TY - JOUR
T1 - Spatially varying WIndow based maximum likelihood feature tracking (SWIFT) method for glacier surface velocity estimations
AU - Tomar, Sangita S.
AU - Ramsankaran, Raaj
AU - Walker, Jeffrey P.
N1 - Funding Information:
Authors thank both the anonymous reviewers for their comments, which helped us to improve the quality of the manuscript. We acknowledge the support provided by the IITB-Monash Research Academy to engage IIT Bombay and Monash University in carrying out this work. We are thankful to the European Space Agency and JAXA for providing freely accessible SAR datasets. We would like to thank USGS and ISRO for providing optical datasets used in this study. Last but not the least, we would like to acknowledge Dr. Farooq Azam and Dr. Gwenn Flowers for providing data and useful information about the study glaciers, i.e. Chhota Shigri and South Glacier, respectively.
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - 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.
AB - 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.
KW - automated window size
KW - feature tracking approach
KW - Glacier surface velocity
KW - optical
KW - SAR
UR - http://www.scopus.com/inward/record.url?scp=85131781174&partnerID=8YFLogxK
U2 - 10.1080/10106049.2022.2082556
DO - 10.1080/10106049.2022.2082556
M3 - Article
AN - SCOPUS:85131781174
SN - 1010-6049
VL - 37
SP - 13769
EP - 13796
JO - Geocarto International
JF - Geocarto International
IS - 26
ER -