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LiDAR Remote Sensing of Forest Vegetation

LiDAR Remote Sensing of Forest Vegetation. Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota. 1 ns = 0.15 m. Light Detection and Ranging (LiDAR). Source: TopScan, Germany. Airborne LiDAR. Ground Surface elevations (30 cm vertical, 1 m horizontal accuracy)

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LiDAR Remote Sensing of Forest Vegetation

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  1. LiDAR Remote Sensing of Forest Vegetation Ryan Anderson, Bruce Cook, and Paul Bolstad University of Minnesota

  2. 1 ns = 0.15 m Light Detection and Ranging (LiDAR)

  3. Source: TopScan, Germany Airborne LiDAR • Ground Surface elevations (30 cm vertical, 1 m horizontal accuracy) • Wetland delineation. • Interpolation of water table heights. • Vegetation height and density (i.e., structure) • Improved landcover classification (fusion with imagery). • Spatial estimates of biomass, canopy height, basal area, LAI ,etc (does not saturate!) • Input variable for other models

  4. Elevation and Vegetation Height Bare Earth Elevation (m) Vegetation Height (m) Leaf-On data 2.3 million pulses (15% ground hits) Median height = 5.2 m

  5. East-West cross-section Upland-Wetland Catena Coniferous Wetland North-South cross-section Mixed Forest Hwy 182 Clearcut Grass Shrub Wetland Landscape Profiles Deciduous Upland

  6. Stand Structure First returns for 30 x 30m plots Mixed Upland Alder-Cedar Wetland Height Coniferous Wetland Frequency

  7. 1 km Bare Earth Elevation • “Leaf off” collection • Spring 2006 • 1st and last returns • “Leaf on” collection • Summer 2005 • 1st return only Approx. 1.5 pulse m-2 1 m nearest neighbor interpolation Ground control points (n=34): 100% of QA/QC points ± 15 cm Image difference (n=46 million): 90% of 2005/06 pixels ± 60 cm

  8. LiDAR Methods…Flying is the easy part! • Collect vertical ground control points • Collect field observations for variables of interest (FIA–style plots) • Acquire LiDAR and fine resolution multi-spectral imagery (Quickbird) • Triangulate ‘ground hits’ and compute base height of ‘feature hits’ • Use digital terrain model to extract features • Compute feature heights • Combine feature heights with return intensity, multi-temporal/spectral imagery, and DEM to classify landcover • Extract pulses associated with field plots and compute LiDAR variables (density and height for biomass, GPP/NPP; gap fraction for LAI/fPAR) • Develop relationships between LiDAR and plot variables • Apply relationships to entire scene • Use spatial variable to drive growth models (e.g., MODIS GPP/NPP algorithm)

  9. Training Plots • FIA-style plot design • 76 Upland plots (~30 located precisely enough to be useful for LiDAR analysis) • Wetland plots to be taken this field season • Height and growth in central subplot • Biomass calculated by species specific allometric equations [Biomass] = a [DBH]b • Productivity calculated by inferring past diameters from cored trees

  10. Training Plot Locations

  11. LiDAR Variables Extracted for Each Plot

  12. Stepwise Multiple Regression Analysis Full Model 1: Full Model 2: Where Y is the plot-measured variable of interest (biomass, height, productivity, etc)

  13. Canopy Height

  14. Biomass

  15. Productivity

  16. This Field Season

  17. This Field Season

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