Mapping Forest Vegetation Structure in the National Capital Region using LiDAR Data and Analysis Geoff Sanders, Data Manager Mark Lehman, GIS Specialist National Capital Region Network 2010 Resource Information Management Meeting Ft. Collins, CO

Background Why did we decide to do this? Interested in doing this for a while Opportunity for cost sharing Opportunity to leverage large amounts of existing data Opportunity to compare vegetation structure between two parks, one with high deer density (CATO), one with low deer density (PRWI). Where are we doing this? Prince William Forest Park, Prince William County, VA (Completed in 2009) Catoctin Mountain Park, Frederick/Washington County, MD (Planned for 2009 – delayed until 2010) Harpers Ferry National Historical Park (Planned for 2010) What questions are we trying to answer? Canopy Density Canopy cover to canopy gap ratio. Tree/canopy heights Understory cover Carbon content Biomass

Catoctin Mountain Park (CATO) Harpers Ferry National Historical Park (HAFE) Prince William Forest Park (PRWI)

Sensor Types: Discrete vs Waveform Waveform Sensors Larger footprints, typically greater than 1 meter. Record the total amount of energy returned to the sensor. Sensor identifies the peaks of varying intensities and records the elapsed time to determine the height of the surface. Criterion for determining multiple returns in based on the intensity of the signal returned to the sensor. Advantage: Ideal for characterizing canopy structure over large areas. Collects more information on canopy structure than discrete sensors. Downside: Not as common as discrete sensors. Coarser resolution – not as good at creating high resolution topographic maps.

Sensor Types: Discrete vs Waveform Discrete Sensors High spatial resolution due to small sensor footprint : 15 – 80 cm footprints Record one to five signal returns per laser pulse Sensor identifies peaks that represent major objects in the path of the laser. Distance determined by the elapsed time before the leading edge of the peaks. Large data sets: typical forest can produce a layer with more than 600,000 pts./m2. Ideal for mapping ground and canopy topography. Advantage: High spatial resolution allows for the aggregation of data to characterize specific locations like forest monitoring plots or individual trees. Downside: may underestimate canopy height.

Multiple Return Discrete Sensor 15 cm

Planning Determine what type of sensor is most appropriate for your project. Waveform Discrete (multi-return) If a you choose to go with a discrete sensor - determine the appropriate post- spacing or point density Point density is dependent upon: Speed of the aircraft Elevation of the aircraft Transmission rate of the sensor Scan angle Be clear about the area of interest. Know the areas around your area of interest. May need to be involved with permitting or negotiating access to restricted air space.

LiDAR Sensor Information Discrete sensor Near- IR laser 60K pulses/second Measures UP TO four returns per pulse. Standard point density was one meter postings We used half – meter post density to try and maximize returns from ground and understory. This translates to UP TO 16 returns/m2 For future work we may increase point density GPS Surveys RTK GPS base stations are established on the ground within the survey area to help correct for atmospheric effects and improves the positional accuracy of the data.

Products Raw Products – 8 data layers Four layers of LiDAR returns Four layers of LiDAR intensity for each return layer  corresponds to waveform amplitude  could be used to differentiate tree species. Derived Products Bare Earth DEM (~2m resolution)  Interpolated using inverse distance weighted kriging algorithm Canopy height  Difference between the first return (top of canopy) and bare earth return. Understory layer  Defined using the presence and intensity of LiDAR returns within 2m of surface.  Field data will validate the presence of understory and help optimize threshold value. Canopy to canopy gap ratio  Gap thresholds need to be defined and tested.  Field data will validate the presence of gaps identified by the LiDAR data. High resolution (sub-meter) aerial photographs.

Sub-meter Near IR Aerial Photos

DEM Canopy HeightCanopy Gaps

4 th ReturnGround Return 3 rd Return2 nd Return1 st Return LiDAR Returns Reveal Forest Canopy Structure

Incorporating Monitoring Data

Raw Return Points One vegetation monitoring plot (30 m diameter) - ~ 5900 pts (returns). NCRN has over 130 vegetation monitoring plots in PRWI. Majority of the pts. (returns) were 1 st returns and in this case most came from the canopy. Returns diminish as the intensity of the pulse drops. Returns 1 st – 3,599 2 nd – 1,843 3 rd – th – 32 Important to remember: You will not always get 4 returns. Your first return will not always be the canopy. Your last return (or 4 th ) will not always be the ground.

Future Work Capture LiDAR data for CATO and HAFE during the Summer of 2010 Independent field work will be conducted during the Summer of 2010 to accompany NCRN forest monitoring data to help validate canopy gap and understory canopy data products. NRPP Climate Change funded project Investigating the vulnerability of NPS resources along the tidal Potomac and Anacostia Rivers to sea-level rise. Existing LiDAR data will be used to create high resolution DEMs to inform geospatial models (e.g. SLAMM). Other Projects

Acknowledgements Andrew Emore – University of Maryland – Center for Environmental Science NCRN Staff Prince William Forest Park Staff Catoctin Mountain Park Staff Canaan Valley Institute

Evans, J.S. et al Discrete Return Lidar in Natural Resources: Recommendations for Project Planning, Data Processing, and Deliverables. Remote Sensing Vol. 1: Lefsky, M.A Lidar Remote Sensing for Ecosystems Studies. BioScience. Vol. 52 (1). References