Evidence of Instability in Previously-Mapped Landslides as Measured Using GPS, Optical, and SAR Data between 2007 and 2017: A Case Study in the Portuguese Bend Landslide Complex, California

Analyzing Joshimath's sinking: causes, consequences, and future prospects with remote sensing techniques

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022-2023 using a PSInSAR-based approach, varies from - 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas - ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016-2017, 2018-2019, and 2020-2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022-2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022-2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022-2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to - 13.13 mm/year (2022-2023). Towards the south-east, the town experienced rapid land subsidence, - 13.13 mm/year to - 5 mm/year (2022-2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

Automatic Extraction of Potential Landslides by Integrating an Optical Remote Sensing Image with an InSAR-Derived Deformation Map

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention.

Retrieval of the planetary boundary layer height from lidar measurements by a deep-learning method based on the wavelet covariance transform

Understanding and characterization of the planetary boundary layer (PBL) are of great importance in terms of air pollution management, weather forecasting, modelling of climate change, etc. Although many lidar-based approaches have been proposed for the retrieval of the PBL height (PBLH) in case studies, development of a robust lidar-based algorithm without human intervention is still of great challenging. In this work, we have demonstrated a novel deep-learning method based on the wavelet covariance transform (WCT) for the PBLH evaluation from atmospheric lidar measurements. Lidar profiles are evaluated according to the WCT with a series of dilation values from 200 m to 505 m to generate 2-dimensional wavelet images. A large number of wavelet images and the corresponding PBLH-labelled images are created as the training set for a convolutional neural network (CNN), which is implemented based on a modified VGG16 (VGG - Visual Geometry Group) convolutional neural network. Wavelet images obtained from lidar profiles have also been prepared as the test set to investigate the performance of the CNN. The PBLH is finally retrieved by evaluating the predicted PBLH-labelled image and the wavelet coefficients. Comparison studies with radiosonde data and the Micro-Pulse-Lidar Network (MPLNET) PBLH product have successfully validated the promising performance of the deep-learning method for the PBLH retrieval in practical atmospheric sensing.

Adaptive digital filter for the processing of atmospheric lidar signals measured by imaging lidar techniques

The lidar signal measured by the atmospheric imaging lidar technique is subject to sunlight background noise, dark current noise, and fixed pattern noise (FPN) of the image sensor, etc., which presents quite different characteristics compared to the lidar signal measured by the pulsed lidar technique based on the time-of-flight principle. Enhancing the signal-to-noise ratio (SNR) of the measured lidar signal is of great importance for improving the performance of imaging lidar techniques. By carefully investigating the signal and noise characteristics of the lidar signal measured by a Scheimpflug lidar (SLidar) based on the Scheimpflug imaging principle, we have demonstrated an adaptive digital filter based on the Savitzky-Golay (S-G) filter and the Fourier analysis. The window length of the polynomial of the S-G filter is automatically optimized by iteratively examining the Fourier domain frequency characteristics of the residual signal between the filtered lidar signal and the raw lidar signal. The performance of the adaptive digital filter has been carefully investigated for lidar signals measured by a SLidar system under various atmospheric conditions. It has been found that the optimal window length for near horizontal measurements is concentrated in the region of 90-150, while it varies mainly in the region of 40-100 for slant measurements due to the frequent presence of the peak echoes from clouds, aerosol layers, etc. The promising result has demonstrated great potential for utilizing the proposed adaptive digital filter for the lidar signal processing of imaging lidar techniques in the future.

Mini-Scheimpflug lidar system for all-day atmospheric remote sensing in the boundary layer

Development of a lightweight, low-cost, easy-to-use and low-maintenance lidar technique has been of great interest for atmospheric aerosol remote sensing in recent years and remains a great challenge. In this work, an 808 nm mini-Scheimpflug lidar (SLidar) system with about 450 mm separation between the transmitter and the receiver has been developed by employing a 114 mm aperture Newtonian telescope (F4). System performances, such as the beam characteristic, the range resolution, and the signal-to-noise ratio of the lidar signal, have been carefully investigated. Despite employing a small receiving aperture, all-day measurements were still feasible with about a one-minute signal averaging for both the horizontal urban area monitoring and the slant atmospheric sounding in the boundary layer. The lidar signal in the region of 29-50 m with a scattering angle less than 179.5° could be slightly underestimated due to the variation of the phase function. The extinction coefficient evaluated in the region between 29 and 2000 m according to the Klett method agreed well with the concentrations of particulate matters for both horizontal and slant measurements. The promising result demonstrated in this work has shown great potential to employ the robust mini-SLidar system for atmospheric monitoring in the boundary layer.

Comparison studies of the Scheimpflug lidar technique and the pulsed lidar technique for atmospheric aerosol sensing

The Scheimpflug lidar (SLidar) technique has been recently developed for various remote sensing applications, where the lidar signal is detected by an image sensor according to the Scheimpflug principle instead of the time-of-flight principle. Comparison studies between the SLidar technique and the conventional pulsed lidar technique are crucial for understanding the principle as well as the measurement results of the SLidar technique. In this work, a 520-nm Scheimpflug lidar system and a 532-nm pulsed lidar system have been developed for comparison studies. Atmospheric remote measurements as well as statistical analysis have been carried out on a near-horizontal path and on a slant direction with an elevation angle of 30$^\circ $^∘. The temporal-spatial variations of the atmospheric backscattering maps measured by the 520-nm SLidar system and the 532-nm pulsed lidar system generally agreed well. The median extinction coefficient measured by the SLidar and the pulsed techniques has shown similar temporal evolution during the near-horizontal comparison study, and a correlation coefficient of 0.99 has been achieved through statistical analysis on all lidar measurements. Moreover, the root-mean-square error (RMSE) ratio for each extinction coefficient profile has also been evaluated, and the mean value of the RMSE ratio for all lidar measurements was about 11% in homogeneous atmospheric conditions. During slant comparison studies, the RMSE ratio between the SLidar curve and the pulsed lidar curve was less than 5% in the region of 0.5-2 km, and it generally increased with the increase of measurement distance, primarily due to the decreased range resolution of the SLidar technique. The promising results suggested that the SLidar technique, featuring a short blind range, could be suitable for aerosol sensing, particularly in the planetary boundary layer.