University of Maryland Summary Method for Quantifying the Forest Reference Level of the Emissions Reduction Program of Mai-Ndombe Province, Democratic Republic of Congo

Principal Investigator:
Matthew Hansen

Sponsor:
World Bank

Time period:
2019
to
2020

Abstract:

The Global Land Analysis and Discovery (GLAD) laboratory in the Department of Geographical Sciences at the University of Maryland will work in partnership with the Observatory of Satellite data of the Forests of Central Africa (OSFAC) and the Department of Forest Inventory and Management (DIAF) of the government of the Democratic Republic of Congo (DRC) to execute the required analyses. In summary, probability-based samples of time-series imagery will be used as reference data in estimating activity data for the province of Mai-Ndombe, DRC, from January 1, 2004 to December 31, 2014 for the reference period (including two sub-periods for the 2004-2010, and 2011-2014 intervals), January 1, 2015 to December 31, 2016 for the interim reference period and January 1, 2017 to December 31, 2018 for the first monitoring period. The method employs a sampling block population where the province is divided into 2.5km by 2.5km blocks. Activity data will be assessed per block and per second stage pixel samples for forest loss and forest gain by forest transition type.