Grid Based Global Carbon Edge Regression Coefficents

To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. 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: Asymptotic: \(\mathrm{Biomass} = \theta_1-\theta_2\cdot\exp(-\theta_3\cdot\mathrm{Distance})\) , Logarithmic: \(\mathrm{Biomass}=\beta_0+\beta_1\ln\cdot(\mathrm{Distance})\) , or Linear: \(\mathrm{Biomass}=\eta_0+\eta_1\cdot Distance\) 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.