Comparing aboveground biomass recovery speed under different forest restoration strategies using GEDI Lidar and Landsat.
Mengyu Liang, University of Maryland College Park, mliang77@terpmail.umd.edu (Presenter)
Temilola E. Fatoyinbo, NASA GSFC, lola.fatoyinbo@nasa.gov
Julie Silva, University of Maryland College Park, jasilva@umd.edu
Matthew Hansen, University of Maryland, mhansen@umd.edu
Laura Duncanson, University of Maryland, lduncans@umd.edu
Restoring lost and degraded forests through deforestation curbing, afforestation, and reforestation offer critical opportunities to tackle climate change. Effective forest management guided by science can contribute to meeting carbon emission reduction goals for the 2030 agenda. Several forest management approaches aimed at increasing carbon sequestration are currently operational. In this project, we compare four conservation and restoration strategies, including forest conservation through protected areas, active reforestation, natural regeneration, and assisted natural regeneration.To do this, we combine datasets from NASA's GEDI and Landsat missions and utilize machine learning tools to monitor aboveground biomass density (AGBD) recovery associated with long-term forest conservation areas in East Africa. GEDI provides spatially consistent and high-resolution measurements of the Earth's forest structure for the current-day condition. Landsat archives capture long-term vegetation characteristics since the 1980s. Fusing these two data sources can help reconstruct management trajectories and reveal forest recovery dynamics. Past research activities have offered promising proofs of concept for such data fusion. In this study, we further develop on existing methods for fusing GEDI AGBD estimates with multispectral Landsat data to map regrowth in forest AGBD in East Africa. Such techniques enabled a data-driven quantification for accessing different forest restoration strategies, which will provide critical and timely information to help bolster forest management toward climate mitigation.
Associated Project(s):
Poster Location ID: 1-39
Presentation Type: Poster
Session: Poster Session 1
Session Date: Tue (May 9) 5:00-7:00 PM
CCE Program: TE