What is the weight of Mt Tabor's trees?

LiDAR data can be useful for estimating biomass in a given area, which can then be used to estimate carbon sequestration. High-resolution raster data models in the area of Portland’s iconic Mt Tabor Park here are generated from point cloud data acquired via the Oregon Department of Geology and Mineral Industries (DOGAMI), and then manipulated to produce a feature height data model, which was refined in order to exclude man-made features like buildings, roads, cars and reservoirs. Descriptive statistics from the feature height model were then plugged into a formula devised for estimating biomass on Vancouver Island (Tsui, et al., 2012). Although Mt Tabor Park is not an archetypal forest in this sense, similarities in climate and species of vegetation lend plausibility to deploying this equation for the park’s biomass estimate here. The results, however, in which estimated biomass is somewhat greater than expected, suggest that Tsui’s methods are likely not the best fit for estimating the biomass of a developed urban park. Further research would employ more sophisticated methods for classifying the raw LiDAR point cloud and for the identification of individual trees in the study area.

A digital elevation model is created from the raw LiDAR data using ground return points (the only classification in this dataset), while a digital surface model is created from first return points. Taking advantage of the data's fine grain, I was able to produce 1-foot resolution rasters.

The difference between the DEM and DSM yields a model for visualizing the height of individual features in the study area relative to the ground. This feature height raster highlights the location of buildings, roads, and trees, which on Mt Tabor are largely Douglas-fir, the official tree of Cascadia.

After clipping the feature height raster to the park's boundaries, I created a new raster based on a minimum cell value of 10 (feet above ground elevation) and then used the pixel editor tool to get rid of any remaining man-made features so as to isolate vegetation.

Calculating statistics for this vegetation raster gives us the numbers needed to compute biomass within Mt Tabor Park. Mean canopy height is 71.8 feet. The 90th percentile for canopy height is 119.7 feet. The 10th percentile for canopy height is 22 feet.

These figures are then plugged into the formula via Tsui, et al. (2012) for calculating biomass of coastal forests in the Pacific Northwest:

-7.144 - 12.925⋅MeanH + 2.239⋅h10_Nelson + 14.019⋅h90_Nelson, where the *_Nelson variables represent values for indicated percentiles of feature height.

-7.144 - (12.925*71.8) + (2.239*22) + (14.019*119.7) = 792.1733 Mg (megagrams or metric tons) per hectare. The feature height model derived above covers nearly 72 hectares, so the total biomass of vegetation under canopy 10 feet or higher inside park boundaries is estimated at 56,981.6 metric tons according to this method.

This result is somewhat higher than what I'd expect based on Tsui's paper, which is probably due to the excision of return points lower than 10 feet, a first step in eliminating man-made features in the data that necessarily increased the value of all three variables for feature height in the study area. This method is therefore not likely the best fit for calculating biomass in a developed and well-groomed urban park like Mt Tabor; it was devised for estimations of "untamed" forest in the relative wilderness of Vancouver Island. Given more time I would explore machine learning approaches to identifying individual trees in the park in order to compare different approaches to the question of biomass and hopefully arrive at a more accurate and/or nuanced estimate.