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The range of mapped carbon values is in line with values found in the literature.
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We then mapped the carbon value (either mean or total) and the environmental strata value of each substratum onto the results of these simulations to examine potential patterns.
The final carbon maps show comprehensive spatial patterns with e.g. high carbon values along the riparian vegetation (gallery forests) and the dense forest patches in the lower elevations in PESA.
To map carbon distributions as well as to analyze the relation between phenological parameters and aboveground carbon values, we used a random forest regression (RFR).
We were able to map carbon distributions within the selected study areas, whereat higher uncertainties were identified in physiognomies and related carbon values that were not well represented by the field sampling.
But elsewhere, carbon values are very low of no zero.
Possible points of representation range from below the alpha carbon (negative values) to beyond the beta carbon (values >1.0).
Figure 1 Continental U.S. risk-scaled carbon value map.
Multiplying the average relative carbon value on the map by the market value of carbon determines the risk scaled value of the forest carbon for a given forest type.
Using this final stratification, we mapped ACD at 1.0 ha resolution by assigning the LiDAR-derived median carbon value for each class to the area occupied by that class across the regional maps.
The bottom-up approach focusses on mapping carbon stocks and uncertainties at fine scales.
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