Chen and Colleagues Introduce Remote Sensing Method to Identify Landslides
Posted: 3/7/2024 (CSDE Research)
CSDE Affiliate Tzu-Hsin Karen Chen (Urban Design and Planning, Environmental & Occupational Health Sciences) released new research with colleagues in Science of the Total Environment, titled “Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya“. This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. Authors used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The work reveals that most landslides in the Himalayas are not new, demonstrating how “landslides follow landslides.”