Abstract ID: 3.13250 | Accepted as Talk | Requested as: Talk | TBA | TBA

In the Indian Himalayan region, where permafrost is predominantly discontinuous, the impacts of climate change are especially pronounced, making the study of its dynamics crucial. Thawing permafrost can lead to slope instability, surface subsidence, and increased sedimentation, significantly affecting ecosystems. The melting of excess ice in permafrost results in slope failures, ground subsidence, and infrastructure damage due to reduced soil strength and elevated pore water pressures. We have observed that rising temperatures are accelerating the degradation of permafrost, leading to increased slope failures, and widespread geomorphic changes. To assess these impacts, we have conducted extensive field investigations and ground-penetrating radar (GPR) surveys to determine active layer thickness across permafrost regions. The active layer, which undergoes seasonal thawing and refreezing, plays a crucial role in slope stability. To further analyze these changes, we have employed remote sensing techniques combined with machine learning to monitor active layer dynamics and detect slope failures caused by active layer subsidence.