The Development and Evaluation of a High-Resolution Above Ground Biomass Product for the Commonwealth of Puerto Rico (2000).

This study details the development of a U.S. Commonwealth of Puerto Rico above-ground forest biomass (agb) product (baseline 2000) developed by the United States Environmental, Protection Agency (epa) that was compared to another AGB product developed by the U.S. Forest Service (usfs) for the same area. The USEPA product tended to over-predict in areas of low biomass and under-predict in high biomass areas when compared to observed plot data, but compared favorably to a Forest Inventory Analysis (fia) assessment of structure and condition of Puerto Rico forests (72.6 Mg/ha versus 80.0 Mg/ ha, respectively). AGB estimates were highly correlated with reference FIA biomass for both maps at their native spatial resolutions (USEPA: r =0.93, USFS: r = 0.92). AGB mean difference between both products was 33.5 Mg/ha (USFS mean = 106.1 Mg/ha; USEPA mean = 72.6 Mg/ha), a difference not out-of- scope when compared to other biomass comparative studies.

Estimating above-ground carbon biomass in a newly restored coastal plain wetland using remote sensing.

Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R(2) values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R(2) of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas.