Sampling Intensification for Forest Inventories within a specific domain

Sampling Intensification for Forest Inventories within a specific domain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sampling Intensification for Forest Inventories within a specific domain Trinh H.K. Duong, Guillaume Chauvet, Olivier Bouriaud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5896569/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract National Forest Inventories (NFIs) are large-scale surveys that typically employ low sampling intensity, sufficient for national-level estimations. However, this low sampling intensity can make it difficult to produce reliable estimates for specific domains of interest under a design-based approach due to limited sample sizes.NFIs use models (model-assisted or model-based approaches) for small area estimation to make estimations in the domain of interest with minimal or no sample.However the reduced sample size can also be challenging for fitting models.Increasing the sampling intensity would represent resolve these issues.In this paper, we propose solutions to complement an existing NFI sample in order to improve estimation.We compare several sampling designs of intensification.This intensification poses the issue of integrating two dependent and non-overlapping samples with varying sampling intensities: the regular NFI sample and the intensified sample.We provide estimators of totals and ratios, and associated variance estimators for the domain of interest and the entire territory using a conditional approach.Our results show that intensification reduces the variance for an estimation at the level of both the domain of interest and the whole territory, that the choice of sampling designs considered has a limited impact on the estimation of the outcome. two-phase sampling domain of interest conditional approach intensified sample national forest inventory ratio estimation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Mar, 2025 Reviews received at journal 27 Mar, 2025 Reviews received at journal 04 Mar, 2025 Reviewers agreed at journal 01 Feb, 2025 Reviewers agreed at journal 29 Jan, 2025 Reviewers invited by journal 29 Jan, 2025 Editor assigned by journal 25 Jan, 2025 Submission checks completed at journal 25 Jan, 2025 First submitted to journal 24 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5896569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":409877428,"identity":"d3a0b751-ba64-4fae-933a-a26109a74869","order_by":0,"name":"Trinh H.K. 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