Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA. (12th April 2019)

• Record Type: Journal Article
• Authors: Hurtt, G; Zhao, M; Sahajpal, R; Armstrong, A; Birdsey, R; Campbell, E; Dolan, K; Dubayah, R; Fisk, J P; Flanagan, S; Huang, C; Huang, W; Johnson, K; Lamb, R; Ma, L; Marks, R; O'Leary, D; O'Neil-Dunne, J; Swatantran, A; Tang, H
• Is Part Of: Environmental research letters. Volume 14:Number 4(2019:Apr.)
• Publication Date: 2019-04-12
• Subjects: forest -- carbon -- climate mitigation -- MRV -- modeling; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ab0bbe
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current forest cover and carbon stocks. For planning, the additional step of modeling is required to quantitatively estimate forest carbon sequestration potential in response to alternative land-use and management decisions. To be most useful and of decision-relevant quality, these model estimates must be at very high spatial resolution and with very high accuracy to capture important heterogeneity on the land surface and connect to monitoring efforts. Here, we present results from a new forest carbon monitoring and modeling system that combines high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and project future forest carbon sequestration potential for the state of Maryland at 90 m resolution. Statewide, the contemporary above-ground carbon stock was estimated to be 110.8 Tg C (100.3–125.8 Tg C), with a corresponding mean above-ground biomass density of 103.7 Mg ha −1 which was within 2% of independent empirically-based estimates. The forest above-ground carbon sequestration potential for the state was estimated to be much larger at 314.8 Tg C, and the forest above-ground carbon sequestration potential gap (i.e. potential-current) was estimated to be 204.1 Tg C, nearly double the current stock. These results imply a large statewide potential for future carbon sequestration from afforestation and reforestation activities. The high spatial resolution of the model estimates underpinning these totals demonstrate important heterogeneity across the state and can inform prioritization of actual afforestation/reforestation opportunities. With this approach, it is now possible to quantify both the forest carbon stock and future carbon sequestration potential over large policy relevant areas with sufficient accuracy and spatial resolution to significantly advance planning.