County-scale biomass map comparison: a case study for Sonoma, California. Issue 5 (2nd November 2017)

• Record Type: Journal Article
• Authors: Huang, Wenli; Swatantran, Anu; Duncanson, Laura; Johnson, Kristofer; Watkinson, Dana; Dolan, Katelyn; O'Neil-Dunne, Jarlath; Hurtt, George; Dubayah, Ralph
• Is Part Of: Carbon management. Volume 8:Issue 5/6(2017)
• Page Start: 417
• Page End: 434
• Publication Date: 2017-11-02
• Subjects: Aboveground biomass -- carbon monitoring system -- map comparison -- Sonoma County -- California; Carbon dioxide mitigation -- Periodicals; Greenhouse gas mitigation -- Periodicals; Carbon dioxide -- Environmental aspects -- Periodicals; Greenhouse gases -- Environmental aspects -- Periodicals; 363.73874605
• DOI: 10.1080/17583004.2017.1396840
• Languages: English
• ISSNs: 1758-3004

Abstract

ABSTRACT: The amount of carbon stored in forests affects a wide range of regional- to global-scale climate change processes. However, current maps often show large differences in carbon accounting. In this study, we present a framework to evaluate and compare multiple recent biomass maps at the county scale (4119 km 2 ). We first compare the differences in the forest and non-forest areas at the pixel and county levels from multiple maps. Map-based estimates of county-level mean and total biomass are compared to the United States Forest Service (USFS) sample-based estimates. Comparison of raster-based biomass products shows differences in mean and total biomass at both pixel- and county-levels. Despite all the maps using USFS's plot data for model training, only the three active sensor derived products compare well to USFS's estimates of total biomass (within 10%), while the three passive sensor derived map products underestimated total biomass by as much as 47%. Our evaluation demonstrates that the biomass map generated using combined Light Detection and Ranging (LiDAR) and auxiliary data achieve accurate estimates at plot-level (R 2 = 0.67; RMSE = 97.9 Mg.ha -1 ). This comparison study confirmed that missing direct height information either from active sensors tends to underestimate total biomass and mean biomass density at county-level.