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Kumar, Pramod

Gold and Uranium Occurrences in Quartz-Pebble Conglomerate of Iron Ore Group, Bagiyabahal-Baratangra Area, Sundargarh District, Odisha, India

Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging

Abstract Views :208 | PDF Views:91

Authors

S. K. Singh ¹, Gaurav Jain ¹, Asfa Siddiqui ², Smruti Naik ¹, B. P. Rathore ¹, Vaibhav Garg ², Snehmani ³, Vinay Kumar ², I. M. Bahuguna ¹, S. A. Sharma ¹, Chander Shekhar ³, Praveen K. Thakur ², Kavach Mishra ², Pramod Kumar ², T. H. Painter ⁴, J. Dozier ⁵

Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
3 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
4 University of California, Los Angeles, CA, US
5 University of California, Santa Barbara, CA, US

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1182-1195

Abstract

Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth’s environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat).

AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

Keywords

AVIRIS-NG, Hyperspectral Imaging, Snow Reflectance, Super-Resolution Method, Terrain Parameters, Urban Land Cover.

Full Text

References

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Deep Learning Technique for Forecasting the Price of Cauliflower

Abstract Views :87 | PDF Views:41

Authors

Ranjit Kumar Paul ¹, Md Yeasin ¹, Pramod Kumar ², A. K. Paul ¹, H. S. Roy ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN

Source

Current Science, Vol 124, No 9 (2023), Pagination: 1065-1073

Abstract

Vegetables are the staple food in our diets. Vegetable prices are difficult to forecast because they are influenced by a variety of factors, including weather, demand and supply chain, Government policies, etc. and exhibit volatile fluctuations. Marketing of vegetables is complex, especially because of their perishability, seasonality and bulkiness. An accurate and timely forecast of vegetables is essential to help its stakeholders. Previous studies observed that traditional statistical models are unable to capture the complex behaviour of vegetable markets. In this study, a comparative assessment has been carried out among the traditional time-series model, machine learning and deep learning techniques in order to find the best-suited model. For empirical illustration, cauliflower markets have been chosen as it is one of India’s most important and popular winter. In order to identify the complexity in the price of cauliflower, the machine learning technique, i.e. artificial neural network and deep learning technique, i.e. long short-term memory model have been implemented. In addition, the traditional stochastic time-series model, i.e. autoregressive integrated moving average model, was used to compare the prediction accuracy of the above models. To this end, the moving window forecast approach was also implemented to evaluate the sensitivity of these models with respect to forecast length. It can be concluded that the deep learning model outperforms the traditional time-series model and the machine learning technique for both short- and long-term forecasting.

Keywords

Cauliflower, Deep Learning Technique, Machine Learning, Statistical Models, Vegetable Prices.

Full Text

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