Publication Details

Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States

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Cao, Baijing ; Domke, Grant M.; Russell, Matthew B.; Walters, Brian F.

Year Published

2019

Publication

Science of The Total Environment

Abstract

Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of forest ecosystems, as these processes control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distribution of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. Here we explored the effects of spatial aggregation of climatic, biotic, topographic and soil variables on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States (CONUS). Litter and soil variables were measured on permanent sample plots (n = 3303) from the National Forest Inventory (NFI) within the United States from 2000 to 2011. These data were used with vegetation phenology data estimated fromLANDSAT imagery (30 m) and raster data describing environmental variables for the entire CONUS to predict litter and soil C stocks. The total estimated litter C stockwas 2.07±0.97 Pg with an average density of 10.45±2.38Mgha−1, and the soil C stock at 0–20 cm depth was 14.68±3.50 Pg with an average density of 62.68±8.98Mg ha−1. This study extends NFI data from points to pixels providing spatially explicit and continuous predictions of litter and soil C stocks on forest land in the CONUS. The approaches described illustrate the utility of harmonizing field measurements with remotely sensed data to facilitate modeling and prediction across spatial scales in support of inventory, monitoring, and reporting activities, particularly in countries with ready access to remotely sensed data but with limited observations of litter and soil variables.

Keywords

National Forest Inventory; Machine learning; NDVI; Google Earth Engine

Citation

Cao, Baijing; Domke, Grant M.; Russell, Matthew B.; Walters, Brian F. 2019. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Science of The Total Environment. 654: 94-106. https://doi.org/10.1016/j.scitotenv.2018.10.359.

Last updated on: May 31, 2022