Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients.
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| Title: | Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients. |
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| Authors: | Strunk, Jacob L.1 (AUTHOR) jacob.strunk@usda.gov, Bell, David M.2 (AUTHOR) david.bell@usda.gov, Gregory, Matthew J.3 (AUTHOR) matt.gregory@oregonstate.edu |
| Source: | Remote Sensing. Jul2022, Vol. 14 Issue 14, pN.PAG-N.PAG. 18p. |
| Subjects: | Environmental mapping, Forest mapping, Digital photogrammetry, Forest measurement, Aerial photogrammetry, Random forest algorithms |
| Geographic Terms: | Washington (State) |
| Abstract: | We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23/km2 when acquired for entire states), repeatedly (2–3 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (≈1 plot/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects. [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: 158297714 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Strunk%2C+Jacob+L%2E%22">Strunk, Jacob L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jacob.strunk@usda.gov</i><br /><searchLink fieldCode="AR" term="%22Bell%2C+David+M%2E%22">Bell, David M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> david.bell@usda.gov</i><br /><searchLink fieldCode="AR" term="%22Gregory%2C+Matthew+J%2E%22">Gregory, Matthew J.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> matt.gregory@oregonstate.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jul2022, Vol. 14 Issue 14, pN.PAG-N.PAG. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Environmental+mapping%22">Environmental mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+photogrammetry%22">Digital photogrammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+measurement%22">Forest measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photogrammetry%22">Aerial photogrammetry</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Washington+%28State%29%22">Washington (State)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23/km2 when acquired for entire states), repeatedly (2–3 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (≈1 plot/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects. [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/rs14143433 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: N.PAG Subjects: – SubjectFull: Environmental mapping Type: general – SubjectFull: Forest mapping Type: general – SubjectFull: Digital photogrammetry Type: general – SubjectFull: Forest measurement Type: general – SubjectFull: Aerial photogrammetry Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Washington (State) Type: general Titles: – TitleFull: Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Strunk, Jacob L. – PersonEntity: Name: NameFull: Bell, David M. – PersonEntity: Name: NameFull: Gregory, Matthew J. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 14 – Type: issue Value: 14 Titles: – TitleFull: Remote Sensing Type: main |
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