Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India.

Detection and mapping of landslides is one of the most important techniques used for reducing the impact of natural disasters especially in the Himalaya, owing to its high amount of tectonic deformation, seismicity, and unfavorable climatic conditions. Moreover, the northeastern part of the Himalaya, severely affected by landslides every monsoon, is poorly studied. The information on the inventories is inhomogeneous and lacking. In this context, satellite-based earth observation data, which has significantly advanced in the last decade and often serves as a potential source for data collection, monitoring, and damage assessment for disasters in a short time span, has been implemented. Keeping in mind the above framework, this study aims to exploit the potentials of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery for identifying new landslides in vegetated and hilly areas of the northeastern part of India. In order to assess the potentials of our data and methodology, a landslide event which occurred on 13 August 2016 13:30 h (IST) in North Sikkim, India, triggered due to rainfall has been explored in detail. The landslide also resulted in the formation of a lake, 2.2 km in length and 290 m in width. Difficulty in procurement of cloud-free datasets immediately after the event led us to the use of Sentinel-1 SAR backscatter data, to assess its potential for this purpose. It is observed that the potential of SAR amplitude imagery is limited to different aspects as per the sensor look direction during the mode of acquisition. Furthermore, the present study also incorporates a change detection algorithm to evaluate the performance of the Sudden Landslide Identification Product (SLIP) model to identify new landslides using Sentinel-2 multispectral imagery. Overall, the results exhibit that integrated usage of both optical and SAR amplitude imagery may provide a plethora of information for identification and mapping of new landslides for damage assessment and early warning. All the above results combined together suggest this method for rapid identification of landslides in the Himalayan terrain with special emphasis on the northeastern part of the Himalaya. The automation of this method for future operational usage is also suggested.

Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data.

Satellite-based flood assessment for extent and severity is very crucial input before, during, and after a flood event has occurred. Though optical remote sensing data has been widely used for flood hazard mapping, Synthetic Aperture Radar (SAR) data is preferred for detecting inundated areas and providing reliable information during a flood event due to its capability to operate in all weather and day/night time. Availability of cloud-free optical images during monsoon over north eastern India is a rarity. SAR data also has the advantage of detecting inundation under vegetated areas due to its penetration capabilities and sensitivity to soil moisture. The present study is an attempt to use SAR data for flood monitoring of the Kaziranga National Park (KNP) during monsoon, 2017. Every year, animals are washed away by floods and most of them migrate to higher grounds in order to escape from the rising water levels. Flooding events are common in the study area during the monsoon season due to high rainfall and its close proximity to the Brahmaputra River. Dual polarized (VV and VH) Sentinel-1 SAR images obtained for the entire monsoon period in 2017 were used to create inundation maps of the KNP. Two flood waves were observed in July and August, the second of which is considered to be one of the worst flooding events inundating most areas of the park. The use of SAR data for monitoring of flood events can be very crucial for identifying locations for building animal shelters and finding routes for rescue and relief operations during the disaster.