Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

Jinxun Liu, Shuguang Liu, Thomas R. Loveland and Larry L. Tieszen

Ecological Modelling, 2008, vol. 219, issue 3, 361-372

Abstract: Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975–2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2Tg C (1teragram=1012gram) per year, including 1.9Tg C removed from the ecosystem as the consequences of land cover change.

Keywords: Land cover change; Net primary productivity; GEMS; Carbon budget; Uncertainty (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:219:y:2008:i:3:p:361-372

DOI: 10.1016/j.ecolmodel.2008.04.019

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