Output from carbon forest edge effect analysis: edge effect distance and magnitude

In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider: method 1: Biomass= θ_1-θ_2⋅exp(-θ_3⋅Distance) method 2: Biomass= β_0+β_1⋅ln(Distance) method 3: Biomass = \eta_0+\eta_1

• Distance Then, for each grid cell, the candidate with the highest $R^2$ is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core () was estimated as the average biomass density at the largest observed distance in the cell. regression_coefficients_as_shapefile - projected spatially as an ESRI Shapefile where the methods are defined as: