Forest Biomass and Land-Use Change in Central Africa: Reducing Regional Carbon Cycle Uncertainty

Central Africa has the second largest unfragemented block of tropical rain forest in the world it is also one of the largest carbon and biodiversity reservoirs. With nearly one-third of the forest currently allocated for logging, the region is poised to undergo extensive land-use change. Through the mapping of the forests, our Integrated Forest Monitoring System for Central Africa (INFORMS) project aims to monitor habitat alteration, support biodiversity conservation, and promote better land-use planning and forest management. Designed as an interdisciplinary project, its goal is to integrate data acquired from satellites with field observations from forest inventories, wildlife surveys, and socio-economic studies to map and monitor forest resources. This project also emphasizes on collaboration and coordination with international, regional, national, and local partners--including non-profit, governmental, and commercial sectors. This project has been focused on developing remote sensing products for the needs of forest conservation and management, insuring that research findings are incorporated in forest management plans at the national level. The societal impact of INFORMS can be also appreciated through the development of a regional remote sensing network in central Africa. With a regional office in Kinshasa, (www.OSFAC.org), the contribution to the development of forest management plans for 1. 5 million hectares of forests in northern Republic of Congo (www.tt-timber.com), and the monitoring of park encroachments in the Albertine region (Uganda and DRC) (www.albertinerift.org).biomass carbon at 1 km resolution synergistically from optical (MODIS) and radar (JERS-1, Radarsat, and SRTM) data. Validation of the forest biomass and carbon distributions using available site-specific data from various sources (US & Canadian Forest Service, LTER, BOREAS, etc.) Establishing the uncertainty in biomass carbon distribution of North America by performing a comparative analysis of regression, physically based models and forest inventory data.