Indian Institute of Remote Sensing

About: Indian Institute of Remote Sensing is a government organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Land cover & Normalized Difference Vegetation Index. The organization has 756 authors who have published 1355 publications receiving 16915 citations. The organization is also known as: Indian Photo-interpretation Institute.

Ionospheric Total Electron Content for Earthquake Precursor Detection

Abstract: Understanding earthquake precursory phenomena based on ionosphere perturbation is a fairly new field in geoscience today and has achieved promising success. Scientists across the globe are now trying to learn insight about the physical and chemical processes involved in the upper atmosphere and beyond during the earthquake preparatory period. One of such studies is based on global navigation satellite system (GNSS) observations. Global Positioning System (GPS) is currently one of the most popular global navigation satellite positioning systems widely available for such society application. GPS has led to technical revolutions in the field of applications like navigation as well as in upper atmospheric/ionospheric studies. GPS signals from the satellites encountered the ionosphere before it is captured by the receiver on the ground. In this process, the free electrons in the ionosphere affect the propagation of the signals by changing their velocity and direction of travel. A number of recent investigations have suggested that satellites and ground-based facilities like that of GNSS may detect earthquake precursors a few hours or days prior to the main event due to ionospheric perturbations induced by initiation of earthquake process. The typical phenomenological features of ionospheric precursors of strong earthquakes are summarised by Pulinets et al. (2003). The parameter of ionosphere that produces most of the effects on radio signals is the total electron content (TEC). The TEC is defined by the integral of electron density in a 1 metre square column along the signal transmission path. The ionosphere causes GPS signal delays to be proportional to the TEC along the path from the GNSS satellite to a receiver. The TEC measurements obtained from dual frequency GNSS receivers are one of the most important parameters to characterise Earth’s ionosphere. The changes in the Earth’s ionosphere can be used to derive the information about an impending earthquake. Therefore, it is very important to monitor the TEC variation due to tectonic deformation prior to an earthquake and its validation in real-world situation.

Crop Height Estimation Using RISAT-1 Hybrid-Polarized Synthetic Aperture Radar Data

Abstract: The objective of this paper was to explore the potential of hybrid-polarized (RH and RV) RISAT-1 SAR data to retrieve the height of wheat crop—an important winter crop in South Asian countries including India. The images acquired over north-west India in 2015 covered critical growth stages of wheat. The field campaigns were carried out in synchronous with the SAR passes. Considering the dominant role of underlying soil cover in the total backscatter $({\boldsymbol{\sigma }_{\boldsymbol{total}}^0})$ response from a target, we propose that refining the $\boldsymbol{\sigma }_{\boldsymbol{total}}^0$ by reducing the effect of underlying soil can significantly improve the retrieval accuracy of crop height (CH). To achieve this, we modified the existing water cloud model (WCM) to estimate soil-corrected vegetation backscatter $({\boldsymbol{\sigma }_{\boldsymbol{veg}}^0})$ . Leaf area index and interaction factor showed great potential as the vegetation descriptors in modeling $\boldsymbol{\sigma }_{\boldsymbol{total}}^0$ using WCM. A comparative analysis between the CH retrieved from $\boldsymbol{\sigma }_{\boldsymbol{total}}^0$ and $\boldsymbol{\sigma }_{\boldsymbol{veg}}^0$ using multilayer perceptron neural networks revealed the response of C -band backscatter to CH. CH was moderately correlated to $\boldsymbol{\sigma }_{\boldsymbol{total}}^0$ , but the results improved considerably with the substitution of $\boldsymbol{\sigma }_{\boldsymbol{total}}^0$ with $\boldsymbol{\sigma }_{\boldsymbol{veg}}^0$ . This holds true particularly in the early growth stages of crop growth when the vegetation cover is scarce and there is a substantial effect of soil background on the remote sensing signal. Thus, the results suggest suitability of C -band hybrid-polarized data for the assessment of CH.

The present status of subsiding land vulnerable to roof collapse in the Jharia Coalfield, India, as obtained from shorter temporal baseline C-band DInSAR by smaller spatial subset unwrapped phase profiling

Abstract: In this paper, we identified recently subsiding areas in Jharia Coalfield, Jharkhand, India from the shorter temporal baseline Radarsat-2 C-band interferometric synthetic aperture radar InSAR data pairs of 2012. Although shorter wavelength C-band differential InSAR DInSAR is more sensitive to slow deformation and better suited for higher precision land subsidence measurement, the dynamic and adverse land cover in mining areas and resulting temporal decorrelation problem poses a serious problem for DInSAR observation in mining areas. We used smaller temporal baseline data pairs and adopted InSAR coherence-guided incremental filtering with smaller moving windows to highlight the deformation fringes over temporal decorrelation noise. We identified the deformation fringes and validated them based on ground information to prepare the land subsidence map of the coalfield in 2012. Several new, previously unreported subsidence areas were detected in the present study with a total subsiding area of 6.9 km2. The recent incidence of roof collapse on 15 November 2014 at Angar Patra village in Katras region of the coalfield where 45 houses collapsed and 10 people were injured is situated in a highly subsiding vulnerable area as obtained from the present study. Due to spatial discontinuities of InSAR coherence, DInSAR phase unwrapping for the entire study area in one go did not appear feasible. To avoid this problem, we performed DInSAR processing in smaller spatial subsets and unwrapping of the subset interferograms by a ‘minimum cost flow’ algorithm. Subsequently, we plotted unwrapped phase profiles across the deformation fringes and retrieved the maximum deformation phase with respect to background phase and translated them into radar line of sight LOS displacement rates. For obtaining the average subsidence rates, we adopted InSAR coherence-weighted LOS displacement rates taking into account the contribution of each data pair as a function of DInSAR phase quality of the fringe areas. Ground-based subsidence measurements by precision levelling were conducted in four test sites that had been undergoing active underground mining during the observation period. We compared space-borne DInSAR-based subsidence rates obtained by the adopted technique with precision levelling measurements. Overall, the results are found to agree well. In the four test sites with gentle to flat topography, land subsidence occurs at slow to moderate rates due to compression of in-filled material resulting from sand stowing in underground mining, without any evidence of roof collapse. In such cases, the horizontal displacement component is less significant, and overall surface displacement occurs essentially in the vertical direction. However, we assessed the nature of subtle horizontal strain to infer relative shrinkage or dilation of the land surface which could be additive or subtractive to vertical displacement in DInSAR-based LOS displacement.

Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm

Abstract: . minimum spanning tree (MST) of a connected, undirected and weighted network is a tree of that network consisting of all its nodes and the sum of weights of all its edges is minimum among all such possible spanning trees of the same network. In this study, we have developed a new GIS tool using most commonly known rudimentary algorithm called Prim’s algorithm to construct the minimum spanning tree of a connected, undirected and weighted road network. This algorithm is based on the weight (adjacency) matrix of a weighted network and helps to solve complex network MST problem easily, efficiently and effectively. The selection of the appropriate algorithm is very essential otherwise it will be very hard to get an optimal result. In case of Road Transportation Network, it is very essential to find the optimal results by considering all the necessary points based on cost factor (time or distance). This paper is based on solving the Minimum Spanning Tree (MST) problem of a road network by finding it’s minimum span by considering all the important network junction point. GIS technology is usually used to solve the network related problems like the optimal path problem, travelling salesman problem, vehicle routing problems, location-allocation problems etc. Therefore, in this study we have developed a customized GIS tool using Python script in ArcGIS software for the solution of MST problem for a Road Transportation Network of Dehradun city by considering distance and time as the impedance (cost) factors. It has a number of advantages like the users do not need a greater knowledge of the subject as the tool is user-friendly and that allows to access information varied and adapted the needs of the users. This GIS tool for MST can be applied for a nationwide plan called Prime Minister Gram Sadak Yojana in India to provide optimal all weather road connectivity to unconnected villages (points). This tool is also useful for constructing highways or railways spanning several cities optimally or connecting all cities with minimum total road length.

A threat risk modeling framework for Geospatial Weather Information System (GWIS) a DREAD based study

Abstract: Over the years, the focus has been on protecting network, host, database and standard applications from internal and external threats. The Rapid Application Development (RAD) process makes the web application extremely short and makes it difficult to eliminate the vulnerabilities. Here we study web application risk assessment technique called threat risk modeling to improve the security of the application. We implement our proposed mechanism the application risk assessment using Microsoft's threat risk DREAD model to evaluate the application security risk against vulnerability parameters. The study led to quantifying different levels of risk for Geospatial Weather Information System (GWIS) using DREAD model.