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Design-based sampling methods for environmental monitoring

Zhao, Xin (2021). Design-based sampling methods for environmental monitoring. Diss. (sammanfattning/summary) Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae, 1652-6880
ISBN 978-91-7760-780-9
eISBN 978-91-7760-781-6
[Doctoral thesis]

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Abstract

Efficient strategies for environmental monitoring are proposed with an emphasis on the importance of using available information. In environmental monitoring, it is common to use area frames covering the assumed spread of the population of inter-est. By using such a frame, a sample unit is usually not a unit in the population, rather a point on a surface. The population unit of environmental surveys exits in a spatial context where nearby units often have similar values to each other. When this is the case, we can estimate the unknown population parameters more efficiently if the sample is well spread over the population. Spatially balanced sampling is sam-pling designs that employ available auxiliary variables to select well-spread sam-ples. When applying such a design with equal inclusion probabilities, we match the sample distribution to the population distribution of the auxiliary variables, which can improve the estimation of the state of the population. Paper I presents a new sampling strategy for the Swedish national forest inventory using spatially balanced sampling designs for an area frame. When estimating change, we wish to update the sample at the following occasions using the most recently available information. When updating the sample, we also want to have a certain degree of overlap between the successive samples. By doing so, we can get more precise estimates for states and the change between two states simultaneously. Therefore, there is a demand for selecting well-spread and partially overlapping samples over time. In Papers II and III, the focus is on such samples, and more specifically, on positively coordinated and spatially balanced samples. In Paper II, a sampling strategy of selecting positively coordinated and spatially balanced samples is proposed for monitoring the change of environmental variables, while the objective of Paper III is to estimate the variance of an estimator of change using such samples. When a single survey does not provide sufficient quality of estimates for some domain, we can plan for a complementary survey or combine existing surveys to improve the quality. When multiple surveys are combined, there is a risk of introducing bias to the estimators. Combining several surveys to use all available information when estimating the population parameters thus becomes a challenge. In Paper IV, we investigate the possibility of producing less biased or unbiased estimators when combining several independent surveys of a finite population.

Authors/Creators:Zhao, Xin
Title:Design-based sampling methods for environmental monitoring
Series Name/Journal:Acta Universitatis Agriculturae Sueciae
Year of publishing :2021
Number:2021:51
Number of Pages:47
Publisher:Department of Forest Resource Management, Swedish University of Agricultural Sciences
ISBN for printed version:978-91-7760-780-9
ISBN for electronic version:978-91-7760-781-6
ISSN:1652-6880
Language:English
Publication Type:Doctoral thesis
Article category:Other scientific
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 405 Other Agricultural Sciences > Environmental Sciences related to Agriculture and Land-use
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:auxiliary variables, sampling strategy, area frame sampling, inclusion probabilities, spatially balanced sampling designs, the local pivotal method, spatially correlated Poisson sampling, sample coordination, combining samples
URN:NBN:urn:nbn:se:slu:epsilon-p-112985
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-112985
ID Code:24939
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:19 Aug 2021 07:26
Metadata Last Modified:20 Aug 2021 12:57

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