An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery.
Saved in:
| Title: | An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery. |
|---|---|
| Authors: | Qiusheng Wu1,2 Wu.Qiusheng@epa.gov, Lane, Charles3 Lane.Charles@epa.gov, Hongxing Liu2 Hongxing.Liu@uc.edu |
| Source: | Remote Sensing. Nov2014, Vol. 6 Issue 11, p11444-11467. 24p. |
| Subjects: | Analog data, Aerial photography, Ecosystem services, Biodiversity, Wetlands |
| Geographic Terms: | United States |
| Abstract: | Effective conservation of woodland vernal pool-important components of regional amphibian diversity and ecosystem services-depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 99754884 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiusheng+Wu%22">Qiusheng Wu</searchLink><relatesTo>1,2</relatesTo><i> Wu.Qiusheng@epa.gov</i><br /><searchLink fieldCode="AR" term="%22Lane%2C+Charles%22">Lane, Charles</searchLink><relatesTo>3</relatesTo><i> Lane.Charles@epa.gov</i><br /><searchLink fieldCode="AR" term="%22Hongxing+Liu%22">Hongxing Liu</searchLink><relatesTo>2</relatesTo><i> Hongxing.Liu@uc.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2014, Vol. 6 Issue 11, p11444-11467. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Analog+data%22">Analog data</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+photography%22">Aerial photography</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystem+services%22">Ecosystem services</searchLink><br /><searchLink fieldCode="DE" term="%22Biodiversity%22">Biodiversity</searchLink><br /><searchLink fieldCode="DE" term="%22Wetlands%22">Wetlands</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Effective conservation of woodland vernal pool-important components of regional amphibian diversity and ecosystem services-depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts. [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=99754884 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs61111444 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 11444 Subjects: – SubjectFull: Analog data Type: general – SubjectFull: Aerial photography Type: general – SubjectFull: Ecosystem services Type: general – SubjectFull: Biodiversity Type: general – SubjectFull: Wetlands Type: general – SubjectFull: United States Type: general Titles: – TitleFull: An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiusheng Wu – PersonEntity: Name: NameFull: Lane, Charles – PersonEntity: Name: NameFull: Hongxing Liu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 6 – Type: issue Value: 11 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
