Abstract

The study reports diurnal and seasonal variations of atmospheric CO2 across multiple locations in India using the dedicated ground-based observations, satellite observations and model simulations. Data from seven sites collected during different time periods are analysed to understand the role of biospheric, fossil fuel fluxes on diurnal and seasonal variations of atmospheric CO2. The study also examines the impact of land use/land cover on the variability of atmospheric CO2 concentration. Results show that CO2 mixing ratios are highest during night and lowest in the afternoon. Ponmudi station shows homogeneous daily CO2 mixing ratios because of an active ecosystem and free of boundary layer influence. A high altitude site Ooty exhibits higher diurnal and seasonal variations, possibly due to entrapped emissions from tourism and settlements, while Nagpur’s (an urban site), CO2 mixing ratios remain moderate throughout the year due to active plantations. The observed CO2 is further compared with the simulations from atmospheric chemistry-transport model. The model is able to cap- ture the observed seasonal cycle all over the study locations.

Abstract

Google Earth (GE) has been very popular since 2005 amongst remote-sensing science enthusiasts as well as administrators for its ease of use and the large archive of multiresolution temporal data covering the entire globe. With time, Google Earth has evolved to publish layers of imagery with spatial resolution estimated to be better than 30 centimeters(since GE does not publish any information related to accuracy of the imagery). Many scholars have studied the positional accuracy of Google Earth imagery from 2008 till 2018. Improvements in the positional accuracy(absolute as well as relative) were reported by many.In this study,it was attempted to understand if the CRS of Google Earth(GE) is contributing to positional inaccuracies when its imagery is assessed using reliable satellite reference data or GPS surveyed data. Relative assessment of GE location coordinates of ground check points was done using Sentinel-2B satellite imagery. Test imagery over regions of Africa, Canada, India and Australia were used and RMSE values were reported. The radial RMSE for the image check points was computed as 12.5 metres; min-max values in Easting direction were [-23.48 m,8.26 m] while min-max values in Northing direction were [-5.77m, 17.26m]. The paper focuses on planimetric mislocation/accuracy between Google Earth and Sentinel-2B and explores direct or indirect relationship between increasing UTM zone numbers and mislocation values. The paper attempts to emphasize that there is a need for the end-user/consumer to understand that, though Google Earth is undeniably the best go-to-resource with open access and high usability rating , it has its own quality issues and should be used with caution in applications where quality and reliability are at stake. GE is indeed an excellent source of ancillary information but it is expected that the end-user be aware of inherent accuracy issues of Google Earth imagery when consuming in applications ranging from as small as a geo-spatial survey by a school community to larger projects requiring high accuracy and precision like mapping, autonomous navigation, control point surveying or satellite calibration. Index Terms—Google Earth, Sentinel -2B, UTM Zone, Relative Planimetric Accuracy , Reliability , High Precision Applications

Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air ( X ) mole fractions of carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged X CO 2 , X CH 4 , and X CO covering the period from December 2020 to May 2021 are analyzed. The maximum values of X CO 2 , X CH 4 , and X CO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. X CH 4 and X CO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved X CH 4 and X CO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively.

Abstract

This paper proposes a novel adaptive multi-resolution framework for generating terrains. Our framework combines diffusion-based generative network and novel frequency separated terrain features for terrain patch generation. Additionally, we propose to leverage learnable terrain super-resolution for enhancing generated terrain patch followed by novel kernel-based blending of these patches using Perlin noise to generate infinite terrain with realistic terrain features. We provide a comprehensive quantitative and qualitative evaluation of the proposed framework.

Abstract

Building products designed at each level of detail provide single window solutions for Smart City applications. In view of this, the current study uses the tools and techniques to generate second level of detailed buildings that include building shape and height parameters. The extrusion from 2D has been performed altogether in multiple platforms and 3D data handling software. The basic inputs considered for extracting the polygon shapefiles are obtained from high-resolution stereo pair satellite images acquired using Cartosat-I imagery. Eight ground control points are collected using high precision Differential Global Positioning System having millimeters of accuracy. The image adjustments and Digital Surface Model are generated by using the process and concepts of Photogrammetry such as block creation using Rational Polynomial Coefficient text files, internal geometry set up such as interior and exterior orientation, Tie point generation and control point adjustments, and Automated Terrain Extraction. The thus obtained terrain model is overlaid on ortho photograph and the building heights are extracted and compared by using spatial profile graph as well as stereoscopic visualization methods. The height-derived attribute tables are attached to corresponding building footprints and are visualized in different GIS platforms.

Abstract

Building Information Modeling is a sophisticated technology possessing intellectual tools that help planners and architects to design sustainable buildings and carryout performance analysis based on various aspects. Apart from the existing building elements, a network connecting each building component is of vital importance in order to generate organized plans. Building Information Modeling when embedded with Geographic Information System delivers versatile applications in the world of both Geo Spatial field of studies as well as Architecture, Engineering and Construction Industry. The current research is one such attempt to understand the power of Geographic Information System tools in building models especially in solving complicated network connections. Designing the electrical network and planning the circuitry information in such a way so as to avoid faulty or poor connections in between the inter circuit elements is successfully achieved with the help of this integration. Initially, building model is generated to the high level of detail and electrical fixtures are organized with in a power distribution system using Revit Architecture. The virtual network wiring made in Building model is physically connected using three dimensional Polyline features in Geographic Information System platform and the created Network of electrical elements is utilized for Geometric Network Analysis.

Abstract

The entire Himalayan arc is predicted to produce a series of significant earthquakes and the subsequent great earthquakes of magnitude 8.0 and higher. A substantial amount of rock tunneling is being carried out in the Himalayan region to meet the growing demand for transportation, power, and other infrastructure projects. Earthquake forces influence the final design of the tunnel, which requires further strengthening of the concrete lining and improvement in tunnel support. Because of increasing importance, it is essential to combine the dynamic forces and displacements produced by seismic ground movements into the design stage for tunnels. In this paper, a numerical analysis has been carried out to assess the seismic performance of a proposed hydropower tunnel of 8.8m diameter (horseshoe-shape) in Uttarakhand, India. The impact of earthquakes on underground structures such as tunnels is often considered to be insignificant. However, the results of this study show how that stress from seismic loads can be damaging to the stability of the tunnel. In this study, a pseudo-static approach was adopted to assess the impact of the earthquake on the tunnel lining for the sections located in different rocks namely slate (Q=3.4), quartzite (Q=6), and dolomitic limestone (Q=5.2), respectively. Pseudo-static analysis findings suggest there is a 35% increase in the lining forces for an impact of earthquake for the tunnel section situated in Slate. Furthermore, displacements and damage-prone areas are determined to assess the damage to the tunnel, which could be helpful for rapid evaluations of potential future damage.

Abstract

This paper proposes a novel methodology of analyzing mobile Internet of Things (IoT) data by performing spatial and anthropogenic factor-based thematic interactions with it to retrieve interesting patterns that account for the data variation. In order to test out this methodology, a study is conducted by collecting Particulate Matter (PM) data across India using a mobile IoT node, and look into the neighbouring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. By performing the spatial factor analysis on the mobile IoT data, we evaluated the influence of human activities on PM10 levels, most significantly observed for 0