Leveraging time series analysis of radar coherence and normalized difference vegetation index ratios to characterize pre-failure activity of the Mud Creek landslide, California

Abstract

Abstract. Assessing landslide activity at large scales has historically been a challenging problem. Here, we present a different approach on radar coherence and normalized difference vegetation index (NDVI) analyses – metrics that are typically used to map landslides post-failure – and leverage a time series analysis to characterize the pre-failure activity of the Mud Creek landslide in California. Our method computes the ratio of mean interferometric coherence or NDVI on the unstable slope relative to that of the surrounding hillslope. This approach has the advantage that it eliminates the negative impacts of long temporal baselines that can interfere with the analysis of interferometric synthetic aperture (InSAR) data, as well as interferences from atmospheric and environmental factors. We show that the coherence ratio of the Mud Creek landslide dropped by 50 % when the slide began to accelerate 5 months prior to its catastrophic failure in 2017. Coincidentally, the NDVI ratio began a near-linear decline. A similar behavior is visible during an earlier acceleration of the landslide in 2016. This suggests that radar coherence and NDVI ratios may be useful for assessing landslide activity. Our study demonstrates that data from the ascending track provide the more reliable coherence ratios, despite being poorly suited to measure the slope's precursory deformation. Combined, these insights suggest that this type of analysis may complement traditional InSAR analysis in useful ways and provide an opportunity to assess landslide activity at regional scales.

The June 2020 Aniangzhai landslide in Sichuan Province, Southwest China: slope instability analysis from radar and optical satellite remote sensing data

“…The NDVI values are further compared with the interferometric coherence. The purpose of the comparison is to see whether some features could be obtained for early warning without complex MTI processing (Jacquemart and Tiampo 2021).…”

Pre-collapse motion of the February 2021 Chamoli rock-ice avalanche, Indian Himalaya

“…Rosen et al, 1996;Tiampo et al, 2017), or landslides (e.g. Manconi et al, 2018;Handwerger et al, 2019;Dai et al, 2020b;Mondini et al, 2021;Jacquemart and Tiampo, 2021). By measuring the shift of the radar phase relative to earlier measurements of the same features, InSAR can provide measurements of ground deformation at millimeter and centimeter scales.…”

“…Despite these drawbacks, many studies have shown that InSAR can be successfully applied to assess stability of slopes even in high relief terrain (e.g. Manconi et al, 2018;Handwerger et al, 2019;Bekaert et al, 2020;Jacquemart and Tiampo, 2021).…”

Đánh giá tác động của lớp phủ đến nhiệt độ bề mặt đất và phân bố không gian nhiệt độ tại một số tuyến đường trên địa bàn thành phố Hà Nội bằng ảnh viễn thám

“…NDVI có giá trị trong ngưỡng từ −1 đến +1 tương ứng với các giá trị mật độ phủ khác nhau của thực vật trên bề mặt. Chỉ số NDVI được xác định theo công thức sau [22]: Giá trị nhiệt độ bề mặt là hàm phụ thuộc vào giá trị của nhiệt độ sáng (Brightness Temperature) và phát xạ bề mặt (ε). Quá trình thu hồi các giá trị nhiệt độ bề mặt được thực hiện theo các bước sau: Bước 1.…”