气候变化领域集成服务门户(CCPortal): Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data

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DOI	10.1016/j.foreco.2020.118777
	Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data
	Hou Z.; Domke G.M.; Russell M.B.; Coulston J.W.; Nelson M.D.; Xu Q.; McRoberts R.E.
发表日期	2020
ISSN	0378-1127
英文摘要	The United Nations Framework Convention on Climate Change requires annual estimates for forestry and ecological indicators to monitor the change in forest resources, the sustainability of forest management, and the emission and sink of forest carbon. It is particularly important to update estimates of forestland area in a timely fashion and at flexible geographical scales, not only for its value in monitoring biological diversity at the ecosystem scale, but also because of its close association with other indicators such as forest biomass and carbon. However, in the US, the Forest Survey Handbook advises that the sampling error should not exceed 3% per 404686 ha (one million acres) of forestland area, a demanding standard barely met by pooling the Forest Inventory and Analysis (FIA) panel data measured in an inventory cycle of 5–10 years. Consequently, this study aims to propose and illustrate an updating procedure using data assimilation that integrates a design-based estimator with a model-based mixed estimator for updating annual estimates at two population levels, the state- and county-levels. The three states in the USA, Minnesota (MN), Georgia (GA) and California (CA), representing the Northern, the Southern and the Pacific Northwest FIA programs, constitute the study areas. FIA data collected were based on a 5-year inventory cycle for MN (2006–2010) and GA (2005–2009), and a 10-year cycle for CA (2001–2010). The total number of sample plots was 17764 for MN, 6323 for GA, and 16740 for CA. Distinguishing features attribute to this procedure include: (1) unbiasedness: the integration of design-based estimates into the mixed estimator introduces a favorable property – unbiasedness, which could be the property national forest inventories concern the most; (2) efficiency: considerable improvements in estimation precision greater than 55%, achieving sampling errors as small as those relying on using 5–10 years pooled FIA data; (3) time: compared with the temporal trends reflected by design-based estimates, the updated trends were of much smoother trend lines and narrower confidence intervals that would better depict temporal changes for a population at flexible spatial scales; (4) space: this procedure is scale-invariant, meaning its efficiency is not affected by an inventory employing either a large- or small-area estimation, which was demonstrated at the two population levels; and (5) generalizability: this procedure is unbiased and efficient, 100% compatible with the FIA database which is readily available to the public, and thus suitable for various official reporting instruments. © 2020 Elsevier B.V.
英文关键词	Annual estimation; Data assimilation; Forest inventory and analysis; Forestland area estimation; Mixed estimator; Unbiasedness; Updating procedure
语种	英语
scopus关键词	Carbon; Ecology; Efficiency; Environmental regulations; Global warming; Instrument errors; Population statistics; Confidence interval; Ecological indicators; Estimation precision; Forest inventory and analysis; Geographical scale; National forest inventories; Small area estimation; United nations framework convention on climate changes; Forestry
来源期刊	Forest Ecology and Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/155633
作者单位	College of Forestry, Beijing Forestry University, Beijing, 100083, China; Northern Research Station, U.S. Forest Service, Saint Paul, MN, United States; Department of Forest Resources, University of Minnesota, Saint Paul, MN, United States; Southern Research Station, U.S. Forest Service, Blacksburg, VA, United States; Key Laboratory on the Science and Technology of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing, 100102, China
推荐引用方式 GB/T 7714	Hou Z.,Domke G.M.,Russell M.B.,et al. Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data[J],2020.
APA	Hou Z..,Domke G.M..,Russell M.B..,Coulston J.W..,Nelson M.D..,...&McRoberts R.E..(2020).Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data.Forest Ecology and Management.
MLA	Hou Z.,et al."Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data".Forest Ecology and Management (2020).