Optimal resolution for linking remotely sensed and forest inventory data in Europe.
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| Title: | Optimal resolution for linking remotely sensed and forest inventory data in Europe. |
|---|---|
| Authors: | Moreno, Adam1 adam.moreno@boku.ac.at, Neumann, Mathias1, Hasenauer, Hubert1 |
| Source: | Remote Sensing of Environment. Sep2016, Vol. 183, p109-119. 11p. |
| Subjects: | Remote sensing, Forest surveys, Sustainability, Image analysis, Forests & forestry |
| Geographic Terms: | Europe |
| Abstract: | Forests provide critical ecosystem services that ensure the sustainability of the environment and society. To manage forests on large scales, spatially explicit gridded data that describes the characteristics of these forests over the entire study area are required. There have been multiple efforts to create such data on regional and global scales. This type of gridded spatially explicit data on forest characteristics are typically done by integrating terrestrial forest inventory (NFI) and satellite-based remotely sensed data. Many studies that incorporate remotely sensed data and forest inventory data often directly compare pixels to inventory plots. The standard resolution of 0.0083° is typically used to integrate these two types of data sets. There is an assumption that, when producing gridded data sets incorporating forest inventory data, the finer the resolution the better the information. This assumption may seem intuitive, however at this resolution, in Europe, each 0.0083° cell has on average 1 NFI plot, which results in a sample with 0 degrees of freedom that represents 0.02% of the cell area. In this study, we challenge this assumption and we quantify the optimal resolution with which to compare and combine remotely sensed and NFI data from the largest collated and harmonized NFI data set in Europe including 196,434 plots. We determined that aggregating data with an original resolution of 0.0083° to between 0.0664° and 0.266° (or × 8 to × 32) produces the best agreement between these two forest inventory and remotely sensed data sets, and the lowest standard error in NFI data, and maintains the majority of the local-level spatial heterogeneity. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing of Environment is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 117316570 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal resolution for linking remotely sensed and forest inventory data in Europe. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moreno%2C+Adam%22">Moreno, Adam</searchLink><relatesTo>1</relatesTo><i> adam.moreno@boku.ac.at</i><br /><searchLink fieldCode="AR" term="%22Neumann%2C+Mathias%22">Neumann, Mathias</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hasenauer%2C+Hubert%22">Hasenauer, Hubert</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing+of+Environment%22">Remote Sensing of Environment</searchLink>. Sep2016, Vol. 183, p109-119. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+surveys%22">Forest surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Europe%22">Europe</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Forests provide critical ecosystem services that ensure the sustainability of the environment and society. To manage forests on large scales, spatially explicit gridded data that describes the characteristics of these forests over the entire study area are required. There have been multiple efforts to create such data on regional and global scales. This type of gridded spatially explicit data on forest characteristics are typically done by integrating terrestrial forest inventory (NFI) and satellite-based remotely sensed data. Many studies that incorporate remotely sensed data and forest inventory data often directly compare pixels to inventory plots. The standard resolution of 0.0083° is typically used to integrate these two types of data sets. There is an assumption that, when producing gridded data sets incorporating forest inventory data, the finer the resolution the better the information. This assumption may seem intuitive, however at this resolution, in Europe, each 0.0083° cell has on average 1 NFI plot, which results in a sample with 0 degrees of freedom that represents 0.02% of the cell area. In this study, we challenge this assumption and we quantify the optimal resolution with which to compare and combine remotely sensed and NFI data from the largest collated and harmonized NFI data set in Europe including 196,434 plots. We determined that aggregating data with an original resolution of 0.0083° to between 0.0664° and 0.266° (or × 8 to × 32) produces the best agreement between these two forest inventory and remotely sensed data sets, and the lowest standard error in NFI data, and maintains the majority of the local-level spatial heterogeneity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing of Environment is the property of Elsevier B.V. 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: 10.1016/j.rse.2016.05.021 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 109 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Forest surveys Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Forests & forestry Type: general – SubjectFull: Europe Type: general Titles: – TitleFull: Optimal resolution for linking remotely sensed and forest inventory data in Europe. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moreno, Adam – PersonEntity: Name: NameFull: Neumann, Mathias – PersonEntity: Name: NameFull: Hasenauer, Hubert IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 00344257 Numbering: – Type: volume Value: 183 Titles: – TitleFull: Remote Sensing of Environment Type: main |
| ResultId | 1 |
