Making the US national forest inventory spatially contiguous and temporally consistent. (1st June 2022)

• Record Type: Journal Article
• Authors: Yu, Yifan; Saatchi, Sassan; Domke, Grant M; Walters, Brian; Woodall, Christopher; Ganguly, Sangram; Li, Shuang; Kalia, Subodh; Park, Taejin; Nemani, Ramakrishna; Hagen, Stephen C; Melendy, Lindsay
• Is Part Of: Environmental research letters. Volume 17:Number 6(2022)
• Publication Date: 2022-06-01
• Subjects: biomass -- remote sensing -- forests -- greenhouse gas -- FIA -- data fusion; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ac6b47
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Signatories to the United Nations Framework Convention on Climate Change (UNFCCC) are required to annually report economy-wide greenhouse gas emissions and removals, including the forest sector. National forest inventory (NFI) is considered the main source of data for reporting forest carbon stocks and changes to UNFCCC. However, NFI samples are often collected asynchronously across regions in intervals of 5–10 years or sub-sampled annually, both introducing uncertainties in estimating annual carbon stock changes by missing a wide range of forest disturbance and recovery processes. Here, we integrate satellite observations with forest inventory data across the conterminous United States to improve the spatial and temporal resolution of NFI for estimating annual carbon stocks and changes. We used more than 120 000 permanent plots from the US forest inventory and analysis (FIA) data, surveyed periodically at sampling rate of 15%–20% per year across the US to develop non-parametric remote sensing-based models of aboveground biomass carbon density (AGC) at 1 ha spatial resolution for the years 2005, 2010, 2015, 2016, and 2017. The model provided a relatively unbiased estimation of AGC compared to ground inventory estimates at plot, county, and state scales. The uncertainty of the biomass maps and their contributions to estimates of forest carbon stock changes at county and state levels were quantified. Our results suggest that adding spatial and temporal dimensions to the forest inventory data, will significantly improve the accuracy and precision of carbon stocks and changes at jurisdictional scales.