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

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

Airborne LiDAR Source: TopScan, Germany Ground Surface elevations (30 cm vertical, 1 m horizontal accuracy)Ground Surface elevations (30 cm vertical, 1 m horizontal accuracy) –Wetland delineation. –Interpolation of water table heights. Vegetation height and density (i.e., structure)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

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

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

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

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

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)

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

Training Plot Locations

H1010 th percentile of feature heights within the subplot H5050 th percentile of feature heights within the subplot H9090 th percentile of feature heights within the subplot HmeanAverage feature height within the subplot HmaxMaximum feature height within the subplot HcvCoefficient of variation of all feature heights within the subplot D1The proportion of LiDAR canopy returns that were above the lowest of 10 equal width intervals. D5The proportion of LiDAR canopy returns that were above the 5 th of 10 equal width intervals. D9The proportion of LiDAR canopy returns that were above the 9 th of 10 equal width intervals. NgNumber of LiDAR pulses that penetrated to the ground within the subplot NThe total number of LiDAR returns detected within the subplot LiDAR Variables Extracted for Each Plot

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

Canopy Height PredictorCoefficientStandard Error p value Intercept <.001 H <.001 Hcv D r 2 =.7716 crossvalidation RMS: m

Biomass Response variable: Biomass PredictorCoefficientStandard Error p value Intercept H H <.001 D <.001 r 2 =.7679 crossvalidation RMS: Kg/ha

Productivity Response variable: ANPP5 PredictorCoefficientStandard Error p value Intercept <.001 H <.001 H Hcv <.003 r 2 =.5933 crossvalidation RMS: Kg/Ha * Year

This Field Season