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

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. Moskal @ PFC since 2006 (Moskal Directorship since 2012) Current biennium 1:10 return on investment (+$5 million); 75% out of state $

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.

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.

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.

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

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.

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

Plot Location Review - Keep

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

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

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)

Spatial Pattern in Active Frequent-Fire Forests 13

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

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

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

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

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

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 16-32 m trees; avoid areas dominated by 2-8 m trees Habitat stops being distinct from surrounding landscape ~600 m from nest sites

Higher class number -> dominance by taller trees

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

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

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