Grid Based Global Carbon Edge Regression Coefficients and Aggregations

Grid cell based regression coefficients for predicting global biomass in the pantropics.

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:

1. Asymptotic: Biomass=θ1−θ2⋅exp(−θ3⋅Distance),
2. Logarithmic: Biomass=β0+β1ln⋅(Distance) , or
3. Linear: Biomass=η0+η1⋅Distance

Then, for each grid cell, the candidate with the highest R2 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 (θ1) was estimated as the average biomass density at the largest observed distance in the cell.

This dataset also contains a shapefile parameter analysis of both proportion and total area of all forest landcover types (1-5).