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Cross-classes domain inference with network sampling for natural resource inventory

Hou, Zhengyang and McRoberts, Ronald E. and Zhang, Chunyu and Ståhl, Göran and Zhao, Xiuhai and Wang, Xuejun and Li, Bo and Xu, Qing (2022). Cross-classes domain inference with network sampling for natural resource inventory. Forest Ecosystems. 9 , 100029
[Research article]

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Abstract

There are two distinct types of domains, design-and cross-classes domains, with the former extensively studied under the topic of small-area estimation. In natural resource inventory, however, most classes listed in the condition tables of national inventory programs are characterized as cross-classes domains, such as vegetation type, productivity class, and age class. To date, challenges remain active for inventorying cross-classes domains because these domains are usually of unknown sampling frame and spatial distribution with the result that inference relies on population-level as opposed to domain-level sampling. Multiple challenges are noteworthy: (1) efficient sampling strategies are difficult to develop because of little priori information about the target domain; (2) domain inference relies on a sample designed for the population, so within-domain sample sizes could be too small to support a precise estimation; and (3) increasing sample size for the population does not ensure an increase to the domain, so actual sample size for a target domain remains highly uncertain, particularly for small domains. In this paper, we introduce a design-based generalized systematic adaptive cluster sampling (GSACS) for inventorying cross-classes domains. Design-unbiased Hansen-Hurwitz and Horvitz-Thompson estimators are derived for domain totals and compared within GSACS and with systematic sampling (SYS). Comprehensive Monte Carlo simulations show that (1) GSACS Hansen-Hurwitz and Horvitz-Thompson estimators are unbiased and equally efficient, whereas the latter outperforms the former for supporting a sample of size one; (2) SYS is a special case of GSACS while the latter outperforms the former in terms of increased efficiency and reduced intensity; (3) GSACS Horvitz-Thompson variance estimator is design-unbiased fora single SYS sample; and (4) rules-of thumb summarized with respect to sampling design and spatial effect improve precision. Because inventorying a mini domain is analogous to inventorying a rare variable, alternative network sampling procedures are also readily available for inventorying cross-classes domains.

Authors/Creators:Hou, Zhengyang and McRoberts, Ronald E. and Zhang, Chunyu and Ståhl, Göran and Zhao, Xiuhai and Wang, Xuejun and Li, Bo and Xu, Qing
Title:Cross-classes domain inference with network sampling for natural resource inventory
Series Name/Journal:Forest Ecosystems
Year of publishing :2022
Volume:9
Article number:100029
Number of Pages:12
Publisher:KEAI PUBLISHING LTD
ISSN:2095-6355
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:Cross-classes domain estimation, Design-based inference, Network sampling, Generalized systematic adaptive cluster, sampling, Forest inventory
URN:NBN:urn:nbn:se:slu:epsilon-p-117100
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-117100
Additional ID:
Type of IDID
DOI10.1016/j.fecs.2022.100029
Web of Science (WoS)000788682300001
ID Code:27961
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:20 May 2022 07:25
Metadata Last Modified:20 May 2022 07:31

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