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Research article - Peer-reviewed, 2022

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

Hou, Zhengyang; McRoberts, Ronald E.; Zhang, Chunyu; Stahl, Goran; Zhao, Xiuhai; Wang, Xuejun; Li, Bo; Xu, Qing

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.

Keywords

Cross-classes domain estimation; Design-based inference; Network sampling; Generalized systematic adaptive cluster; sampling; Forest inventory

Published in

Forest Ecosystems
2022, volume: 9, article number: 100029
Publisher: KEAI PUBLISHING LTD

Authors' information

Hou, Zhengyang
Beijing Forestry University
McRoberts, Ronald E.
University of Minnesota Twin Cities
Zhang, Chunyu
Beijing Forestry University
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Zhao, Xiuhai
Beijing Forestry University
Wang, Xuejun
Office of the National Forestry and Grassland Administrations Forest Resources Supervision Commissioner in Beijing
Li, Bo
University of Illinois Urbana-Champaign
Xu, Qing
International Centre for Bamboo and Rattan

UKÄ Subject classification

Forest Science

Publication Identifiers

DOI: https://doi.org/10.1016/j.fecs.2022.100029

URI (permanent link to this page)

https://res.slu.se/id/publ/117100