Assessing the Synergistic Use of Sentinel-1, Sentinel-2, and LiDAR Data for Forest Type and Species Classification.
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| Title: | Assessing the Synergistic Use of Sentinel-1, Sentinel-2, and LiDAR Data for Forest Type and Species Classification. |
|---|---|
| Authors: | Aranguren, Itxaso1 (AUTHOR), González-Audícana, María1,2 (AUTHOR), Montero, Eduardo2,3 (AUTHOR), Sanz, José Antonio1,3 (AUTHOR), Álvarez-Mozos, Jesús1,2 (AUTHOR) jesus.alvarez@unavarra.es |
| Source: | Remote Sensing. Jun2025, Vol. 17 Issue 12, p2028. 23p. |
| Subjects: | Forest mapping, Forest management, Remote sensing, Data integration, Optical sensors |
| Abstract: | The design of effective forest management strategies requires the precise characterization of forested areas. Currently, different remote sensing technologies can be used for forest mapping, with optical sensors being the most common. The objective of this study was to evaluate the synergistic use of Sentinel-1, Sentinel-2, and LiDAR data for classifying forest types and species. With this aim, a case study was conducted using random forest, considering three classification levels of increasing complexity. The classifications incorporated Sentinel-1 and Sentinel-2 monthly composites, along with LiDAR metrics and topographic variables. The results showed that the combination of Sentinel-2 monthly composites, LiDAR, and topographic variables obtained the highest overall accuracies (0.90 for level 1, 0.80 for level 2, and 0.79 for level 3). The most important variables were identified as Sentinel-2 red-edge and NIR bands from June, July, and August, along with height-related LiDAR and topographic variables. Although not as precise as Sentinel-2 at the species level, Sentinel-1 enabled the classification of broad forest types with remarkable accuracy (0.80), especially when combined with LiDAR data (0.83). Altogether, the results of this study demonstrate the potential of combining data from different Earth observation technologies to enhance the mapping of forest types and species. [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: 186260832 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing the Synergistic Use of Sentinel-1, Sentinel-2, and LiDAR Data for Forest Type and Species Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aranguren%2C+Itxaso%22">Aranguren, Itxaso</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22González-Audícana%2C+María%22">González-Audícana, María</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Montero%2C+Eduardo%22">Montero, Eduardo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sanz%2C+José+Antonio%22">Sanz, José Antonio</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Álvarez-Mozos%2C+Jesús%22">Álvarez-Mozos, Jesús</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jesus.alvarez@unavarra.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2025, Vol. 17 Issue 12, p2028. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+management%22">Forest management</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+sensors%22">Optical sensors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The design of effective forest management strategies requires the precise characterization of forested areas. Currently, different remote sensing technologies can be used for forest mapping, with optical sensors being the most common. The objective of this study was to evaluate the synergistic use of Sentinel-1, Sentinel-2, and LiDAR data for classifying forest types and species. With this aim, a case study was conducted using random forest, considering three classification levels of increasing complexity. The classifications incorporated Sentinel-1 and Sentinel-2 monthly composites, along with LiDAR metrics and topographic variables. The results showed that the combination of Sentinel-2 monthly composites, LiDAR, and topographic variables obtained the highest overall accuracies (0.90 for level 1, 0.80 for level 2, and 0.79 for level 3). The most important variables were identified as Sentinel-2 red-edge and NIR bands from June, July, and August, along with height-related LiDAR and topographic variables. Although not as precise as Sentinel-2 at the species level, Sentinel-1 enabled the classification of broad forest types with remarkable accuracy (0.80), especially when combined with LiDAR data (0.83). Altogether, the results of this study demonstrate the potential of combining data from different Earth observation technologies to enhance the mapping of forest types and species. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17122028 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 2028 Subjects: – SubjectFull: Forest mapping Type: general – SubjectFull: Forest management Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Data integration Type: general – SubjectFull: Optical sensors Type: general Titles: – TitleFull: Assessing the Synergistic Use of Sentinel-1, Sentinel-2, and LiDAR Data for Forest Type and Species Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aranguren, Itxaso – PersonEntity: Name: NameFull: González-Audícana, María – PersonEntity: Name: NameFull: Montero, Eduardo – PersonEntity: Name: NameFull: Sanz, José Antonio – PersonEntity: Name: NameFull: Álvarez-Mozos, Jesús IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
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