Evaluating Field Sampling Design and LiDAR-Based Approaches for Woody Vegetation Assessment in Reclaimed Wellsite Certification.
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| Title: | Evaluating Field Sampling Design and LiDAR-Based Approaches for Woody Vegetation Assessment in Reclaimed Wellsite Certification. |
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| Authors: | Van Dongen, Angeline1 (AUTHOR), Movchan, Dmytro1 (AUTHOR), Selvaraj, Charumitha1 (AUTHOR), Degenhardt, Dani1 (AUTHOR) dani.degenhardt@nrcan-rncan.gc.ca |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1464. 28p. |
| Subjects: | LIDAR, Statistical sampling, Remote sensing, Woody plants, Field research, Restoration ecology |
| Geographic Terms: | Alberta |
| Abstract: | Highlights: What are the main findings? Subjective plot placement overestimated woody stem density compared to random plots at sites with heterogenous vegetation recovery. LiDAR data provided valuable spatial information but underestimated woody stem density, particularly at sites with high stem density. What are the implications of the main findings? Random sampling provides a more representative assessment of site conditions, if site-level rather than plot-level criteria are emphasized. LiDAR data may be a useful preliminary recovery screening tool or used to inform targeted field sampling. Responsible resource development in Alberta requires the reclamation of disturbed lands to achieve equivalent land capability to pre-disturbance conditions. Vegetation assessments on reclaimed wellsites and oil sand exploration (OSE) sites currently rely on plots placed in areas deemed representative using professional judgement, which may introduce sampling bias. This study compared woody vegetation attributes derived from conventionally placed plots with those from randomly placed plots on certified reclaimed sites. Furthermore, increased sampling intensity was evaluated on a subset of sites. Site-level plot-based estimates were also compared with estimates from uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) and airborne laser scanning (ALS). Woody stem density and height estimates from random and judgment-based plots were generally comparable; however, on sites with spatially heterogeneous recovery, judgment-based placement tended to overestimate woody stem density relative to larger-area sampling. LiDAR data captured spatial patterns of woody vegetation but underestimated stem densities, particularly on high-density, clustered sites. [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: 194140989 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating Field Sampling Design and LiDAR-Based Approaches for Woody Vegetation Assessment in Reclaimed Wellsite Certification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Van+Dongen%2C+Angeline%22">Van Dongen, Angeline</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Movchan%2C+Dmytro%22">Movchan, Dmytro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Selvaraj%2C+Charumitha%22">Selvaraj, Charumitha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Degenhardt%2C+Dani%22">Degenhardt, Dani</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dani.degenhardt@nrcan-rncan.gc.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1464. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Woody+plants%22">Woody plants</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br /><searchLink fieldCode="DE" term="%22Restoration+ecology%22">Restoration ecology</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Alberta%22">Alberta</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Subjective plot placement overestimated woody stem density compared to random plots at sites with heterogenous vegetation recovery. LiDAR data provided valuable spatial information but underestimated woody stem density, particularly at sites with high stem density. What are the implications of the main findings? Random sampling provides a more representative assessment of site conditions, if site-level rather than plot-level criteria are emphasized. LiDAR data may be a useful preliminary recovery screening tool or used to inform targeted field sampling. Responsible resource development in Alberta requires the reclamation of disturbed lands to achieve equivalent land capability to pre-disturbance conditions. Vegetation assessments on reclaimed wellsites and oil sand exploration (OSE) sites currently rely on plots placed in areas deemed representative using professional judgement, which may introduce sampling bias. This study compared woody vegetation attributes derived from conventionally placed plots with those from randomly placed plots on certified reclaimed sites. Furthermore, increased sampling intensity was evaluated on a subset of sites. Site-level plot-based estimates were also compared with estimates from uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) and airborne laser scanning (ALS). Woody stem density and height estimates from random and judgment-based plots were generally comparable; however, on sites with spatially heterogeneous recovery, judgment-based placement tended to overestimate woody stem density relative to larger-area sampling. LiDAR data captured spatial patterns of woody vegetation but underestimated stem densities, particularly on high-density, clustered sites. [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/rs18101464 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1464 Subjects: – SubjectFull: LIDAR Type: general – SubjectFull: Statistical sampling Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Woody plants Type: general – SubjectFull: Field research Type: general – SubjectFull: Restoration ecology Type: general – SubjectFull: Alberta Type: general Titles: – TitleFull: Evaluating Field Sampling Design and LiDAR-Based Approaches for Woody Vegetation Assessment in Reclaimed Wellsite Certification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Van Dongen, Angeline – PersonEntity: Name: NameFull: Movchan, Dmytro – PersonEntity: Name: NameFull: Selvaraj, Charumitha – PersonEntity: Name: NameFull: Degenhardt, Dani IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
