1. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Use of airborne laser scanning (LIDAR) as a tool for forest measurement and monitoring: use and potential Steve Reutebuch Hans-Erik Andersen Bob McGaughey Demetrios Gatziolis Resource Monitoring & Assessment Program Vegetation Monitoring & Remote Sensing Team USDA Forest Service PNW Research Station

2. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station LIDAR—what is it?  Light detection and ranging (LIDAR) Uses laser light to measure distance Uses laser light to measure distance  Different detection approaches Time of flight Time of flight Phase difference Phase difference  Hundreds of applications  In natural resources, 3 LIDAR types are widely available

3. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Widely available LIDAR  Terrestrial laser scanning (TLS) Primarily used in engineering Primarily used in engineering Some use in forestry research scanning plots or individual trees and logs Some use in forestry research scanning plots or individual trees and logs

4. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Widely available LIDAR  NASA IceSAT satellite LIDAR Global- and continental-scale forest canopy height and biomass estimates Global- and continental-scale forest canopy height and biomass estimates  70 m diameter footprint  175 meters spacing Difficult to remove topographic effects on canopy heights Difficult to remove topographic effects on canopy heights Operational 2003-2009 Operational 2003-2009 IceSAT-2 launch 2016 ??? IceSAT-2 launch 2016 ???

5. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Widely available LIDAR  Airborne laser scanning (ALS) Routinely flown commercially over large areas Routinely flown commercially over large areas Large vendor pool Large vendor pool Mature mission specs & deliverables Mature mission specs & deliverables Mature software to process data Mature software to process data Many state and federal partners Many state and federal partners

6. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station ALS LIDAR data uses  Topographic mapping of bare earth surface—primary use Engineering Engineering Flood risk mapping Flood risk mapping Hydrologic modeling Hydrologic modeling Geologic mapping Geologic mapping Landslide mapping Landslide mapping  Infrastructure mapping—still developing  Vegetation measurement and mapping— still developing, with operational uses

7. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station National review of ALS LIDAR data needs  USGS National Digital Elevation Program: Enhanced Elevation Data Requirements Study Enhanced Elevation Data Requirements Study  Funded: USGS, FEMA, NRCS, NGA (DOD)  FY10-12: Conduct study  FY13: Initiate enhanced elevation data collection  Primary use: update bare earth surface models  USGS study recognizes many other uses  130,000 sq miles of data with ARRA funds

8. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station USGS recognized uses of LIDAR

9. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 2010 State LIDAR efforts  8 states have statewide LIDAR programs North Carolina, Louisiana, New Jersey, Maryland, Delaware, Pennsylvania, Ohio, Iowa North Carolina, Louisiana, New Jersey, Maryland, Delaware, Pennsylvania, Ohio, Iowa  8 states have program initiatives Florida, Texas, New York, Oregon, Washington, Minnesota, South Carolina, Mississippi Florida, Texas, New York, Oregon, Washington, Minnesota, South Carolina, Mississippi  Many more projects areas have been flown ~25% of the conterminous US already has LIDAR collected ~25% of the conterminous US already has LIDAR collected Unknown amount of private forest coverage Unknown amount of private forest coverage

10. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 2010 Oregon LIDAR Consortium

11. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 2010 Puget Sound LIDAR Consortium

12. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Not all LIDAR data are the same  Things that affect LIDAR data for forest measurements: Mission specs (pulse rate, scan pattern, flying height, airspeed, pulse diameter, etc.) Mission specs (pulse rate, scan pattern, flying height, airspeed, pulse diameter, etc.) Time of year (leaf-off, leaf-on, snow free, etc.) Time of year (leaf-off, leaf-on, snow free, etc.) LIDAR sensor and data processing LIDAR sensor and data processing Experience of LIDAR vendor Experience of LIDAR vendor

13. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Not all LIDAR data are the same Therefore, don’t expect to get same results when models from one LIDAR dataset are applied to other datasets, even in the same forest type!!!

14. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station LIDAR used in forest measurement  When only partial LIDAR coverage of an area is possible: Sampling within a multi-stage framework Sampling within a multi-stage framework  Statistical framework has been developed and tested by several researchers  PNW LIDAR trials in Alaska:  Hans Andersen, PI  Kenai Peninsula  Interior Alaska

15. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Remote sensing Field plots Wall-to-wall low- resolution coverage w/ LANDSAT TM, SPOT, etc. Subsampling with high res. LIDAR, aerial photos Measurements of trees, shrubs, moss, soils, down wood. Example: Multi-level sampling to support forest inventory in remote northern regions Subsampling high res. satellite imagery

16. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station PNW-RMA (Anchorage) is carrying out a project to test a multi-level approach for biomass estimation in the Tok (1,911 sq km) (1,911 sq km) Multi-level approach will use: Satellite imagery (Landsat, SPOT, PALSAR, Quickbird) Satellite imagery (Landsat, SPOT, PALSAR, Quickbird) 27 High-density LIDAR strip samples 27 High-density LIDAR strip samples Field plot data (80 plots) Field plot data (80 plots)

17. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station LIDAR used in forest measurement  When “wall-to-wall” LIDAR coverage is available 2 types of measurements can be made: 1.Forest layers computed solely from the LIDAR 2.Inventory layers predicted from regression models or imputation methods using LIDAR and well measured ground plots

18. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 1– Layers computed solely from the LIDAR point cloud—obvious ones  Canopy surface model  Bare earth model

19. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 3-ft bare earth model 3-ft canopy surface model 1:12,000 aerial photo

20. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Layers computed solely from the LIDAR point cloud—obvious ones  Bare earth model  Canopy surface model  Canopy height model (Canopy surface minus ground surface)

21. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 3-ft resolution canopy height model Buildings

22. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Layers computed solely from the LIDAR point cloud—obvious ones  Bare earth model  Canopy surface model  Canopy height model  Canopy cover model

23. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station % Canopy Cover (0.1 acre pixels)

24. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Layers computed solely from the LIDAR point cloud—obvious ones  Bare earth model  Canopy surface model  Canopy height model  Canopy cover model  Intensity image from 1 st returns

25. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station 1.5-ft resolution intensity image

26. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Layers computed solely from the LIDAR point cloud—not so obvious  Variance, standard deviation, skewness, kurtosis, etc. of the canopy  Mean, min, max, percentile heights of the canopy  Density of the canopy  Forest / non-forest mask

27. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Standard Deviation of Canopy Height

28. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station LIDAR used in forest measurement  When “wall-to-wall” coverage is available 2 types of measurements can be made: 1.Forest layers computed solely from the LIDAR 2.Inventory layers predicted from regression models or imputation methods using LIDAR and well measured ground plots

29. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station WARNINGS !!!  Can’t get species information from the LIDAR data In some cases, can get: In some cases, can get:  Deciduous vs non-deciduous  Live crowns vs dead crowns  Can’t get understory, down wood, etc.  Not all LIDAR is the same: Changes in LIDAR sensors, sensor settings, and flight parameters can change results Changes in LIDAR sensors, sensor settings, and flight parameters can change results

30. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station MORE WARNINGS !!!!!  Most difficult part of a LIDAR project is: Getting good ground plot data: 1.Matched with regards to geographic position to an accuracy ~ equal to the LIDAR horizontal accuracy (~+/- 1m) 2.Matched with regard to the primary element being measured—large enough to minimize plot edge effect, but small enough to characterize tree size differences within plots (~0.1 – 0.2 ac circular plot)

31. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station MORE WARNINGS !!!!! (cont.)  Most difficult part of a LIDAR project is: Getting good ground plot data: 3.Matched in time of measurement--generally within 1-2 yrs of LIDAR acquisition 4.Matched in what’s measured by the LIDAR and on the plot—all stems that make up a significant portion of the above ground canopy—generally down to a 7-10 cm DBH lower limit, including all species

32. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Examples of layers predicted from regression models  Sherman Pass Scenic Byway Colville National Forest Colville National Forest 100,000 acres flown in 2008 100,000 acres flown in 2008  74 1/10th acre plots used to develop LIDAR inventory regressions measured in 2008

33. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Sherman Pass LIDAR Project Forest cover minimum: 10ft ht & 2% cover in 66ft pixel Ground Plots

34. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Regression models Lorey’s BA-weighted Height ft [LHT_ft] = 21.4980 + [ElevP90] * 0.7242

35. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Regression models Lorey’s BA-weighted Height ft

36. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Regression models Live Basal Area sqft/ac [LBA_sqftac] = sqr ( -5.0579 + [ElevSD] * -0.4280 + [ElevP95] * 0.2307 + [PC1stRtsCC] * 0.1039) + 2.809

37. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Regression models Live Basal Area sqft/ac

38. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Red areas have LIDAR predictor values >+/-10% beyond the range of the ground plots Greater than +/- 10% beyond ground plot LIDAR Metrics

39. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Example ArcGIS Calculations  Any of the LIDAR layers can be used in GIS to calculate combinations of forest structure variables

40. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Live Basal Area > 200 sqft/ac

41. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Canopy Cover 80%+ and Height 100ft+

42. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Current limitations on using existing LIDAR data  No coordination within natural resource organizations at any level for: 1. LIDAR specifications necessary for forest measurements 2. Ground plot measurements when large, multi-agency LIDAR acquisitions occur Missed opportunity to leverage existing LIDAR Missed opportunity to leverage existing LIDAR

43. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Possible problems with use of FIA plots for LIDAR projects  Plots not georeferenced well enough  Not enough plots measured in area within 1-2 years of LIDAR acquisition  Plot layout not well designed for use with high-resolution remote sensing data

44. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station Future for LIDAR in forest measurement?  Faster, cheaper, better LIDAR data, but doesn’t solve ground plot problems  Multi-temporal LIDAR datasets for change analysis  Multispectral LIDAR for species classification  New satellite-based systems for sampling  Beyond LIDAR—other 3D sensors (IFSAR,etc.)

45. RMA Vegetation Monitoring and Remote Sensing Team USDA Forest Service PNW Research Station LIDAR software DEMO Thurs 2009 Savannah River DOE Site LIDAR Project