Browsing by Author "Akanksha Bhardwaj"

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A review on remotely sensed land surface temperature anomaly as an earthquake precursor
(Elsevier B.V., 2017) Anshuman Bhardwaj; Shaktiman Singh; Lydia Sam; P.K. Joshi; Akanksha Bhardwaj; F. Javier Martín-Torres; Rajesh Kumar
The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors. © 2017 Elsevier B.V.
LiDAR remote sensing of the cryosphere: Present applications and future prospects
(Elsevier Inc., 2016) Anshuman Bhardwaj; Lydia Sam; Akanksha Bhardwaj; F. Javier Martín-Torres
The cryosphere consists of frozen water and includes lakes/rivers/sea ice, glaciers, ice caps/sheets, snow cover, and permafrost. Because highly reflective snow and ice are the main components of the cryosphere, it plays an important role in the global energy balance. Thus, any qualitative or quantitative change in the physical properties and extents of the cryosphere affects global air circulation, ocean and air temperatures, sea level, and ocean current patterns. Due to the hardships involved in collecting ground control points and field data for high alpine glaciers or vast polar ice sheets, several researchers are currently using remote sensing. Satellites provide an effective space-borne platform for remotely sensing frozen areas at the global and regional scales. However, satellite remote sensing has several constraints, such as limited spatial and temporal resolutions and expensive data acquisition. Therefore, aerial and terrestrial remote sensing platforms and sensors are needed to cover temporal and spatial gaps for comprehensive cryospheric research. Light Detection and Ranging (LiDAR) antennas form a group of active remote sensors that can easily be deployed on all three platforms, i.e., satellite, aerial, and terrestrial. The generation of elevation data for glacial and snow-covered terrain from photogrammetry requires high contrast amongst various reflective surfaces (ice, snow, firn, and slush). Conventional passive optical remote sensors do not provide the necessary accuracy, especially due to the unavailability of reliable ground control points. However, active LiDAR sensors can fill this research gap and provide high-resolution and accurate Digital Elevation Models (DEMs). Due to the obvious advantages of LiDAR over conventional passive remote sensors, the number of LiDAR-based cryospheric studies has increased in recent years. In this review, we highlight studies that have utilised LiDAR sensors for the cryospheric research of various features, such as snow cover, polar ice sheets and their atmospheres, alpine glaciers, and permafrost. Because this technology shows immense promise for applications in future cryospheric research, we also emphasise the prospects of utilising LiDAR sensors. In this paper, a large compilation of relevant references is presented to allow readers to explore particular topics of interest. © 2016 Elsevier Inc.
MODIS-based estimates of strong snow surface temperature anomaly related to high altitude earthquakes of 2015
(Elsevier Inc., 2017) Anshuman Bhardwaj; Shaktiman Singh; Lydia Sam; Akanksha Bhardwaj; F. Javier Martín-Torres; Atar Singh; Rajesh Kumar
The high levels of uncertainty associated with earthquake prediction render earthquakes some of the worst natural calamities. Here, we present our observations of MODerate resolution Imaging Spectroradiometer (MODIS)-derived Land Surface Temperature (LST) anomaly for earthquakes in the largest tectonically active Himalayan and Andean mountain belts. We report the appearance of fairly detectable pre-earthquake Snow Surface Temperature (SST) anomalies. We use 16 years (2000–2015) of MODIS LST time-series data to robustly conclude our findings for three of the most destructive earthquakes that occurred in 2015 in the high mountains of Nepal, Chile, and Afghanistan. We propose the physical basis behind higher sensitivity of snow towards geothermal emissions. Although the preliminary appearance of SST anomalies and their amplitudes vary, we propose employing a global-scale monitoring system for detecting and studying such spatio-temporal geophysical signals. With the advent of improved remote sensors, we anticipate that such efforts can be another step towards improved earthquake predictions. © 2016 Elsevier Inc.