气候变化领域集成服务门户(CCPortal): Beyond MRV: High-resolution forest carbon modeling for climate mitigation planning over Maryland, USA

CCPortal

DOI	10.1088/1748-9326/ab0bbe
	Beyond MRV: High-resolution forest carbon modeling for climate mitigation planning over Maryland, USA
	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.
发表日期	2019
ISSN	17489318
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 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. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	carbon; climate mitigation; forest; modeling; MRV
语种	英语
scopus关键词	Carbon; Carbon dioxide; Deforestation; Ecology; Image resolution; Land use; Models; Reforestation; Remote sensing; Afforestation/reforestation; Carbon sequestration potential; Climate mitigations; forest; Forest carbon sequestration; High resolution remote sensing; Land use and managements; Very high spatial resolutions; Climate change; carbon; carbon emission; carbon sequestration; emission control; environmental change; forest ecosystem; forestry modeling; forestry policy; land use planning; mitigation; planning practice; prioritization; reforestation; remote sensing; Maryland; United States
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/154617
作者单位	Department of Geographical Sciences, University of Maryland, College Park, MD 20742, United States; NASA-GSFC, Greenbelt, MD, United States; USDA Forest Service Inventory and Analysis Program, Newtown SquarePA, United States; Woods Hole Research Center, Woods Hole, MA, United States; Maryland Department of Natural Resources, Annapolis, MD 21401, United States; Montana Institute of Ecosystems, Montana State University59717, United States; Applied Geosolutions, Durham, NH 03824, United States; Wildland Fire Science Program, Tall Timbers Research Station, 13093 Henry Beadel Drive, Tallahassee, FL 32312, United States; Wuhan University, School of Resources and Environmental Sciences, Hubei Province, 430079, China; Forest and Agriculture Organization of the United Nations, Dhaka, Bangladesh; Rubenstein School of the Environment and Natural Resources, University of Vermont, Burlington, VT 05405, United States
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).