Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery.
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| Title: | Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery. |
|---|---|
| Authors: | Sunde, Michael1 (AUTHOR) diamondd@missouri.edu, Diamond, David1 (AUTHOR), Elliott, Lee1 (AUTHOR) |
| Source: | Remote Sensing. Dec2024, Vol. 16 Issue 23, p4440. 22p. |
| Subjects: | Image recognition (Computer vision), Ecological mapping, Land cover, Ecosystems, Conservation of natural resources |
| Abstract: | Spatial land cover depictions are essential for ecological and environmental management. The thematic resolution of land cover and vegetation maps is also a significant factor affecting the ability to effectively develop policy and land management decisions based on spatial data. Natural resource and conservation planners often seek to develop strategies at broad scales; however, high-quality spatial data depicting current vegetation and ecosystem types over large areas are often unavailable. Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. A RandomForest land cover classification of Sentinel-2 imagery was generated and further articulated into ecological types using a comprehensive set of secondary modeling approaches. A total of 123 types were mapped in Arkansas, including common cultural and ruderal land cover and vegetation such as pine plantations and developed types. Ozark–Ouachita Dry–Mesic Forest covered the most area, 17.51% of the state. Row Crops covered 17.16%. Twenty-five pine or pine plantation types covered 19.73% of the state, with Ozark–Ouachita pine woodland or mature pine plantation covering 6.15%. Field survey points were used to assess the quality of the mapped ecological systems. The approaches presented here provide a framework for finer resolution mapping of ecological systems at broad scales in other regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 181657728 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sunde%2C+Michael%22">Sunde, Michael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> diamondd@missouri.edu</i><br /><searchLink fieldCode="AR" term="%22Diamond%2C+David%22">Diamond, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elliott%2C+Lee%22">Elliott, Lee</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Dec2024, Vol. 16 Issue 23, p4440. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+mapping%22">Ecological mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Land+cover%22">Land cover</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Conservation+of+natural+resources%22">Conservation of natural resources</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Spatial land cover depictions are essential for ecological and environmental management. The thematic resolution of land cover and vegetation maps is also a significant factor affecting the ability to effectively develop policy and land management decisions based on spatial data. Natural resource and conservation planners often seek to develop strategies at broad scales; however, high-quality spatial data depicting current vegetation and ecosystem types over large areas are often unavailable. Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. A RandomForest land cover classification of Sentinel-2 imagery was generated and further articulated into ecological types using a comprehensive set of secondary modeling approaches. A total of 123 types were mapped in Arkansas, including common cultural and ruderal land cover and vegetation such as pine plantations and developed types. Ozark–Ouachita Dry–Mesic Forest covered the most area, 17.51% of the state. Row Crops covered 17.16%. Twenty-five pine or pine plantation types covered 19.73% of the state, with Ozark–Ouachita pine woodland or mature pine plantation covering 6.15%. Field survey points were used to assess the quality of the mapped ecological systems. The approaches presented here provide a framework for finer resolution mapping of ecological systems at broad scales in other regions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181657728 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs16234440 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 4440 Subjects: – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Ecological mapping Type: general – SubjectFull: Land cover Type: general – SubjectFull: Ecosystems Type: general – SubjectFull: Conservation of natural resources Type: general Titles: – TitleFull: Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sunde, Michael – PersonEntity: Name: NameFull: Diamond, David – PersonEntity: Name: NameFull: Elliott, Lee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 16 – Type: issue Value: 23 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
