scheduling models, multiple-criteria forest planning.
Operations research: integer programming, multiple- criteria optimization, multiple-criteria decision support systems.
The economics of non-timber forest benefits.

4. Natural Resources Spatial Informatics Group – L. Rogers
Provide technologies and expertise for analyzing forestry and agricultural issues, specializing in large spatial scales and big data.
Enable landscape, state, and regional scale analyses while simultaneously using the highest resolution data sets available.
Focus on applied problems that integrate environmental, social, and economic objectives to consider the sustainability, acceptability, and productivity of management opportunities.

5. Remote Sensing and Geospatial Analysis Laboratory – LM Moskal
To provide a research rich environment and exceptional resources that drive the understanding of multiscale dynamics of landscape change through the innovative application of remote sensing & geospatial tools.
Projects use airborne lidar, UAVs, structure from motion, photo interpretation, hyperspectral, Landsat, etc.

6. Goals and Objectives
Provide background information to serve as a basis for potentially developing a Washington State riparian forests status and trends monitoring protocol based on remote sensing methods.
Specific objectives of the pilot project included:
Develop a field data collection protocol
Use direct and modeled remote sensing methods for assessing 13 riparian indicator metrics
Provide a per indicator analysis and feasibilities as well as costs and recommendations

7. Project Location Eatonville
The starting point for the project was the DNR Hydro layer. It is the official regulatory dataset, and the Fish/Non-fish classification determines buffer sizes.
Several versions of buffers were developed, but it was decided in conversation with RSAG, that Type F and S streams would be buffered 225ft and Type N streams (all periodicity values) would be buffered 75ft for the first 1500ft of length from Type F and S streams. Type U streams would not be buffered.
The 75ft and 225ft buffer sizes correspond with the 75ft cell size at which the LIDAR was processed, which corresponds to (in terms of area) the 1/8th acre field plots.
All plots needed to be inside these buffers.

8. Landscape Stratification
Plots should sample all conditions in the riparian buffers
LIDAR can be used to pre- stratify the buffers into forest structure classes, or Bins
Height and cover were identified as the most important LIDAR variables in describing the forest structure in the Mashel
Closed
Stands
Increasing in Height
Edge Effects and Few Tall Trees
Variable Structure Stands
We want to put field plots throughout the range of forest conditions in the watershed. Pre-stratifying the landscape into structure classes, or Bins, using LIDAR has been shown to be useful for this.
LIDAR processing produced a set of nearly 200 different metrics for the riparian buffers. A statistical analysis was done to identify important metrics for describing forest structure in the riparian buffers.
Two were identified as the most significant: 80th percentile height, and percent cover (the percentage of first returns above 2m)
Young Stands
Before Crown Closure

9. Plot Location Review - Keep

10. LiDAR-based Models Basal Area Percent Cover Canopy Height
Crown Diameter Stem Diameter Deciduous Count
Density Large Woody Debris Volume Snag Count
Stream Channels Vegetation Class

11. Forest Resilience and Restoration at Multiple Scales – VR Kane
Resilience: Likely to persist through disturbance processes & climate change
Restoration: Select, plan, and monitor restoration projects
Multiple Scales: Tree clumps to project areas to landscapes

12. Tree Approximate Objects
Tree approximate object (TAO)
Identified overstory tree of any height plus 0 to several undetected subordinate trees (whose presence can be inferred from canopy cover in lower strata)

13. Spatial Pattern in Active Frequent-Fire Forests13

14. 2009 Big Meadow Yosemite % of TAO’s in Clumps of 1

15. Niche Overlap - % of TAO’s in Clumps of 1
Low Severity
Moderate Severity
High Severity
0.87
0.73
0.37
Compare recently burned areas to adjacent areas that have not experienced fire in 100+ years
0.88
0.81
0.51

16. Niche Overlap - % of TAOs in Clumps of 5 – 9
Low Severity
Moderate Severity
High Severity
0.91
0.84
0.38
Compare recently burned areas to adjacent areas that have not experienced fire in 100+ years
0.95
0.86
0.7

17. Lidar - Quantifying Canopy Cover Area
Our study maps & analyzes proportion of canopy area by height strata
We looked at many other measures, none as relevant

18. Niche Overlap
Calculated niche overlap for ever expanding annuli from nest site centers

19. CA spotted owls Niche Overlap by Distance
Owl habitat most distinct in area of cover in tall trees >32 m & especially >48 m; agnostic on cover in m trees; avoid areas dominated by 2-8 m trees
Habitat stops being distinct from surrounding landscape ~600 m from nest sites

20. Higher class number -> dominance by taller trees

21. Hexagon PhoDAR for Monitoring
Photogrammetric Digital Surface Models
Photogrammetric Detection and Ranging (PhoDAR)
Photogrammetric structure from motion
NAIP acquisition contractors concurrently collect nationwide:
Stereo imagery 40 cm resolution (Hexagon Imagery Program)
PhoDAR processing produces a lidar-like first return-like point cloud
Requires first lidar flight for ground model
Lidar
Hexagon
PhoDAR

22. North central Washington near Colville National Forest
2008 lidar
2015 NAIP
PhoDAR
North central Washington
near Colville National Forest
2009
2011
2013
2015

23. 2015 NAIP PhoDAR 2008 lidar Harvest with retention of tree clumps
Selective harvest of taller trees
Retention of larger trees;
PhoDAR shows shorter and narrower crowns; alternatively, PhoDAR misses some taller trees
Apparent harvest of taller trees and retention of shorter trees