Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes

Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes | 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 Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes Jiemin Kang, Jinyu Yang, Yunxian Qing, Wei Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4035391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Examining the Saihanba Mechanical Forest Farm, this study utilized landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the vector machine method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units based on the degree of global landscape risk. Employing landscape pattern indices and a method around spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. Results indicate significant shifts in land use types since 1987, notably with a evident increase in forest landscape predominantly at the expense of grassland and sandy land. Over the 1987 to 2020 period, landscape risk demonstrated a gradual decline, with mid-high and high-risk areas clustering in specific locales, while the broader region predominantly featured low landscape risk. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. Since its establishment in 1962, the landscape changes caused by human factors such as afforestation projects and scientific forest management methods have reduced the ecological risk of the landscape. A comprehensive exploration using geographic detectors identified nine ecological driving factors, with soil type emerging as the principal risk determinant, and its synergistic interaction with precipitation and other factors surpassing the individual factor effects. landscape type landscape ecological risk landscape index geographic detector Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4035391","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278084646,"identity":"19b879bd-592a-4f78-9865-ffff359909db","order_by":0,"name":"Jiemin Kang","email":"","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiemin","middleName":"","lastName":"Kang","suffix":""},{"id":278084647,"identity":"baedabfa-5a66-4f53-983e-277344e7f4ee","order_by":1,"name":"Jinyu Yang","email":"","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jinyu","middleName":"","lastName":"Yang","suffix":""},{"id":278084648,"identity":"e44a3498-08d8-4033-a181-d990209a6ed9","order_by":2,"name":"Yunxian Qing","email":"","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yunxian","middleName":"","lastName":"Qing","suffix":""},{"id":278084649,"identity":"37961a08-2404-43c7-9522-8b4a2937a058","order_by":3,"name":"Wei Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDADNmbmgw8+VEjI8ROvhZ0t2XDGGQtjyQai9fDzmEnztlUkbiCkRb797LEHP3fU2vMxMxhIzpwnwbiBgfnhoxt4tBicyUs37D1znJmNmSHB4OM2CWZzBjZj4xx8WhhyzCR4246xAbUcSJy5TYLNsoGHTRqfFvn+N2aSf9uO8bAxMzYc5p0jwWNwgIAWhhs5IF/XSAADmbGZt0FCgqAWgxtvzI1l2w4YsAF9wzjjmISBZDMBv8j355g9fNtWZy/ff/77jw81dfX97M0PH+N1GDASgfgwEp8Zv3KYljrCykbBKBgFo2DkAgDiREUR6TEDmwAAAABJRU5ErkJggg==","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-03-08 03:33:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4035391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4035391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52826322,"identity":"5a2cfee7-bcb2-49ac-8d0a-658e22113b30","added_by":"auto","created_at":"2024-03-17 01:07:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195309,"visible":true,"origin":"","legend":"","description":"","filename":"Basedonecologicalriskassessmentoflandscape.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4035391/v1_covered_f58eb0e3-a30b-47d3-97ae-e3f7257875bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Based on ecological risk assessment of landscape from the perspective of Saihanba landscape pattern changes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"landscape type, landscape ecological risk, landscape index, geographic detector","lastPublishedDoi":"10.21203/rs.3.rs-4035391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4035391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Examining the Saihanba Mechanical Forest Farm, this study utilized landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the vector machine method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units based on the degree of global landscape risk. Employing landscape pattern indices and a method around spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. Results indicate significant shifts in land use types since 1987, notably with a evident increase in forest landscape predominantly at the expense of grassland and sandy land. Over the 1987 to 2020 period, landscape risk demonstrated a gradual decline, with mid-high and high-risk areas clustering in specific locales, while the broader region predominantly featured low landscape risk. 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