Measuring forest biomass using AIMS lidar and aerial high-resolution imagery
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Increasing atmospheric carbon dioxide (CO2) levels are a leading cause of climate change (Malhi et al. 2002). At least half of the Earth’s terrestrial carbon is stored in forest biomass (Gower et al. 1996) by the photosynthetic conversion of atmospheric CO2. Therefore, estimating forest carbon stocks helps us quantify carbon concentrations and potential sources and sinks for CO2. One way that ecologists calculate biomass is with empirical allometric equations that use species and diameter at breast height (DBH) and divide by two to estimate carbon (Brown and Schroeder 1999, Jenkins et al. 2004). I hypothesized that I could estimate stand-level biomass using the Airborne Imaging Multispectral Sensor’s (AIMS) high-resolution imagery and lidar height measurements. To test this notion, I selected a study area on Mount Holyoke College property, in South Hadley, Massachusetts and systematically sampled 366 trees for species, height, DBH, and canopy data. I obtained lidar-derived canopy height and high resolution imagery with the AIMS system. For the ground validation of biomass, I created ten 900m2 subplots, where I identified species, measured DBH for all live stems >12.4cm, and recorded place in the canopy. I calculated biomass using the corresponding biomass equations, summed the results, and scaled to hectare. I also calculated biomass using only dominant and co-dominant trees. I averaged the lidar values and the ground-sampled trees’ heights within each plot to obtain plot average height for each method. By dividing the area into 20 plots, a linear regression indicated that the lidar average height was a significant predictor of dominant ground-sampled tree average height (p<0.001, R2=0.658). To remotely estimate biomass, I identified species and stem density in georeferenced AIMS images of each subplot. From ground data, I created linear regression models to estimate DBH from height. I used lidar height to estimate DBH values in the species-specific allometric biomass equations found in Jenkins et al. (2004). I multiplied these biomass values by the number of stems of each species in the plot, scaled the value to hectare, and summed the results. I compared these results with the ground biomass data. The linear regression indicated that the remote method was a significant predictor of dominant tree ground biomass (p=0.022, R2=0.499). These results suggest that this technique has the potential to adequately predict stand-level biomass in a southern New England forest. The next step will be to expand the dataset to determine the robustness of the method.
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