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@inproceedings{bib_Temp_2024, AUTHOR = {Mahesh Pathakoti, Mahalakshmi D.V, Kanchana, Krishnan Sundara Rajan, Rajashree Vinod Bothale, Prakash Chauhan, Alok Taori}, TITLE = {Temporal variability of atmospheric columnar CO2, CH4, CO and N2O concentrations using ground-based remote sensing FTIR Spectrometer}, BOOKTITLE = {Advances in Space Research}. YEAR = {2024}}

Solar absorption spectra collected using a ground-based Fourier Transform Infrared (FTIR) Spectrometer were used to retrieve the column-averaged concentrations (X) of CO2, CH4, CO, and N2O during the years 2016–2019 in the Suburban region of Shadnagar, Telangana, India. For the research period, the diurnal mean amplitudes of XCO2, XCH4, XCO, and XN2O are ∼ 13 ppmv, ∼50 ppbv, ∼13 ppbv, and ∼ 19 ppbv respectively. Strong seasonality at the study site is indicated by the seasonal variations of XCO2 and XCO of 18.3 ppmv and 39.2 ppbv, respectively. Additionally, we compared FTIR data to XCO2 detected by Orbiting Carbon Observatory-2 (OCO-2) and CO columns from Measurements of Pollution in the Troposphere (MOPITT). For the XCO2 and CO columns, the comparison research shows a mean relative bias between FTIR data and satellite data of 0.92 % and 0.71 %, respectively. The FTIR and satellite data show a Pearson correlation coefficient (r) of 0.71 (XCO2, N = 26 co-located) and 0.60 (CO columns, N = 44), respectively. The findings demonstrate a good agreement between our data and satellite data.

@inproceedings{bib_Spat_2024, AUTHOR = {Shreyash Narendra Gujar, Sara Spanddhana, Ayu Parmar, Sachin Chaudhari, Krishnan Sundara Rajan}, TITLE = {Spatio-Temporal PM Analysis for Event Detection using Low-Cost IoT Sensors}, BOOKTITLE = {Future Internet of Things and Cloud}. YEAR = {2024}}

Local activities have a significant impact on the air quality in every region. A study for two consecutive years (2021-2022) has been conducted in the Gachibowli region of Hyderabad, India, which employs spatially distributed low-cost IoT-based air quality monitors across residential and at traffic junctions and main roads measuring particulate matter (PM), temperature, and humidity. The study shows the observations during three seasons, aiming to establish correlations between the PM spikes and specific events that triggered these spikes. Detailed discussions focus on the variations in PM levels linked to traffic patterns over six months and the Diwali festival in 2021 and 2022. PM concentrations increased 2-3 times the normal range during Diwali and decreased post-Diwali. In the case of traffic-related pollution, 1.5 times higher PM levels were observed during morning and evening peak traffic hours, with significant reductions in afternoons across all months with variations in range depending on the season.

@inproceedings{bib_Thre_2023, AUTHOR = {Mahesh Pathakoti, Mahalakshmi D.V, Sreenivas Gaddamidi), S.S. Arun, R.V. Bothale, P. Chauhan, Prakash Chauhan, Krishnan Sundara Rajan, Naveen Chandra}, TITLE = {Three-dimensional view of CO2 variability in the atmosphere over the Indian region}, BOOKTITLE = {Atmospheric Research}. YEAR = {2023}}

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.

@inproceedings{bib_Unde_2023, AUTHOR = {K TARUN TEJA, Krishnan Sundara Rajan}, TITLE = {Understanding the Impact of Agricultural Fertilizer Application Over Inflows into Nagarjuna Sagar Reservoir}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}

Excessive nutrient loading into the inland water bodies and resulting algal blooms have been a major threat to the water quality of the inland water bodies such as lakes and reservoirs where the water is relatively stagnant for longer duration. To improve the conditions, it is essential to study and understand the role of the contributing factors in increasing the nutrient load coming into the water body from the watershed. In general, the excessive usage of inorganic (NPK) fertilizers are considered to be the spearhead behind the increasing nutrient output from the watershed, but their impact has not been well evaluated due to several constraints. So, this study tries to evaluate the role of the fertilizer application on the nutrient yield from the watershed into an inland water body. Nagarjuna Sagar reservoir and its contributing

@inproceedings{bib_Deca_2023, AUTHOR = {Eravelli Ankitha Reddy, Krishnan Sundara Rajan}, TITLE = {Decadal Analysis of Observed Temperature using P-MiSTIC}, BOOKTITLE = {I-GUIDE Forum}. YEAR = {2023}}

This study employed the P-MiSTIC method to analyze decadal temperature patterns in contiguous Peninsular India. A gridded temperature dataset from IMD spanning 1990-2020 was utilized, generating three decade-wise datasets. P-MiSTIC identified similar zones in decadal and overall datasets, revealing localized data characteristics. The 30-year trend for diurnal temperature range (DTR) showed a significant increase of 0.002oC/year for all zones, while the Western Himalayas and Karakoram range exhibited higher increasing trends of 0.019oC/year and 0.028oC/year, respectively. The DTR lapse rate over 30 years was quantified as -0.133oC/km, with a sharp decline observed from the first two decades (0.012 & 0.028 oC/km) to the third decade (-0.533 oC/km). Differential elevation-dependent temperature changes were observed, with the Karakoram region displaying the most prominent increase, suggesting the emergence of Elevation Dependent Warming in contiguous Peninsular India.

@inproceedings{bib_MODE_2023, AUTHOR = {Krishnan Sundara Rajan, Shreya}, TITLE = {MODELLING EVACUATION STRATEGIES UNDER DYNAMIC CONDITIONS DUE TO OBSTACLE LOCATIONS BASED ON A SEMANTIC 3D BUILDING MODELS}, BOOKTITLE = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences}. YEAR = {2023}}

@inproceedings{bib_Dete_2023, AUTHOR = {Avantika Latwal, Rehana Shaik, Krishnan Sundara Rajan}, TITLE = {Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies}, BOOKTITLE = {Environmental Monitoring and Assessment}. YEAR = {2023}}

Water quality monitoring of reservoirs is currently a signifcant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal eforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modifed Normalized Diference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifcally at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the fndings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby ofering a robust scientifc basis for efective reservoir management.

@inproceedings{bib_A_BI_2022, AUTHOR = {V Tejaswini, P. Kesava Rao, E. N. Dhanamjaya Rao, Nagaraja Ravoori, S. K. Sinha, Krishnan Sundara Rajan}, TITLE = {A BIM-Based Approach of Electrical Network Analysis and Applications Using GIS Tools}, BOOKTITLE = {International Conference on Sustainable Construction Technologies and Advancements in Civil Engineer}. YEAR = {2022}}

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.

@inproceedings{bib_Adap_2022, AUTHOR = {Aryamaan Jain, Avinash Sharma, Krishnan Sundara Rajan}, TITLE = {Adaptive & Multi-Resolution Procedural Infinite Terrain Generation with Diffusion Models and Perlin Noise}, BOOKTITLE = {Indian Conference on Computer Vision, Graphics and Image Processing}. YEAR = {2022}}

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.

@inproceedings{bib_Geos_2021, AUTHOR = {Durga Prasad Dhulipudi, Krishnan Sundara Rajan}, TITLE = {Geospatial object detection using machine learning-aviation case study}, BOOKTITLE = {Integrated Communications Navigation and Surveillance Conference}. YEAR = {2021}}

This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system.Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and then identifies and locates important visual references from imaging sensors and could help in landing and taxiing.