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Aggarwal, S. P.

Integrated Approach for Snowmelt Run-Off Estimation Using Temperature Index Model, Remote Sensing and GIS

Abstract Views :242 | PDF Views:84

Authors

S. P. Aggarwal ¹, Praveen K. Thakur ¹, Bhaskar R. Nikam ¹, Vaibhav Garg ¹

Affiliations
1 Water Resources Department, Indian Institute of Remote Sensing Dehradun, 4-Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 106, No 3 (2014), Pagination: 397-407

Abstract

The snow and glacier melt run-off is one of the most important sources of freshwater for the perennial Himalayan rivers. The water from these rivers sustains billions of people in South Asia, especially during lean season. The study has been done to integrate temporal snow cover area (SCA) and digital elevation model (DEM) derived from satellite remote sensing data with Geographic Information System (GIS) and finally into temperature index-based snowmelt run-off estimation model. The study area for snowmelt run-off estimation is part of head reach sub-basins of Ganga river, i.e. Alakhnanda and Bhagirathi river basins up to Joshimath and Uttarkashi respectively. The temporal SCA (2002-07 for Bhagirathi river and 2000, 2008 for Alakhnanda river) was derived from remote sensing data and DEM was used to find elevation zones and aspect maps. Snowmelt run-off model (SRM) is a temperature index-based snowmelt run-off simulation model, which has been used in this study for simulating snowmelt run-off. The daily hydro meteorological data from India Meteorological Department and Central Water Commission were used for estimating snowmelt. Overall accuracy of SRM for Alakhnanda river in terms of coefficient of correlation (R²) is 0.84-0.90 for years 2000 and 2008, and 0.74-0.84 in Bhagirathi river for 2002-2007.

Keywords

Remote Sensing, Snowmelt Run-Off, Snow Cover Area, Temperature Index Model.

Full Text

Satellite-Based Mapping and Monitoring of Heavy Snowfall in North Western Himalaya and its Hydrologic Consequences

Abstract Views :201 | PDF Views:79

Authors

Bhaskar R. Nikam ¹, Vaibhav Garg ¹, Prasun K. Gupta ¹, Praveen K. Thakur ¹, A. Senthil Kumar ¹, Arpit Chouksey ¹, S. P. Aggarwal ¹, Pankaj Dhote ¹, Saurabh Purohit ¹

Affiliations
1 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 113, No 12 (2017), Pagination: 2328-2334

Abstract

Snow cover is one of the most important land surface parameters in global water and energy cycle. Large area of North West Himalaya (NWH) receives precipitation mostly in the form of snow. The major share of discharge in rivers of NWH comes from snow and glacier melt. The hydrological models, used to quantify this runoff contribution, use snow-covered area (SCA) along with hydro-meteorological data as essential inputs. In this context, information about SCA is essential for water resource management in NWH region. Regular mapping and monitoring of snow cover by traditional means is difficult due to scarce snow gauges and inaccessible terrain. Remote sensing has proven its capability of mapping and monitoring snow cover and glacier extents in these area, with high spatial and temporal resolution. In this study, 8-day snow cover products from MODIS, and 15-daily snow cover fraction product from AWiFS were used to generate long-term SCA maps (2000–2017) for entire NWH region. Further, the long term variability of 8-daily SCA and its current status has been analysed. The SCA mapped has been validated using AWiFS derived SCA. The analysis of current status (2016–17) of SCA has indicated that the maximum extent of snow cover in NWH region in last 17 years. In 2nd week of February 2017, around 67% of NWH region was snow covered. The comparison of SCA during the 1st week of March and April in 2016–17 against 2015–16 indicates 7.3% and 6.5%, increased SCA in current year. The difference in SCA during 1st week of March 2017 and 1st week of April 2017 was observed to be 14%, which indicates that the 14% SCA has contributed to the snow melt during this period. The change in snow water equivalent retrieved using SCATSAT-1 data also validates this change in snow volume.

Keywords

AWiFS, MOD10A2, North Western Himalaya, Snow Cover Area, SCATSAT-1.

Full Text

References

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Space technology support for development of agriculture in the North Eastern Region of India – scope and challenges

Abstract Views :142 | PDF Views:79

Authors

B. K. Handique ¹, C. Goswami ¹, P. T. Das ¹, J. Goswami ¹, P. Jena ¹, F. Dutta ¹, D. K. Jha ¹, S. P. Aggarwal ¹

Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, India, IN

Source

Current Science, Vol 123, No 8 (2022), Pagination: 975-986

Abstract

The North Eastern Region of India (NER) has tremendous scope for accelerating its growth in agriculture and allied areas through advanced data acquisition, interpretation and dissemination methods with geospatial technology. For several thematic applications, geospatial tools and techniques are being used to provide synoptic, cost-efficient and timely information for effective crop planning and monitoring in the region. A review of space applications in agriculture, horticulture, sericulture, land-use suitability, shifting cultivation, groundwater prospecting, soil resources management, etc. has been made, highlighting the scope and limitation of using these advanced technologies. Satellite remote sensing has several limitations in NER, viz. small and fragmented farmlands, persistent clouds during monsoon, mixed farming, steep hills, etc. Considering these facts, unmanned aerial vehicles (UAVs) are used as an alternative for satellite remote sensing applications in agriculture. The increased availability of very high resolution satellite and UAV data will offer opportunities for innovative solutions to fulfil specific user needs of agriculture and allied sectors in NER

Full Text

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Three-Dimensional Point Cloud Segmentation Using a Combination of RANSAC and Clustering Methods

Abstract Views :118 | PDF Views:67

Authors

Puyam S. Singh ¹, Iainehborlang M. Nongsiej ², Valarie Marboh ², Dibyajyoti Chutia ¹, Victor Saikhom ¹, S. P. Aggarwal ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
2 Department of Computer Science, St Anthony’s College, Shillong 793 001, IN

Source

Current Science, Vol 124, No 4 (2023), Pagination: 434-441

Abstract

There are challenges in performing 3D scene understanding on point clouds derived from drone images as these data are highly unstructured with no neighbouring information, highly redundant making the processing difficult and time-consuming and have variable density making it difficult to group and segment them. For proper scene understanding, these point clouds need to be segmented and classified into different groups representing similar characteristics. The approaches for segmentation differ based on the distinctiveness of each data product. Although newer machine learning-based approaches work well, they need large amounts of standardized labelled data which in turn require extensive resources and human intervention to obtain good results. Considering these, we have proposed a hybrid clustering-based hierarchical model for effective segmentation of dense 3D point cloud. We have applied the model to local data having a mix of man-made and natural vegetation with variable topography. The combination of RANSAC, DBSCAN and Euclidean method of cluster extraction proved to be useful for precise segmentation and classification of point clouds. The performance of the model has been assessed using Davies–Bouldin dbIndex-based intrinsic measures. The hybrid approach is able to segment 91% of the point clouds precisely compared to the conventional one-step clustering approach.

Keywords

Clustering, Drone Images, Hierarchical Model, Three-Dimensional Point Cloud, Segmentation.

Full Text

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