Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder

Lingcao Huang

Lingcao Huang will work with Kevin Schaefer, Kristy Tiampo, and Michael Willis on a project using machine learning to automatically quantify the development of retrogressive thaw slumps (RTS) whose formation is due to the thawing the ice-rich permafrost. Retrogressive thaw slumps are the most dynamic landforms in cold regions and in which thaw of ice-rich permafrost on slopes causes mass-wasting of soil and vegetation. As reported by many local studies, their number and affected areas have increased dramatically in recent decades. However, their spatial distribution as well as development are poorly quantified and understood because they are widespread but localized features.  By applying machine learning technology, especially, deep learning, Lingcao will develop a method to delineate and quantify RTS occurrence and dynamics in northern Alaska. The objectives are to provide a tool to monitor RTS dynamics in larger areas and also advance the understanding of permafrost degradation in northern Alaska related to external controlling factors such as climatic variables.

View Publications

  • Xia Z; Huang L; Fan C; Jia S; Lin Z; Liu L; Luo J; Niu F; Zhang T. (Aug 2022). Retrogressive thaw slumps along the Qinghai-Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics. Earth System Science Data , 14(9), 3875-3887. 10.5194/essd-14-3875-2022
  • Huang L; Lantz TC; Fraser RH; Tiampo KF; Willis MJ; Schaefer K. (Jun 2022). Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic. Remote Sensing , 14(12), 2747-2747. 10.3390/rs14122747
  • Tiampo KF; Huang L; Simmons C; Woods C; Glasscoe MT. (May 2022). Detection of Flood Extent Using Sentinel-1A/B Synthetic Aperture Radar: An Application for Hurricane Harvey, Houston, TX. Remote Sensing , 14(9), 2261-2261. 10.3390/rs14092261
  • Huang L; Liu L; Luo J; Lin Z; Niu F. (Oct 2021). Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images. International Journal of Applied Earth Observation and Geoinformation , 102, 102399-102399. 10.1016/j.jag.2021.102399
  • Parsekian AD; Chen RH; Michaelides RJ; Sullivan TD; Clayton LK; Huang L; Zhao Y; Wig E; Moghaddam M; Zebker H. (Jul 2021). Validation of Permafrost Active Layer Estimates from Airborne SAR Observations. Remote Sensing , 13(15), 2876-2876. 10.3390/rs13152876
  • Huang L; Baud P; Cordonnier B; Renard F; Liu L; Wong T-F. (Dec 2019). Synchrotron X-ray imaging in 4D: Multiscale failure and compaction localization in triaxially compressed porous limestone. Earth and Planetary Science Letters , 528. 10.1016/j.epsl.2019.115831
  • Zhang E; Liu L; Huang L. (Jun 2019). Automatically delineating the calving front of Jakobshavn Isbrae from multitemporal TerraSAR-X images: a deep learning approach. The Cryosphere , 13(6), 1729-1741. 10.5194/tc-13-1729-2019
  • Huang L; Liu L; Jiang L; Zhang T. (Dec 2018). Automatic Mapping of Thermokarst Landforms from Remote Sensing Images Using Deep Learning: A Case Study in the Northeastern Tibetan Plateau. Remote Sensing , 10(12). 10.3390/rs10122067