ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements

SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK

In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE) is used for extraction of spectral features from two widely used hyperspectral datasets- Kennedy Space Centre, Florida and University of Pavia, Italy. Additionally, a convolutional neural network (CNN) is utilized to obtain spatial features. The spatial and spectral feature vectors are then stacked together to form a joint feature vector. Finally, the joint feature vector is trained using multinomial logistic regression (softmax regression) for prediction of class labels. The classification performance analysis is done through generation of the confusion matrix. The confusion matrix is then used to calculate Cohen’s Kappa (Κ) to get a quantitative measure of classification performance. The results show that the K value is higher than 0.99 for both HSI datasets

HICF: A MATLAB PACKAGE FOR HYPERSPECTRAL IMAGE CLASSIFICATION AND FUSION FOR EDUCATIONAL LEARNING AND RESEARCH

A significant surge has been observed with the development and research in remote sensing in recent years for hyperspectral applications in Earth observation. Subsequently, the development of software and tools have also experienced an unprecedented rise, both in research as well as in academia. Although commercial software and tools such as ENVI by ITT Visual Information Solutions, Boulder, CO, USA are available for visualizing and analyzing the hyperspectral images, such software are expensive. Some open source toolboxes such as the MATLAB-based Hyperspectral Image Analysis Toolbox (HIAT) are also available. However, mostly these toolboxes have not been packaged for dissemination and operation without the MATLAB software which is commercial. In this paper, we introduce the Hyperspectral Image Classification and Fusion (HICF) package which is being developed at the Geoinformatics laboratory, Department of Civil Engineering, Indian Institute of Technology Kanpur (IITK) in MATLAB that can be used by standalone installation with an open source supplementary MATLAB compiler. This software is intended to provide a collection of algorithms both conventional and those developed at the Geoinformatics laboratory that utilizes the numerical computing capability of MATLAB for the processing of hyperspectral and multispectral imagery. The HICF software comprises a simple design of the graphical user interface which can be efficiently used particularly for academic purposes

Efficient spatial-spectral computation of local planar gravimetric terrain corrections from high-resolution digital elevation models

Computation of gravimetric terrain corrections (TCs) is a numerical challenge, especially when using very high-resolution (say, ∼30 m or less) digital elevation models (DEMs). TC computations can use spatial or/and spectral techniques: Spatial domain methods are more exact but can be very time-consuming; the discrete/fast Fourier transform (D/FFT) implementation of a binomial expansion is efficient, but fails to achieve a convergent solution for terrain slopes >45°. We show that this condition must be satisfied for each and every computation-roving point pair in the whole integration domain, not just at or near the computation points. A combination of spatial and spectral methods has been advocated by some through dividing the integration domain into inner and outer zones, where the TC is computed from the superposition of analytical mass-prism integration and the D/FFT. However, there remain two unresolved issues with this combined approach: (1) deciding upon a radius that best separates the inner and outer zones and (2) analytical mass-prism integration in the inner zone remains time-consuming, particularly for high-resolution DEMs. This paper provides a solution by proposing: (1) three methods to define the radius separating the inner and outer zones and (2) a numerical solution for near-zone TC computations based on the trapezoidal and Simpson's rules that is sufficiently accurate w.r.t. the exact analytical solution, but which can reduce the computation time by almost 50 per cent

Comparison and Validation of Satellite-Derived Digital Surface/Elevation Models over India

© 2020, Indian Society of Remote Sensing. India presents among the world’s most topographically complex geomorphologies, with land elevations ranging from –2 m to + 8586 m and terrain gradients sometimes exceeding 45°. Here, we present an evaluation of four freely available digital surface models (DSMs) on a model-to-model basis, as well as a validation using independent ground-truth data from levelled benchmarks in India. The DSMs tested comprise SRTM1″, SRTM3″, ASTER1″ and Cartodem1″ [an India-only model]. Along with these four DSMs, the MERIT3″ digital elevation model (DEM) is also tested with the ground-truth data. Our results for India indicate some mismatch of these DEMs/DSMs from their claimed accuracies/precisions. All DSMs/DEMs (except for ASTER) have > 90% of pixels satisfying ± 16 m at the one-sigma level, but only in the low-lying (< 500 m) parts of India, i.e. the Gangetic plains and the Thar desert

GPS-BASED MONITORING OF CRUSTAL DEFORMATION IN GARHWAL-KUMAUN HIMALAYA

The Himalayan region has experienced a number of large magnitude earthquakes in the past. Seismicity is mainly due to tectonic activity along the thrust faults that trend parallel to the Himalayan mountain belt. In order to study the ongoing tectonic process, we report Global Positioning System (GPS) measurements of crustal deformation in the Garhwal-Kumaun Himalaya through two continuous and 21 campaign stations. We collect GPS data since 2013 and analyze with the GAMIT/GLOBK suite of postprocessing software. Our estimated surface velocities in ITRF2008, India-fixed, and Eurasia-fixed reference frame lie in the range of 42&ndash;52&thinsp;mm/yr, 1&ndash;6&thinsp;mm/yr, and 31&ndash;37&thinsp;mm/yr, respectively. We observe insignificant slip rate (&sim;&thinsp;1&thinsp;mm/yr) of HFT that indicates its locking behavior. The slip rates of MBT and MCT, however, are consistent with the seismic activity of the study region

MULTI-SENSOR GEODETIC APPROACH FOR LANDSLIDE DETECTION AND MONITORING

The lesser Himalayan region is mostly affected by landslide events occurring due to rainfall, steep slopes and presence of tectonic activity beneath, causing loss of life and property. Some critical zones in the region have encountered recurring landslides over the past and need careful investigation for better planning and rescue operations. This research work presents a geodetic framework comprising multiple sensors to monitor the Sirobagarh landslide in Uttarakhand, India, which is affected by recurring landslides. Three field visits were made to this site for geodetic data collection using Terrestrial Laser Scanner (TLS), Global Navigation Satellite System (GNSS) and Robotic Total Station (RTS). Co-registration and vegetation removal of the TLS scans corresponding to the three visits resulted in generation of three Digital Elevation Models (DEM), which were differenced to estimate temporal movement of the landslide scarp. DEM differences indicate subsidence of the landslide scarp with vertical displacement values ranging from &minus;0.05 to &minus;5.0&thinsp;m. Rainfall induced debris flow is one of the prominent reason for large displacement magnitude (&sim;5&thinsp;m) in the upper landslide scarp. Horizontal displacement estimates obtained by geodetic network analysis of six GNSS stations installed on the study site show movement towards the Alaknanda river. The maximum horizontal and vertical displacement values for the GNSS stations were 0.1305&thinsp;m and &minus;2.1315&thinsp;m respectively. Similar pattern is observed by displacement measurements of RTS target reflectors installed on a retaining wall constructed to arrest the debris flow approaorching the National Highway. The displacement estimates obtained from the sensors applied in this study indicate subsidence of the landslide scarp and surroundings. More time series observations can provide better understanding of the overall deformation process

Prevalence and burden of HBV co-infection among people living with HIV:A global systematic review and meta-analysis

Globally, in 2017 35 million people were living with HIV (PLHIV) and 257 million had chronic HBV infection (HBsAg positive). The extent of HIV-HBsAg co-infection is unknown. We undertook a systematic review to estimate the global burden of HBsAg co-infection in PLHIV. We searched MEDLINE, Embase and other databases for published studies (2002-2018) measuring prevalence of HBsAg among PLHIV. The review was registered with PROSPERO (#CRD42019123388). Populations were categorized by HIV-exposure category. The global burden of co-infection was estimated by applying regional co-infection prevalence estimates to UNAIDS estimates of PLHIV. We conducted a meta-analysis to estimate the odds of HBsAg among PLHIV compared to HIV-negative individuals. We identified 506 estimates (475 studies) of HIV-HBsAg co-infection prevalence from 80/195 (41.0%) countries. Globally, the prevalence of HIV-HBsAg co-infection is 7.6% (IQR 5.6%-12.1%) in PLHIV, or 2.7 million HIV-HBsAg co-infections (IQR 2.0-4.2). The greatest burden (69% of cases; 1.9 million) is in sub-Saharan Africa. Globally, there was little difference in prevalence of HIV-HBsAg co-infection by population group (approximately 6%-7%), but it was slightly higher among people who inject drugs (11.8% IQR 6.0%-16.9%). Odds of HBsAg infection were 1.4 times higher among PLHIV compared to HIV-negative individuals. There is therefore, a high global burden of HIV-HBsAg co-infection, especially in sub-Saharan Africa. Key prevention strategies include infant HBV vaccination, including a timely birth-dose. Findings also highlight the importance of targeting PLHIV, especially high-risk groups for testing, catch-up HBV vaccination and other preventative interventions. The global scale-up of antiretroviral therapy (ART) for PLHIV using a tenofovir-based ART regimen provides an opportunity to simultaneously treat those with HBV co-infection, and in pregnant women to also reduce mother-to-child transmission of HBV alongside HIV