Considerations for Planning, Acquiring, and Processing LIDAR Data for Forestry Applications University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Robert J. McGaughey USDA Forest Service--PNW Research Station Hans-Erik Andersen University of Washington-Precision Forestry Cooperative Stephen E. Reutebuch USDA Forest Service--PNW Research Station

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Outline  Brief LIDAR overview  LIDAR data characteristics & deliverables  QA/QC procedures  Data processing  Introduction of FUSION software  Conclusions

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team LIDAR  Light Detection and Ranging  4 types Atmospheric Atmospheric Continuous waveform Continuous waveform Discrete return (profiling) Discrete return (profiling) Discrete return (scanning) Discrete return (scanning)  Airborne Laser Scanning (ALS) Discrete return (scanning) mounted on aircraft Discrete return (scanning) mounted on aircraft

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team ALS System Components  Scanning laser emitter-receiver unit  Differentially- corrected GPS  Inertial measurement unit (IMU)  Computer to control the system monitor mission progress  Interesting targets

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Multiple Returns  Many laser systems can record several returns for each pulse  Multiple returns occur when the laser beam is only partially blocked Part of the laser energy is reflected back to the sensor Part of the laser energy is reflected back to the sensor The remaining laser energy continues downward The remaining laser energy continues downward  Up to 5 returns per pulse Typically only 2-3 returns Typically only 2-3 returns  Many systems record the amount of energy reflected by target objects Intensity (near-infrared, 1064 nm) Intensity (near-infrared, 1064 nm)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Multiple Returns All returns (16,664 pulses) 1 st returns 2 nd returns (4,385 pulses, 26%) 3 rd returns (736 pulses, 4%) 4 th returns (83 pulses, <1%)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Comparison With Other Remote Sensing Technologies  Active sensor Laser pulse is emitted and reflected energy is measured Laser pulse is emitted and reflected energy is measured Passive systems rely on reflected solar energy Passive systems rely on reflected solar energy  Returns are actual measurements Range is computed based on round-trip travel time for laser energy Range is computed based on round-trip travel time for laser energy Combined with accurate aircraft position and attitude to produce XYZ point measurement Combined with accurate aircraft position and attitude to produce XYZ point measurement  Small footprint at target cm footprint at ground surface cm footprint at ground surface 4+ pulses/m 2 is common 4+ pulses/m 2 is common Multiple returns over porous targets Multiple returns over porous targets

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team LIDAR Accuracy  RMSE provided by LIDAR system manufacturers 10-15cm vertical 10-15cm vertical cm horizontal cm horizontal  Several studies provide independent verification of these values Ground survey points Ground survey points Tree heights Tree heights Building heights Building heights Alignment of power transmission lines Alignment of power transmission lines

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team LIDAR DEM  Mean LIDAR DEM error 22 cm…9-inch field boot height! 22 cm…9-inch field boot height!  Maximum errors: +1.3 meter, meter…(+4.3 ft, -2.1 ft) +1.3 meter, meter…(+4.3 ft, -2.1 ft)  Error is not significantly affected by canopy density Bare-Earth Accuracy

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Tree Height Accuracy  Recent study uses total station to accurately measure tree heights in relatively open forest  High density LIDAR (4+ pulses/m 2 ) Narrow beam (0.3 mrad) Mean ± Std. Dev. Wide beam (0.8 mrad) Mean ± Std. Dev. Douglas-fir ± 0.41 m ± 0.56 Ponderosa Pine ± 0.13 m ± 0.24 Overall ± 0.43 m ± 0.56 m

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team LIDAR Data Characteristics  High spatial resolution Typical density is pulses/m 2 Typical density is pulses/m returns/pulse in forest areas 2-3 returns/pulse in forest areas Surface/canopy models typically 1-5m grid Surface/canopy models typically 1-5m grid  Large volume of data 5,000-60,000 pulses/hectare 5,000-60,000 pulses/hectare 12, ,000 returns/hectare 12, ,000 returns/hectare Gigabyte/hectare Gigabyte/hectare

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team “Typical” Mission Specifications Topographic mapping Vegetation mapping Scan angle ±20 ° ±12 ° Flying height 2200 m 1200 m Pulse rate kHz kHz Beam footprint 66 cm 36 cm Swath width 1600 m 510 m Pulse spacing 1-3 m m

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team LIDAR Contracting  Area selection and specification  Data specifications  Deliverables  Data acquisition and delivery  QA/QC Assessment  Data processing  Final products

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Deliverables: General Considerations  Delivery format Return data, surfaces, images, GIS coverages Return data, surfaces, images, GIS coverages Can you read the format? Can you read the format? Does the data contain all the information you need? Does the data contain all the information you need?  Delivery media External hard drives are common but not easy to backup to more stable media External hard drives are common but not easy to backup to more stable media  You would like to be able to retrieve single data files from your backup What do you do with multiple DVDs? What do you do with multiple DVDs?  How do you know what has been delivered? Contracts need to include specific deliverables to help you assess overall data quality and completeness Contracts need to include specific deliverables to help you assess overall data quality and completeness Deliverables based on the actual data…not just coverage area boundaries Deliverables based on the actual data…not just coverage area boundaries  Do you have adequate storage space to move data onto faster devices? Disk space Disk space Bandwidth Bandwidth

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Deliverables: Common Data Products  Metadata Flight information Flight information LIDAR system settings LIDAR system settings Data coverage Data coverage  Bare ground products Bare-ground returns Bare-ground returns Surface models Surface models  Return data Coarse filtering to remove outliers Coarse filtering to remove outliers Includes return number Includes return number  Always get the return data for a project Adds very little to contract cost Adds very little to contract cost Will cost you $$ if you decide you want it later Will cost you $$ if you decide you want it later

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Deliverables: Forestry-Specific Products  Canopy height models Normalized using bare-ground surface Normalized using bare-ground surface Filtered to remove buildings, powerlines, and other above-ground features Filtered to remove buildings, powerlines, and other above-ground features  Canopy cover maps Presence/absence of vegetation Presence/absence of vegetation Vegetation density (percent cover) Vegetation density (percent cover)  Geo-referenced LIDAR intensity images First return intensity value (reflected energy) First return intensity value (reflected energy) Useful an “image” (B/W IR image) Useful an “image” (B/W IR image) Useful as a layer for further analysis Useful as a layer for further analysis

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team QA/QC Assessment: Did you get what you ordered?  Initial delivery: Missing data Missing data Missing returns Missing returns Misclassified returns Misclassified returns Tile naming inconsistencies Tile naming inconsistencies  Contractor used custom software to produce ASCII formatted data Several flight lines were omitted Several flight lines were omitted They had no way to view the ASCII files They had no way to view the ASCII files They didn’t know what they delivered They didn’t know what they delivered  Over 60 client-hours to sort out the problems  Contractor made 3 deliveries over a 5- week period

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team QA/QC Assessment: Quality and Completeness Total project area 47,818 ha (118, 111 acres) Total returns 2.9 billion Total file storage space (LAS binary format) 77 Gb Data tiles (processing bins) 277 Tile size 1 km wide by 2 km tall (200 ha) HTML report generated by FUSION-Catalog HTML report generated by FUSION-Catalog

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Data Processing: What do I do with all these points? Raw data are interesting to look at but require extensive processing to create useful information

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Data Processing: Bare-Earth Surface Model

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Data Processing: Canopy Surface Model

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Final Products: Canopy Height (Raw Data)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Final Products: Percent Cover (2.5m grid)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Final Products: LIDAR Intensity Image (1.25m grid)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Final Products: LIDAR Intensity Image (5m grid) 1m grid

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Coniferous/Deciduous Classification Using Intensity Values (Raw Data)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team FUSION Software Sample options Shape & sizeShape & size Coloring rulesColoring rules Snap to POISnap to POI LDVDataviewer Color LightingGlyph Motion Measurement FUSIONDatainterface Image LIDAR data Points of interest (POI) Bare-earth model Hotspots Canopy model Tree data PDQ Simple Data viewer Command line processing and utility programs

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team FUSION Software  Displays several kinds of data  Allows users to interactively select portions of large datasets for viewing Users can “mine” the data to discover new information Users can “mine” the data to discover new information  Clips all data layers and makes them available for detailed 3D viewing  Supports stereoscopic viewing  Runs on a current hardware  Available through RSAC…included on DVD you were given at registration…demos on Thursday

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Main System components  FUSION – 2D interface to several data types Allows extraction of data subsets Allows extraction of data subsets Interacts with LDV to display samples Interacts with LDV to display samples Includes tools to develop images using LIDAR point cloud (colored by elevation or intensity) Includes tools to develop images using LIDAR point cloud (colored by elevation or intensity)  LDV – 3D data visualization Very interactive Very interactive Provides a variety of display options Provides a variety of display options Allows direct measurement in data Allows direct measurement in data Provides structured measurement protocol for measuring tree attributes Provides structured measurement protocol for measuring tree attributes Provides analysis framework for prototyping analysis strategies Provides analysis framework for prototyping analysis strategies

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Command Line Components  Catalog Creates QA/QC summary for project area Creates QA/QC summary for project area Intensity image for project area Intensity image for project area Outlier analysis Outlier analysis  GroundFilter Filters all-return data to obtain bare-ground returns Filters all-return data to obtain bare-ground returns GridSurfaceCreate interpolates regular grid using bare-ground points GridSurfaceCreate interpolates regular grid using bare-ground points  VegetationMask (in progress) Identifies areas with vegetation cover Identifies areas with vegetation cover Identifies and masks buildings, powerlines, and most man-made, above-ground features Identifies and masks buildings, powerlines, and most man-made, above-ground features Provides mask for all vegetation analysis Provides mask for all vegetation analysis  CanopyModel Creates canopy surface or vegetation height models Creates canopy surface or vegetation height models  PercentCover Computes canopy cover estimates Computes canopy cover estimates Computes vertical structure indices (in progress) Computes vertical structure indices (in progress)

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Command Line Components  ClipData Extracts subsets of return data Extracts subsets of return data Usually used to clip individual tree- or plot- level samples Usually used to clip individual tree- or plot- level samples  CloudMetrics Computes statistical metrics for data subsets Computes statistical metrics for data subsets Provides basis for plot-level regression with field plot data Provides basis for plot-level regression with field plot data

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Conclusions  LIDAR can help foresters characterize spatial variability in forest conditions at resolutions beyond our wildest dreams  Cost is decreasing while LIDAR system capabilities are increasing  Analysis procedures are being defined and refined

University of Washington-Precision Forestry Cooperative Pacific Northwest Research Station-Silviculture and Forest Models Team Conclusions  The number of commercial LIDAR providers is increasing More competition, more work, more interest in uses other than bare-earth modeling More competition, more work, more interest in uses other than bare-earth modeling  Large LIDAR acquisitions are underway Foresters and other resource specialists need to be “at the table” when decisions regarding data specifications are made Foresters and other resource specialists need to be “at the table” when decisions regarding data specifications are made Raw return data is always valuable even if the analysis tools and methods are not fully mature Raw return data is always valuable even if the analysis tools and methods are not fully mature  FUSION will be demonstrated Thursday