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DOI10.1088/1748-9326/ab0bbe
Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA
Hurtt, G.1; Zhao, M.1; Sahajpal, R.1; Armstrong, A.1,2; Birdsey, R.3,4; Campbell, E.5; Dolan, K.1,2,6; Dubayah, R.1; Fisk, J. P.1,7; Flanagan, S.1,8; Huang, C.1; Huang, W.1,9; Johnson, K.3,10; Lamb, R.1; Ma, L.1; Marks, R.5; O'; Leary, D.1; O'; Neil-Dunne, J.11; Swatantran, A.1; Tang, H.1
发表日期2019
ISSN1748-9326
卷号14期号:4
英文摘要

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 aboveground 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.


WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
来源期刊ENVIRONMENTAL RESEARCH LETTERS
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/95427
作者单位1.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA;
2.NASA, GSFC, Greenbelt, MD USA;
3.US Forest Serv, USDA, Inventory & Anal Program, Newtown Sq, PA USA;
4.Woods Hole Res Ctr, POB 296, Woods Hole, MA 02543 USA;
5.Maryland Dept Nat Resources, Annapolis, MD 21401 USA;
6.Montana State Univ, Montana Inst Ecosyst, Bozeman, MT 59717 USA;
7.Appl Geosolut, Durham, NH 03824 USA;
8.Tall Timbers Res Stn, Wildland Fire Sci Program, 13093 Henry Beadel Dr, Tallahassee, FL 32312 USA;
9.Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Hubei, Peoples R China;
10.Forest & Agr Org United Nations, Dhaka, Bangladesh;
11.Univ Vermont, Rubenstein Sch Environm & Nat Resources, Burlington, VT 05405 USA
推荐引用方式
GB/T 7714
Hurtt, G.,Zhao, M.,Sahajpal, R.,et al. Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA[J],2019,14(4).
APA Hurtt, G..,Zhao, M..,Sahajpal, R..,Armstrong, A..,Birdsey, R..,...&Tang, H..(2019).Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA.ENVIRONMENTAL RESEARCH LETTERS,14(4).
MLA Hurtt, G.,et al."Beyond MRV: high-resolution forest carbon modeling for climate mitigation planning over Maryland, USA".ENVIRONMENTAL RESEARCH LETTERS 14.4(2019).
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