Spatially varying WIndow based maximum likelihood feature tracking (SWIFT) method for glacier surface velocity estimations

Glacier surface velocity is an important variable for glacier dynamics studies. Estimation of accurate surface velocity from remote sensing is a challenge, especially for glaciers with no in-situ observations. To overcome this challenge, a new method for glacier feature tracking named as Spatially v...

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Bibliographic Details
Published in:Geocarto International
Main Authors: Sangita S. Tomar, Raaj Ramsankaran, Jeffrey P. Walker
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis Group 2022
Subjects:
glacier surface velocity
feature tracking approach
sar
optical
automated window size
Physical geography
GB3-5030
Canada
New Zealand
glacier*
Online Access:https://doi.org/10.1080/10106049.2022.2082556
https://doaj.org/article/e7ac05e05f6f4c7ea5fe74dfbf3364b5
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Summary:Glacier surface velocity is an important variable for glacier dynamics studies. Estimation of accurate surface velocity from remote sensing is a challenge, especially for glaciers with no in-situ observations. To overcome this challenge, a new method for glacier feature tracking named as Spatially varying WIndow based maximum likelihood Feature Tracking (SWIFT) has been proposed. This method utilizes both optical data (to automatically determine the window size [WS] using the concept of Object Based Image Analysis [OBIA]) and Synthetic Aperture Radar (SAR) data (to perform feature tracking). The proposed method uses a spatially varying WS unlike other existing softwares that cannot provide the flexibility of a spatially varying WS. The proposed method has been tested and validated at three different glaciers (South Glacier [SG], Canada; Chhota Shigri Glacier [CSG], India; and Tasman Glacier [TG], New Zealand) for which field measured data were available. The obtained results for all three glaciers showed consistent improvement in estimated velocity by SWIFT when compared with spatially fixed WS-based estimates from normalized cross correlation-based Correlation Image Analysis Software (CIAS). Considering the data availability, the proposed SWIFT method has been implemented using a variety of SAR and optical satellite data to understand its performance/effectiveness for glacier surface velocity estimation. When validated against field measurements, the results from SWIFT gave an RMSE of 12.8 m/years, 15.32 m/years and 67.1 m/years for SG, CSG and TG, respectively. Moreover, the RMSE of SWIFT estimates were observed to have an RMSE that was 19–36% lower than the best performing spatially fixed WS.

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