B108-0007
High-resolution monitoring of forest carbon sequestration to meet climate goals
High-resolution monitoring of forest carbon sequestration to meet climate goals
Wednesday, 16 December 2020
Poster
Abstract:
Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for
inclusion of forest carbon in climate mitigation plans. Required forest carbon data consist of integrated: (1) baseline mapping of contemporary carbon stocks, (2) projections of future carbon
stocks for planning, and (3) annual monitoring for assessment. Previously, we developed high-resolution mapping of contemporary carbon stocks and 300-yr projections of annual carbon
sequestration potential (CSP) for Maryland at 90m resolution by integrating airborne LiDAR with mechanistic ecosystem modeling (Ecosystem Demography (ED) model). Here we extend this
work to Delaware and present the first consistent, annual monitoring results for both states (Maryland and Delaware). For monitoring, we intersect annual carbon stock estimates with 30m
Landsat-derived changes in forest area to compute realized carbon gains and losses over the period 2011—2019. Moving forward, we expect to extend this pilot system developed for Maryland and Delaware to an additional 10 U.S. states. As the framework is flexible, developing a nationwide or global system is increasingly feasible, particularly with the recent availability of
GEDI LiDAR observations from space.
inclusion of forest carbon in climate mitigation plans. Required forest carbon data consist of integrated: (1) baseline mapping of contemporary carbon stocks, (2) projections of future carbon
stocks for planning, and (3) annual monitoring for assessment. Previously, we developed high-resolution mapping of contemporary carbon stocks and 300-yr projections of annual carbon
sequestration potential (CSP) for Maryland at 90m resolution by integrating airborne LiDAR with mechanistic ecosystem modeling (Ecosystem Demography (ED) model). Here we extend this
work to Delaware and present the first consistent, annual monitoring results for both states (Maryland and Delaware). For monitoring, we intersect annual carbon stock estimates with 30m
Landsat-derived changes in forest area to compute realized carbon gains and losses over the period 2011—2019. Moving forward, we expect to extend this pilot system developed for Maryland and Delaware to an additional 10 U.S. states. As the framework is flexible, developing a nationwide or global system is increasingly feasible, particularly with the recent availability of
GEDI LiDAR observations from space.