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- V. Keerthi
- V. K. Dadhwal
- A. S. Kiran Kumar
- S. Ghosh
- S. Nandy
- Sanjiv K. Sinha
- Hitendra Padalia
- Bhaskar R. Nikam
- Vaibhav Garg
- Prasun K. Gupta
- Praveen K. Thakur
- Arpit Chouksey
- S. P. Aggarwal
- Pankaj Dhote
- Saurabh Purohit
- N. R. Patel
- R. Devadas
- A. Huete
- Y. V. N. Krishna Murthy
- Nikita Agarwal
- Shiva Reddy Koti
- Sameer Saran
- Ashutosh Bhardwaj
Journals
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Senthil Kumar, A.
- Algorithms to Improve Spectral Discrimination from Indian Hyperspectral Sensors Data
Abstract Views :244 |
PDF Views:90
Authors
Affiliations
1 Geophysical and Special Products Group, National Remote Sensing Centre (ISRO), Balanagar, Hyderabad 500 037, IN
2 Space Applications Centre (ISRO), Jodhpur Tekra, Ahmedabad 380 015, IN
1 Geophysical and Special Products Group, National Remote Sensing Centre (ISRO), Balanagar, Hyderabad 500 037, IN
2 Space Applications Centre (ISRO), Jodhpur Tekra, Ahmedabad 380 015, IN
Source
Current Science, Vol 108, No 5 (2015), Pagination: 842-847Abstract
With the inclusion of a hyperspectral imager (HySI) sensor on the Indian Mini Satellite (IMS-1) Earth observation mission and subsequently near identical hyperspectral sensor on the Chandrayaan-1 lunar mission, 64-band hyperspectral data from both these missions have provided the user community rich information to explore new algorithms to exploit sensorspecific parameters and to interpret and/or classify the features in multi-resolution frame. In this article, methods to improve spectral uniqueness present in the HySI by analysing adjacent bands' spectral overlaps, by implementing spectral deconvolution and reconstruction techniques are presented. Similarly, the use of multi-resolution approach for fast searching of standard spectral library end-members for better discrimination of hyperspectral pixel data are also discussed along with applications in Earth and lunar surface hyperspectral image interpretation. These spectral analyses techniques are useful in discriminating subtle differences in spectral signatures that help study the origin of secondary craters and gullies/ landslides on the lunar surface.Keywords
Lunar Surface, Multi-resolution Approach, Spectral Deconvolution, Spectral Overlap.Full Text
- Rapid Assessment of Recent Flood Episode in Kaziranga National Park, Assam Using Remotely Sensed Satellite Data
Abstract Views :279 |
PDF Views:117
Authors
Affiliations
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, IN
1 Indian Institute of Remote Sensing (ISRO), Dehradun 248 001, IN
Source
Current Science, Vol 111, No 9 (2016), Pagination: 1450-1451Abstract
Flooding is considered as the most damaging natural hazard. Continuous population growth associated with changes in land use and climate exacerbates flood hazard, and makes livelihood more precarious. A large part of Assam, a state of northeastern India, experiences devastating flood frequently. The Brahmaputra river, along with its distributaries and tributaries in Assam, causes flood in the monsoon season each year which affects agriculture, human life and properties, wildlife, etc.Full Text
References
- Kushwaha, S. P. S., Kalra, M. and Sahi, S., Mapping of Kaziranga Conservation Area, Project Report, IIRS/FED/Kaziranga/36/ 8026/2008.
- Ghosh, S. et al., Mar. Geod., 2015, 38(suppl), 597–613.
- Sridevi, T., Sharma, R., Mehra, P. and Prasad, K. V. S. R., Remote Sensing Lett., 2016, 7(4), 348–357.
- NRSC flood report; http://www.nrsc.gov.in/assam_floods_july_2016 (accessed on 7 August 2016).
- Xu, H. Q., Int. J. Remote Sensing, 2006, 27, 3025–3033.
- Frappart, F., Calmant, S., Cauhope, M., Seyler, F. and Cazenave, A., Remote Sensing Environ., 2006, 100(2), 252–264.
- Dubey, A. K., Gupta, P. K., Dutta, S. and Singh, R. P., J. Hydrol., 2015, 529, 1776-1787.
- Space-Borne Sun-Induced Fluorescence:An Advanced Probe to Monitor Seasonality of Dry and Moist Tropical Forest Sites
Abstract Views :211 |
PDF Views:74
Authors
Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
Source
Current Science, Vol 113, No 11 (2017), Pagination: 2180-2183Abstract
Space-borne sun-induced fluorescence (SIF) is the latest breakthrough in remote sensing of physiological response of plants. We studied the seasonality of sal (Shorea robusta) forest canopies analysing space-borne SIF and reflectance data collected over moist and dry sites in central India. Results indicate that the monthly response of OCO-2 SIF, MODIS NDVI and GPP differs significantly across the wet and dry forest sites. SIF explained higher seasonal variations and was also better correlated to rainfall across sites compared to NDVI.Keywords
Fluorescence, Remote Sensing, Seasonal Variations, Tropical Forests, Vegetation Index.Full Text
References
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- Frankenberg, C. et al., Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing Environ., 2014, 147, 1–12.
- Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R. and Moreno, J., Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sensing Environ., 2009, 113, 2037–2051.
- Govindjee, Sixty-three years since Kautsky: chlorophyll a fluorescence. J. Plant Physiol., 1995, 22, 131–160.
- Kautsky, H. and Hirsch, A., Neueversuchezur kohlensaure assimilation. Naturwissenschaften, 1931, 19, 964.
- Maxwell, K. and Johnson, G. N., Chlorophyll fluorescence – a practical guide. J. Exp. Bot., 2000, 51(345), 659–668.
- Krause, H. and Weis, W., Chlorophyll fluorescence and photosynthesis: the basics. Annu. Rev. Plant Physiol. Plant Mol. Biol., 1991, 42, 313–349.
- Frankenberg, C., Butz, A. and Toon, G. C., Disentangling chlorophyll fluorescence from atmospheric scattering effects in O2A band spectra of reflected sunlight. Geophys. Res. Lett., 2011, 38, 1–5.
- Smorenburg, K. et al., Remote sensing of solar-induced fluorescence of vegetation. In Proceedings SPIE 4542, Remote Sensing for Agriculture, Ecosystems, and Hydrology III, 2002, pp. 178–190.
- Lee, J-E. et al., Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence. Proceedings of the Royal Society, 2013, pp. 1–9.
- Satya, Upreti, D. K. and Nayaka, S., Shorea robusta – an excellent host tree for lichen growth in India. Curr. Sci., 2005, 89(4), 594–595.
- Troup, R. S., The Silviculture of Indian Trees, Vols I and II, Clarendon Press, Oxford, UK, 1921.
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- Misra, R., Studies on the primary productivity of terrestrial communities at Varanasi. Trop. Ecol., 1969, 10, 1–15.
- Pokhriyal, T. C., Ramola, B. C. and Raturi, A. S., Soil moisture regime and nitrogen content in natural sal forest (Shorea robusta). Indian For., 1987, 113, 300–306.
- Singh, O., Sharma, D. C. and Rawat, J. K., Production and decomposition of leaf litter in sal, teak, eucalyptus and poplar forests in Uttar Pradesh. Indian For., 1993, 119(2), 112–121.
- Kushwaha, S. P. S. and Nandy, S., Species diversity and community structure in sal (Shorea robusta) forests of two different rainfall regimes in West Bengal, India. Biodivers. Conserv., 2012, 21(5), 1215–1228.
- Roy, P. S. et al., New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. Int. J. Appl. Earth Obs. Geoinf., 2015, 39, 142–159.
- Veroustraete, F., Patyn, J. and Myneni, R. B., Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model. Remote Sensing Environ., 1996, 58, 115–130.
- 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
1 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
Source
Current Science, Vol 113, No 12 (2017), Pagination: 2328-2334Abstract
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
- IMD, India Meteorological Department: AWS Lab Pune; http://www.imdaws.com/ViewAwsData.aspx (accessed on 10 April 2017).
- Aggarwal, S. P., Thakur, P. K., Nikam, B. R. and Garg, V., Integrated approach for snowmelt run-off estimation using temperature index model, remote sensing and GIS. Curr. Sci., 2014, 106(3), 397–407.
- Kulkarni, A. V., Singh, S. K., Mathur, P. and Mishra, V. D., Algorithm to monitor snow cover using AWiFS data of Resourcesat-1 for the Himalayan region. Int. J. Remote Sens., 2006, 27, 2449–2457.
- Dozier, J., Snow reflectance from Landast-4 thematic mapper. IEEE Trans. Geosci. Remote Sens., 1984, 22, 323–328.
- Dozier, J. and Marks, D., Snow mapping and classification from Landsat Thematic Mapper (TM) data. Ann. Glaciol., 1987, 9, 97–103.
- Dozier, J., Spectral signature of alpine snow covers from the Landsat thematic mapper. Remote Sens. Environ., 1989, 28, 9–22.
- Dozier, J. and Frew, J., Computational provenance in hydrologic science: a snow mapping example. Philos. Trans. R. Soc. London, Ser. A., 2009, 367, 1021–1033.
- Vogel, S. W., Usage of high-resolution Landsat-7 band-8 for single band snow cover classification. Ann. Glaciol., 2002, 34, 53–57.
- Dorothy, K. H. et al., Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2001; https://modis-snow-ice.gsfc.nasa.gov (accessed on 13 February 2017).
- Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo. N. E. and Bayr, K. J., MODIS snow cover products. Remote Sens. Environ., 2002, 83, 181–194.
- Hall, D. K., Riggs, G. A. and Roman, M. O., VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD), Version 1.0, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2015, p. 38.
- NSIDC, National Snow and Ice Data Centre website: Snow Cover Products; https://nsidc.org/data/MOD10A2 (accessed on 13 February 2017).
- Hall, D. K. and Riggs, G. A., MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2016; doi: http://dx.doi.org/10.5067/MODIS/MOD-10A2.006 (accessed on 13 February 2017).
- Nikam, B. R., Garg, V., Gupta, P. K., Thakur, P. K., Aggarwal, S. P. and Kumar, A. S., A Preliminary Assessment Report on Assessment of Long-Term and Current Status (2016-17) of Snow Cover Area in North Western Himalayan River Basins using Remote Sensing. Technical Report, IIRS/WRD/Technical Report/ 2017/212, IIRS Dehradun, 2017, p. 26.
- Jain, S. K., Goswami, A. and Saraf, A. K., Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. Int. J. Remote Sens., 2008, 29(20), 5863–5878.
- Sharma, V., Mishra, V. D. and Joshi, P. K., Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya. Int. J. Remote Sens., 2014, 35(9), 3036–3056.
- ISRO, Indian Space Research Organisation; http://www.isro.gov.in/Spacecraft/scatsat-1 (accessed on 10 April 2017).
- Yueh, S., Cline, D. and Elder, K., POLSCAT Ku-band Radar Remote Sensing of Terrestrial Snow Cover. IEEE Proc. International Geoscience and Remote Sensing Symposium-2008, Boston, MA, USA, 7–11 July 2008 (doi:10.1109/IGARSS.2008.4779276).
- CWC, Central Water Commission: Flood Forecast Portal; http://www.india-water.gov.in/ffs/hydrograph/ (accessed on 10 April 2017).
- Thakur, P. K., Aggarwal, S. P., Arun, G., Sood, S., Kumar, A. S., Snehmani and Dobhal, D. P., Estimation of snow cover area, snow physical properties and glacier classification in parts of Western Himalayas using C-band SAR data. J. Indian Soc. Remote Sens., 2016; doi:10.1007/s12524-016-0609-y.
- Thakur, P. K., Garg, P. K., Aggarwal, S. P., Garg, R. D. and Snehmani, Snow cover area mapping using synthetic aperture radar in Manali watershed of Beas River in the Northwest Himalayas. J. Indian Soc. Remote Sens., 2013; doi:10.1007/s12524-012-0236-1.
- Estimating Net Primary Productivity of Croplands in Indo-Gangetic Plains Using GOME-2 Sun-Induced Fluorescence and MODIS NDVI
Abstract Views :217 |
PDF Views:83
Authors
N. R. Patel
1,
Hitendra Padalia
1,
R. Devadas
2,
A. Huete
2,
A. Senthil Kumar
1,
Y. V. N. Krishna Murthy
3
Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
2 University of Technology Sydney, Sydney, AU
3 National Remote Sensing Centre, Hyderabad 500 072, IN
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
2 University of Technology Sydney, Sydney, AU
3 National Remote Sensing Centre, Hyderabad 500 072, IN
Source
Current Science, Vol 114, No 06 (2018), Pagination: 1333-1337Abstract
Recently evolved satellite-based sun-induced fluorescence (SIF) spectroscopy is considered as a direct measure of photosynthetic activity of vegetation. We have used monthly averages of satellite-based SIF retrievals for three agricultural year cycles, i.e. May to April for each of the three years, viz. 2007–08, 2008–09 and 2009–10 to assess comparative performance of SIF and normalized difference vegetation index (NDVI) for predicting net primary productivity (NPP) over the Indo-Gangetic Plains, India. Results show that SIF values for C4 crop-dominated districts were higher than C3 crop-dominated districts during summer and low during winter for all three years. SIF explained more or less above 70% of variance in NPP. The variance explained by integrated NDVI ranged from 60% to 67%. Thus the present study has shown the potential of SIF data for improved modelling of agricultural productivity at a regional scale.Keywords
Crop Lands, Net Primary Productivity, Photosynthetic Activity, Sun-Induced Fluorescence.Full Text
References
- Smorenburg, K. et al., Remote sensing of solar induced fluorescence of vegetation. SPIE Proc.: Remote Sensing Agric., Ecosyst. Hydrol. III, 2000, p. 4542.
- Meroni, M., Rossini, M., Guanter, L., Alonso, L., Rascher, U., Colombo, R. and Moreno, J., Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sensing Environ., 2009, 113, 2037–2051.
- Porcar-Castell, A. et al., Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot., 2014, 65, 4065–4095.
- Frankenberg, C. et al., New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett., 2011, 38, L03801; doi:10.1029/2010GL045896.
- Guanter, L. et al., Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sensing Environ., 2012, 121, 236–251.
- Joiner, J., Yoshida, Y., Vasilkov, A. P., Yoshida, Y., Corp, L. A. and Middleton, E. M., First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 2011, 8, 637–651.
- Joiner, J. et al., Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech., 2013, 6, 2803–2823.
- Guanter, L. et al., Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA, 2014, 111(14), E1327–E1333.
- Pal, D. K., Bhattacharyya, T., Srivastava, P., Chandran, P. and Ray, S. K., Soils of the Indo-Gangetic Plains: their historical perspective and management. Curr. Sci., 2009, 96(9), 1193–1202.
- Nayak, R. K., Patel, N. R. and Dadhwal, V. K., Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model. Environ. Monit. Assess., 2010, 1, 195–213.
- Data Mining Techniques for Predicting Dengue Outbreak in Geospatial Domain Using Weather Parameters for New Delhi, India
Abstract Views :286 |
PDF Views:77
Authors
Affiliations
1 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
1 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
Source
Current Science, Vol 114, No 11 (2018), Pagination: 2281-2291Abstract
Dengue is a hazardous disease which poses a critical threat to the population of Delhi, India. These cases are steadily reported during and post-monsoon season indicating its correlation with weather parameters. Establishing this relation will help understand the spread of dengue and will allow decision makers take precautionary steps beforehand. Our study explains the adopted multi-regression and Naïve Bayes approach to model the relation between dengue cases and weather parameters, i.e. maximum temperature, rainfall and relative humidity. Both these models have served a great deal in modelling this relationship which has enabled us to forecast a probable dengue outbreak. Our results have shown that sudden and high rainfall accompanied with 30–35°C temperature and high relative humidity contributes to a highly vulnerable weather for the spread of dengue. Also, we have proposed a new application of spherical k-means clustering algorithm to identify zones with similar transmission pattern which gives insight into the distribution of dengue incidences in Delhi. Results show that Central, Civil Lines, Rohini, South and West zones have the highest odds of dengue occurrences.Keywords
Dengue, Multi-Regression, Naive Bayes, Spherical K-Means, Weather Parameters.Full Text
References
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- Comprehensive Remote Sensing, Volume 2: Data Processing and Analysis Methodology
Abstract Views :170 |
PDF Views:81
Authors
Affiliations
1 Kongu Engineering College, Perundurai, Erode 638 060, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
1 Kongu Engineering College, Perundurai, Erode 638 060, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
Source
Current Science, Vol 119, No 9 (2020), Pagination: 1570-1571Abstract
No Abstract.Full Text