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Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.

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Title: Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.
Authors: Strunk, Jacob L.1 Jacob.Strunk@oregonstate.edu, Temesgen, Hailemariam1, Andersen, Hans-Erik2, Packalen, Petteri3
Source: Photogrammetric Engineering & Remote Sensing. Feb2014, Vol. 80 Issue 2, p143-150. 8p.
Subjects: Landsat satellites, Linear statistical models, LIDAR, Vegetation mapping, Forest mapping
Geographic Terms: Kenai Peninsula (Alaska)
Abstract: In this study we demonstrate that sample strips of lidar in combination with Landsat can be used to predict forest attributes more precisely than from Landsat alone. While lidar and Landsat can each be used alone in vegetation mapping, the cost of wall to wall lidar may exceed users' financial resources, and Landsat may not support the desired level of prediction precision. We compare fitted linear models and k nearest neighbors (kNN) methods to link field measurements, lidm, and Landsat. We also compare 900 m² and 8,100 m² resolutions to link lidar to Landsat. An approach with ]idar and Landsat together reduced estimates of residual variability for biomass by up to 36 percent relative to using Landsat alone. Linear models generally performed better than kNN approaches, and when linking lidar to Landsat, using 8,100 m resolution performed better than 900 m². [ABSTRACT FROM AUTHOR]
Copyright of Photogrammetric Engineering & Remote Sensing is the property of ASPRS: The Imaging & Geospatial Information Society 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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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 94485791
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.
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  Data: <searchLink fieldCode="AR" term="%22Strunk%2C+Jacob+L%2E%22">Strunk, Jacob L.</searchLink><relatesTo>1</relatesTo><i> Jacob.Strunk@oregonstate.edu</i><br /><searchLink fieldCode="AR" term="%22Temesgen%2C+Hailemariam%22">Temesgen, Hailemariam</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Andersen%2C+Hans-Erik%22">Andersen, Hans-Erik</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Packalen%2C+Petteri%22">Packalen, Petteri</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Photogrammetric+Engineering+%26+Remote+Sensing%22">Photogrammetric Engineering & Remote Sensing</searchLink>. Feb2014, Vol. 80 Issue 2, p143-150. 8p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Vegetation+mapping%22">Vegetation mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Kenai+Peninsula+%28Alaska%29%22">Kenai Peninsula (Alaska)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this study we demonstrate that sample strips of lidar in combination with Landsat can be used to predict forest attributes more precisely than from Landsat alone. While lidar and Landsat can each be used alone in vegetation mapping, the cost of wall to wall lidar may exceed users' financial resources, and Landsat may not support the desired level of prediction precision. We compare fitted linear models and k nearest neighbors (kNN) methods to link field measurements, lidm, and Landsat. We also compare 900 m² and 8,100 m² resolutions to link lidar to Landsat. An approach with ]idar and Landsat together reduced estimates of residual variability for biomass by up to 36 percent relative to using Landsat alone. Linear models generally performed better than kNN approaches, and when linking lidar to Landsat, using 8,100 m resolution performed better than 900 m². [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Photogrammetric Engineering & Remote Sensing is the property of ASPRS: The Imaging & Geospatial Information Society 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=94485791
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.14358/PERS.80.2.143-150
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 143
    Subjects:
      – SubjectFull: Landsat satellites
        Type: general
      – SubjectFull: Linear statistical models
        Type: general
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Vegetation mapping
        Type: general
      – SubjectFull: Forest mapping
        Type: general
      – SubjectFull: Kenai Peninsula (Alaska)
        Type: general
    Titles:
      – TitleFull: Prediction of Forest Attributes with Field Plots, Landsat, and a Sample of Lidar Strips: A Case Study on the Kenai Peninsula, Alaska.
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            NameFull: Andersen, Hans-Erik
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              Text: Feb2014
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              Y: 2014
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