Remote Sensing, Vol. 17, Pages 3975: Evaluating Global and National Datasets in an Ensemble Approach to Estimating Carbon Emissions as Part of SERVIR’s CArbon Pilot

Remote Sensing doi: 10.3390/rs17243975

Authors:
Christine Evans
Emil A. Cherrington
Lauren Carey
Ashutosh Limaye
Sajana Maharjan
Diego Incer Nuñez
Eric R. Anderson
Kelsey Herndon
Africa I. Flores-Anderson

Understanding where forest loss occurs and the resulting carbon emissions is a critical component of assessing national carbon budgets. To complement existing greenhouse gas (GHG) guidance and evaluate input datasets used in emissions estimation, SERVIR—a joint USAID and NASA initiative—implemented the SERVIR CArbon Pilot (S-CAP) project. This study focuses on the variability and reliability of land cover and biomass datasets that serve as inputs for such calculations. Seventeen aboveground biomass and twelve land cover change datasets were analyzed to characterize the variability in forest cover loss and biomass estimates for Guatemala, Nepal, and Zambia. Forest loss estimates varied substantially, ranging from 20,733 to 441,227 ha/yr in Guatemala, 1738 to 385,087 ha/yr in Nepal, and 6141 to 1,902,957 ha/yr in Zambia. Biomass estimates also differed widely depending on the dataset and forest mask applied: mean values ranged from 54.6 to 293.3 tons/ha across countries and periods. Accuracy assessments using national reference data for forest changes ranged from 67 to 97%, while National Forest Inventory biomass estimates diverged notably from global products. The ensemble approach highlights how differences in input datasets, particularly in forest extent and biomass magnitude, can propagate through emissions calculations. These findings underscore the importance of understanding and evaluating dataset variability prior to national carbon reporting and emissions estimation.

Source link

Christine Evans www.mdpi.com