Snow Covered with Dust after Chamoli Rockslide: Inference Based on High-Resolution Satellite Data

The high-resolution multi-temporal PlanetScope image of 7 February 2021 clearly shows the fall of a large part of the Nanda Ghunti glacier (Uttarakhand) down in the base of the valley from a height of about 2000 m. The recorded seismic signals at the local seismic networks, close to the Joshimath station, show the occurrence of the fall of the first glacier block followed by another block which corresponds to the seismic signal recorded the second time. The timings of signals recorded from the seismic station are related to the visual sign of local dust in the valley after the fall of the glacier blocks at 05:01 AM and 05:28 AM UTC on 7 February 2021. In the present paper, we carried out the changes in spectral signatures of PlanetScope imageries and backscattering coefficients from Sentinel-1 synthetic aperture radar (SAR) data at six different locations. Our analysis suggests pronounced changes at all locations based on spectral signatures and backscattering coefficients due to deposition of snow dust due to the fall of the glacier blocks. Changes in surface wetness are evident after the melting of snow due to the deposition of dust in the valley

Changes in the Flood Plains and Water Quality Along the Himalayan Rivers After the Chamoli Disaster of 7 February 2021

The Himalayan regions are vulnerable to all kinds of natural hazards. On 7 February 2021, a deadly disaster occurred near the Tapovan, in Uttarakhand, Himalayas. During the event, large volume of debris along with broken glacial fragments flooded the Rishi Ganga River and washed away the nearby hydropower plants (Rishi Ganga and Tapovan), which was revealed from detailed analysis of multi spectral and bi-temporal satellite data. We present the impact of the Chamoli disaster on the flood plains and water quality of Himalayan rivers, Rishi Ganga near Tapovan, Alaknanda near Srinagar and Ganga near Haridwar and Bijnor. We used four locations along four sections of Himalayan rivers and have analysed various indices, modified normalized difference water index, normalized difference chlorophyll index, and normalized difference turbidity index, to study the changes in water quality and flood plains. On comparison of the spectral and backscattering coefficients derived from Sentinel-2 optical and Sentinel-1 synthetic aperture radar data, changes in the water quality and flood plains of the rivers were found

Chamoli Disaster: Pronounced Changes in Water Quality and Flood Plains Using Sentinel Data

The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster

Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions

Landslide Detection in the Himalayas Using Machine Learning Algorithms and U-Net

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed of five optical bands from the RapidEye satellite imagery. Dataset-2 is composed of the RapidEye optical data, and ALOS-PALSAR derived topographical data. We used a small dataset consisting of 239 samples acquired from several training zones and one testing zone to evaluate our models’ performance using the fully convolutional U-Net model, Support Vector Machines (SVM), K-Nearest Neighbor, and the Random Forest (RF). We created thirty-two different maps to evaluate and understand the implications of different sample patch sizes and their effect on the accuracy of landslide detection in the study area. The results were then compared against the manually interpreted inventory compiled using fieldwork and visual interpretation of the RapidEye satellite image. We used accuracy assessment metrics such as F1-score, Precision, Recall, and Mathews Correlation Coefficient (MCC). In the context of the Nepali Himalayas, employing RapidEye images and machine learning models, a viable patch size was investigated. The U-Net model trained with 128 × 128 pixel patch size yields the best MCC results (76.59%) with the dataset-1. The added information from the digital elevation model benefited the overall detection of landslides. However, it does not improve the model’s overall accuracy but helps differentiate human settlement areas and river sand bars. In this study, the U-Net achieved slightly better results than other machine learning approaches. Although it can depend on architecture of the U-Net model and the complexity of the geographical features in the imagery, the U-Net model is still preliminary in the domain of landslide detection. There is very little literature available related to the use of U-Net for landslide detection. This study is one of the first efforts of using U-Net for landslide detection in the Himalayas. Nevertheless, U-Net has the potential to improve further automated landslide detection in the future for varied topographical and geomorphological scenes

Geosciences / Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.(VLID)449478

A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation

Nepalese landslide information system (NELIS): A conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal

Comprehensive and sustainable landslide risk management, including the identification of areas susceptible to landslides, requires responsible organisations to collaborate efficiently. Landslide risk management efforts are often made after major triggering events, such as hazard mitigation after the 2015 Gorkha earthquake in Nepal. There is also a lack of knowledge sharing and collaboration among stakeholders to cope with major disaster events, in addition to a lack of efficiency and continuity. There should be a system to allow for landslide information to be easily updated after an event. For a variety of users of landslide information in Nepal, the availability and extraction of landslide data from a common database are a vital requirement. In this study, we investigate the requirements to propose a concept for a web-based Nepalese landslide information system (NELIS) that provides users with a platform to share information about landslide events to strengthen collaboration. The system will be defined as a web GIS (geographic information system) that supports responsible organisations in addressing and managing different user requirements of people working with landslides, thereby improving the current state of landslide hazard and risk management in Nepal. The overall aim of this study is to propose a conceptual framework and design of NELIS. A system like NELIS could benefit stakeholders involved in data collection and landslide risk management in their efforts to report and provide landslide information. Moreover, such a system would allow for detailed and structured landslide documentation and consequently provide valuable information regarding susceptibility and hazard and risk mapping. For the reporting of landslides directly to the system, a web portal is proposed. Based on field surveys, a literature review and stakeholder interviews, a structure of the landslide database and a conceptual framework for the NELIS platform are proposed

Geosciences / A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.DK W 1237-N23(VLID)355021