Bismay Ranjan Tripathy

Bio: Bismay Ranjan Tripathy is an academic researcher from Centre for Earth Science Studies. The author has contributed to research in topics: Population & Sediment. The author has an hindex of 6, co-authored 13 publications receiving 124 citations. Previous affiliations of Bismay Ranjan Tripathy include Tsinghua University & Kumaun University.

Modeling the luminous intensity of Beijing, China using DMSP-OLS night-time lights series data for estimating population density

Abstract: Various scientific researches were conducted to monitor human activities and natural phenomena with the availability of various night time satellite data such as Defense Meteorological Satellite Program (DMPS). Population growth especially in a faster growing economy like China is an important indicator for assessing socio-economic development, urban planning and environmental management. Thus, spatial distribution of population is instrumental in assessing growth and developmental activities in Beijing city of China. The satellite observation data derived from Defense Meteorological Satellite Program (DMSP) was utilized to estimate population density through the measurement of light flux with radiometric recording. The data was calibrated using C0, C1, C2 parameters before processing. Population density of Beijing city was estimated using light volume of this calibrated data. Regression analysis between urban population and light volume revealed high correlation ( r 2 = 0.89 ) . Thus, population density can effectively be estimated using light intensity. The model used for estimating urban population density can effectively be utilized for other major cities of the world.

Estimation of Urban Population Dynamics Using DMSP-OLS Night-Time Lights Time Series Sensors Data

Abstract: Nocturnal lighting is a primary method for enabling human activities. Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) radiance calibrated nighttime light data in 1992, a variety of data sets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. DMSP uses OLS sensor, which has an oscillating scan radiometer with a broad field of view and captures images at a nominal resolution. Correlations at aggregate scales and analysis of the saturated areas of the images showed the strong relation between light intensity and the populations. The demographics of the People’s Republic of China are identified by a large population, whose population is about 19% of the total world population. Beijing, Guangdong, and Tianjin are the metropolises of China, which contribute the maximum population. Using the regression analysis, the metropolises population data can be extracted from the DMSP nighttime data ( $\text{r}^{\mathrm {\mathbf {2}}}=0.96$ ). Consequently, the DMSP imagery may prove to be useful to inform a smart interpolation program to improve maps and data sets of human population distributions in the areas of the world, where good census data may not be available or do not exist.

Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data

Abstract: Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993–2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r 2 = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Abstract: Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R 2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.

Analyzing trend in artificial light pollution pattern in India using NTL sensor's data

Abstract: Exponential growth of population and the resultant rapid rate of urbanization and industrialization in India have significantly transformed its nighttime light environment. The study makes an attempt to analyze the spatio-temporal pattern of light pollution and its causative actors in a fast-developing economy. We utilized nighttime light data from 1993 to 2013 and calibrated through linear regression. Ten patches of major changes from the whole study area were selected to assess the intensity of light pollution at regional scale. Spatial analysis of light pollution in selected patches revealed that New Delhi, Telangana, Maharashtra, Karnataka and Uttar Pradesh experienced increase in very high light pollution intensity. West Bengal, Gujarat and Tamil Nadu witnessed a remarkable change from low to high light pollution. Urban expansion, industrial development and air pollution are main drivers for increasing light pollution. Strong correlation was found between light pollution and digital numbers (DN) values at regional scale. The maps generated through Defense Meteorological Satellite Program Operational Line Scanner Night Time Light data not only helped in assessing the intensity of light pollution but also identified its causative actors.The results of study can effectively be utilized for setting priorities of environmental protection in different geographical regions at various scales.

Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)

Abstract: For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS. For an overview of USGS information products, including maps, imagery, and publications, Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this report is in the public domain, permission must be secured from the individual copyright owners to reproduce any copyrighted materials contained within this report. 10. Diagram showing the GMTED2010 layer extents (minimum and maximum latitude and longitude) are a result of the coordinate system inherited from the 1-arc-second SRTM