Precision, Detection Limits, and Uncertainty in Multi-Temporal Geomatic Glacier Monitoring: The Rutor Glacier Case Study.
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| Title: | Precision, Detection Limits, and Uncertainty in Multi-Temporal Geomatic Glacier Monitoring: The Rutor Glacier Case Study. |
|---|---|
| Authors: | Macelloni, Myrta Maria1 (AUTHOR) myrta.macelloni@polito.it, Giulio Tonolo, Fabio2 (AUTHOR), Di Pietra, Vincenzo1,3 (AUTHOR), Morra di Cella, Umberto1,3 (AUTHOR), Cina, Alberto1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1550. 27p. |
| Subjects: | Detection limit, Geomatics, Time series analysis, Statistical accuracy, Alpine glaciers, Climate change, Error analysis in mathematics, Mass budget (Geophysics) |
| Geographic Terms: | Italy, Alps |
| Abstract: | Highlights: What are the main findings? A statistically robust framework based on DSM differencing and Limit of Detection (LoD) analysis enables reliable identification of actual glacier elevation changes. Spatially correlated uncertainty propagation improves the accuracy and reliability of glacier volume and mass-balance estimates derived from multi-temporal photogrammetric surveys. What are the implications of the main findings? Explicit uncertainty quantification is essential to correctly interpret glacier changes and avoid misinterpretation caused by photogrammetric noise. The proposed methodology supports more reliable glacier monitoring and provides consistent geomatic products for climate-change studies and operational environmental analyses. Alpine glaciers are a vital resource for mountain regions. They provide water reserves, support energy production and tourism, and promote biodiversity. However, they are highly susceptible to climate change. In fact, they are recognised as being among the areas most affected by, and increasingly exposed to, natural hazards. The Rutor glacier in Aosta Valley, Italy, which has been the subject of repeated measurements since the 19th century and currently covers an area of around 8 km2, is undergoing significant and continuous retreat. It thus serves as an exemplary case study of the impact of climate change on the Italian Alps. This ongoing research has made it possible to conduct multi-temporal analysis of the glacier. Within this framework, Politecnico di Torino, in collaboration with ARPA Valle d'Aosta, has developed a multidisciplinary research approach focused on the characterisation of alpine environments. This study illustrates the geomatic workflows and derived geospatial products that can be used to carry out a 4D monitoring of the extent and volume of the Rutor Glacier and estimate its mass balance over the past six years. A specific focus of the study is the propagation of errors in multi-temporal analyses used to quantify glacier melt, with particular attention to the precision of input 3D geospatial data and to the Limit of Detection of elevation differences, ultimately enabling the estimation of the uncertainty associated with the derived quantities and their temporal trends. Finally, advantages and limitations in the multi-temporal and multi-sensor monitoring of glaciers are presented and discussed. [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: 194141075 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Precision, Detection Limits, and Uncertainty in Multi-Temporal Geomatic Glacier Monitoring: The Rutor Glacier Case Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Macelloni%2C+Myrta+Maria%22">Macelloni, Myrta Maria</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> myrta.macelloni@polito.it</i><br /><searchLink fieldCode="AR" term="%22Giulio+Tonolo%2C+Fabio%22">Giulio Tonolo, Fabio</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Di+Pietra%2C+Vincenzo%22">Di Pietra, Vincenzo</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Morra+di+Cella%2C+Umberto%22">Morra di Cella, Umberto</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cina%2C+Alberto%22">Cina, Alberto</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1550. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Detection+limit%22">Detection limit</searchLink><br /><searchLink fieldCode="DE" term="%22Geomatics%22">Geomatics</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Alpine+glaciers%22">Alpine glaciers</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Mass+budget+%28Geophysics%29%22">Mass budget (Geophysics)</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Italy%22">Italy</searchLink><br /><searchLink fieldCode="DE" term="%22Alps%22">Alps</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A statistically robust framework based on DSM differencing and Limit of Detection (LoD) analysis enables reliable identification of actual glacier elevation changes. Spatially correlated uncertainty propagation improves the accuracy and reliability of glacier volume and mass-balance estimates derived from multi-temporal photogrammetric surveys. What are the implications of the main findings? Explicit uncertainty quantification is essential to correctly interpret glacier changes and avoid misinterpretation caused by photogrammetric noise. The proposed methodology supports more reliable glacier monitoring and provides consistent geomatic products for climate-change studies and operational environmental analyses. Alpine glaciers are a vital resource for mountain regions. They provide water reserves, support energy production and tourism, and promote biodiversity. However, they are highly susceptible to climate change. In fact, they are recognised as being among the areas most affected by, and increasingly exposed to, natural hazards. The Rutor glacier in Aosta Valley, Italy, which has been the subject of repeated measurements since the 19th century and currently covers an area of around 8 km2, is undergoing significant and continuous retreat. It thus serves as an exemplary case study of the impact of climate change on the Italian Alps. This ongoing research has made it possible to conduct multi-temporal analysis of the glacier. Within this framework, Politecnico di Torino, in collaboration with ARPA Valle d'Aosta, has developed a multidisciplinary research approach focused on the characterisation of alpine environments. This study illustrates the geomatic workflows and derived geospatial products that can be used to carry out a 4D monitoring of the extent and volume of the Rutor Glacier and estimate its mass balance over the past six years. A specific focus of the study is the propagation of errors in multi-temporal analyses used to quantify glacier melt, with particular attention to the precision of input 3D geospatial data and to the Limit of Detection of elevation differences, ultimately enabling the estimation of the uncertainty associated with the derived quantities and their temporal trends. Finally, advantages and limitations in the multi-temporal and multi-sensor monitoring of glaciers are presented and discussed. [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=194141075 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101550 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1550 Subjects: – SubjectFull: Detection limit Type: general – SubjectFull: Geomatics Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: Alpine glaciers Type: general – SubjectFull: Climate change Type: general – SubjectFull: Error analysis in mathematics Type: general – SubjectFull: Mass budget (Geophysics) Type: general – SubjectFull: Italy Type: general – SubjectFull: Alps Type: general Titles: – TitleFull: Precision, Detection Limits, and Uncertainty in Multi-Temporal Geomatic Glacier Monitoring: The Rutor Glacier Case Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Macelloni, Myrta Maria – PersonEntity: Name: NameFull: Giulio Tonolo, Fabio – PersonEntity: Name: NameFull: Di Pietra, Vincenzo – PersonEntity: Name: NameFull: Morra di Cella, Umberto – PersonEntity: Name: NameFull: Cina, Alberto 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 |
