More Like this

1. Mapping the Extent and Timing of Water Tracks with High Temporal Resolution Satellite Imagery in the McMurdo Dry Valleys, Antarctica: Can Subsurface Melt Conditions be Deduced from Orbital Imagery for RSL-Analogs?

   Kuentz, L. ; Levy, J. ; Salvatore, M. ( March 2021 , 52nd Lunar and Planetary Science Conference)

   null (Ed.)

   Abstract presented at the 52nd Lunar and Planetary Science Conference. 

   more » « less
2. Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery

   https://doi.org/10.3390/rs14174132  

   Witharana, Chandi ; Udawalpola, Mahendra R. ; Liljedahl, Anna K. ; Jones, Melissa K. ; Jones, Benjamin M. ; Hasan, Amit ; Joshi, Durga ; Manos, Elias ( September 2022 , Remote Sensing)

   Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique capacities in capturing the morphological dynamics of RTSs. The central goal of this study is to develop a deep learning convolutional neural net (CNN) model (a UNet-based workflow) to automatically detect and characterize RTSs from VHSR imagery. We aimed to understand: (1) the optimal combination of input image tile size (array size) and the CNN network input size (resizing factor/spatial resolution) and (2) the interoperability of the trained UNet models across heterogeneous study sites based on a limited set of training samples. Hand annotation of RTS samples, CNN model training and testing, and interoperability analyses were based on two study areas from high-Arctic Canada: (1) Banks Island and (2) Axel Heiberg Island and Ellesmere Island. Our experimental results revealed the potential impact of image tile size and the resizing factor on the detection accuracies of the UNet model. The results from the model transferability analysis elucidate the effects on the UNet model due the variability (e.g., shape, color, and texture) associated with the RTS training samples. Overall, study findings highlight several key factors that we should consider when operationalizing CNN-based RTS mapping over large geographical extents. 

   more » « less
3. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types

   https://doi.org/10.3390/jimaging6120137  

   Bhuiyan, Md Abul ; Witharana, Chandi ; Liljedahl, Anna K. ( December 2020 , Journal of Imaging)

   We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.

    

   more » « less
4. Mapping and characterizing Arctic beaded streams through high resolution satellite imagery

   https://doi.org/10.1016/j.rse.2022.113378  

   Harlan, Merritt E. ; Gleason, Colin J. ; Flores, Jonathan A. ; Langhorst, Theodore M. ; Roy, Samapriya ( February 2023 , Remote Sensing of Environment)
5. Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection

   https://doi.org/10.1016/j.isprsjprs.2020.10.010  

   Witharana, Chandi ; Bhuiyan, Md Abul ; Liljedahl, Anna K. ; Kanevskiy, Mikhail ; Epstein, Howard E. ; Jones, Benjamin M. ; Daanen, Ronald ; Griffin, Claire G. ; Kent, Kelcy ; Ward Jones, Melissa K. ( December 2020 , ISPRS Journal of Photogrammetry and Remote Sensing)

   null (Ed.)