Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Mitra, Debashis
- Detection and Mapping of Seagrass Meadows at Ritchie’s Archipelago using Sentinel 2A Satellite Imagery
Abstract Views :163 |
PDF Views:81
Authors
Sharad Bayyana
1,
Satish Pawar
1,
Swapnali Gole
2,
Sohini Dudhat
2,
Anant Pande
2,
Debashis Mitra
3,
Jeyaraj Antony Johnson
4,
Kuppusamy Sivakumar
2
Affiliations
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
2 Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, IN
3 Marine and Atmospheric Science Department, Indian Institute of Remote Sensing, Dehradun 248 001, IN
4 Department of Habitat Ecology, Wildlife Institute of India, Dehradun 248 002, IN
1 Indian Institute of Remote Sensing, Dehradun 248 001, IN
2 Department of Endangered Species Management, Wildlife Institute of India, Dehradun 248 002, IN
3 Marine and Atmospheric Science Department, Indian Institute of Remote Sensing, Dehradun 248 001, IN
4 Department of Habitat Ecology, Wildlife Institute of India, Dehradun 248 002, IN
Source
Current Science, Vol 118, No 8 (2020), Pagination: 1275-1282Abstract
This study presents an attempt to utilize seagrass data acquired from field surveys to compare classification models for mapping seagrasses using Sentinel -2A satellite data. Out of three models tested , viz. Random Forest, Support Vector Machine and K-Nearest Neighbor; Random Forest classification model proved most effective in the given scenario with 0.99 model accuracy. Seagrasses present as deep as 21 m were detected post water column correction, presenting the capability of Sentinel-2A satellite in detecting submerged benthic habitat.Keywords
Depth Invariant Index, Ritchie’s Archipelago, Seagrass, Sentinel-2A.Full Text
References
- Unsworth, R. K. and Cullen, L. C., Recognising the necessity for Indo‐Pacific seagrass conservation. Conserv. Lett., 2010, 3(2), 63– 73.
- Mumby, P. J., Green, E. P., Edwards, A. J. and Clark, C. D., Measurement of seagrass standing crop using satellite and digital airborne remote sensing. Mar. Ecol. Prog. Ser., 1997, 159, 51–60.
- Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G., Sourbes, L. and Tsirika, A., Use of SPOT 5 for mapping seagrasses: an application to Posidonia oceanica. Remote Sens. Environ., 2005, 94(1), 39–45.
- Nobi, E. P. and Thangaradjou, T., Evaluation of the spatial changes in seagrass cover in the lagoons of La kshadweep islands, India, using IRS LISS III satellite images. Geocarto Int., 2012, 27(8), 647–660.
- Jensen, J. R., Inland wetland change detection in the Eve rglades Water Conservation Area 2A using a time series of normalized remotely sensed data. Photogramm. Eng. Remote Sensing, 1995, 61(2), 199–209.
- Clark, C. D., Ripley, H. T., Green, E. P., Edwards, A. J. and Mumby, P. J., Cover mapping and measurement of tropical coastal environments with hyperspectral and high spatial resolution data. Int. J. Remote Sens., 1997, 18(2), 237–242.
- Malthus, T. J. and George, D. G., Airborne remote sensing of macrophytes in Cefni Reservoir, Anglesey, UK. Aquat. Bot., 1997, 58(3–4), 317–332.
- Alberotanza, L., Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello L agoons, Italy. Int. J. Remote Sens., 1999, 20(3), 523–533.
- Dekker, A., Brando, V., Anstee, J., Fyfe, S., Malthus, T. and Kar-pouzli, E., Remote sensing of seagrass ecosystems: use of spaceborne and airborne sensors. In Seagrasses: Biology, Ecologyand Conservation, Springer, Dordrecht, 2007, pp. 347–359.
- Ward, D. H., Markon, C. J. and Douglas, D. C., Distribution and stability of eelgrass beds at Izembek Lagoon, Alaska. Aquat. Bot., 1997, 58(3–4), 229–240.
- Macleod, R. D. and Congalton, R. G., A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogramm. Eng. Remote Sensing, 1998, 64(3), 207–216.
- Anstee, J. M., Dekker, A. G. and Brando, V. E., Retrospective change detection in a shallow coastal tidal lake: mapping
- seagrasses in Wallis Lake, Australia. In Analysis of Multi-Temporal Remote Sensing Images, 2004, pp. 277–285.
- Chauvaud, S., Bouchon, C. and Maniere, R., Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove). Int. J. Remote Sensing, 1998, 19(18), 3625–3639.
- Schweizer, D., Armstrong, R. A. and Posada, J., Remote sensing characterization of benthic habitats and submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela. Int. J. Remote Sensing, 2005, 26(12), 2657–2667.
- Alkhatlan, A., Bannari, A., El-Battay, A., Al-Dawood, T. and Abahussain, A., Potential of Landsat-oli for seagrass and algae species detection and discrimination in Bahrain national water using spectral reflectance. In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium , Valencia, Spain, 2018, pp. 4043–4046.
- Horning, N., Robinson, J. A., Sterling, E. J., Turner, W. and Spector, S., Remote Sensing for Ecology and Conservation: A Hand-book of Techniques, Oxford University Press, 2010.
- Lyzenga, D. R., Passive remote sensing techniques for mapping water depth and bottom features. Appl. Opt., 1978, 17(3), 379– 383.
- Lyzenga, D. R., Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int. J. Remote Sensing, 1981, 2(1), 71–82.
- Sagawa, T., Boisnier, E., Komatsu, T., Mustapha, K. B., Hattour, A., Kosaka, N. and Miyazaki, S., Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga’s model. Int. J. Remote Sensing, 2010, 31(12), 3051–3064.
- Zoffoli, M., Frouin, R. and Kampel, M., Water column correction for coral reef studies by remote sensing. Sensors, 2014, 14(9), 16881–16931.
- Pu, R., Bell, S., Meyer, C., Baggett, L. and Zhao, Y., Mapping and assessing seagrass along the western coast of Florida using La nd-sat TM and EO-1 ALI/hyperion imagery. Estuar., Coastal Shelf Sci., 2012, 115, 234–245.
- Geevarghese, G. A., Akhil, B., Magesh, G., Krishnan, P., Purvaja, R. and Ramesh, R., A comprehensive geospatial assessment of seagrass distribution in India. Ocean Coast. Manage., 2018, 159, 16–25.
- Fauzan, M. A., Kumara, I. S., Yogyantoro, R., Suwardana, S., Fadhilah, N., Nurmalasari, I. and Wicaksono, P., Assessing the capability of Sentinel-2A data for mapping seagrass percent cover in Jerowaru, East Lombok. Indonesian J. Geogr., 2017, 49(2), 195–203.
- Manuputty, A., Lumban-Gaol, J. and Agus, S. B., Seagrass mapping based on satellite image Worldview-2 by using depth invariant index method. Indonesian J. Mar. Sci./Ilmu Kelautan, 2016, 21(1), 37–44.
- Thangaradjou, T. and Bhatt, J. R., Status of seagrass ecosystems in India. Ocean Coast. Manage., 2018, 159, 7–15.
- Jagtap, T. G. and Inamdar, S. N., Mapping of seagrass meadows from the Lakshadweep Islands (India), using aerial photographs. J. Indian Soc. Remote Sens., 1991, 19(2), 77–82.
- Senthil Kumar, T. T. R. S. S. and Kannan, S., Seagrass resource assessment in the Mandapam coast of the Gulf of Mannar Biosphere Reserve, India. Appl. Ecol. Environ. Res., 2008, 6(1), 139–146.
- Umamaheswari, R., Ramach, S. and Nobi, E. P., Mapping the extend of seagrass meadows of Gulf of Mannar Biosphere Reserve, India using IRS ID satellite imagery. Int. J. Biodiver. Conserv., 2009, 1(5), 187–193.
- Das, H. S., Status of seagrass habitats of the Andaman and Nicobar coast. Technical Report 4, Salim Ali Centre for Ornithology and Natural History (SACON), Coimbatore, India, 1996.
- Thangaradjou, T., Sivakumar, K., Nobi, E. P. and Dilipan, E., Distribution of seagrasses along the Andaman and Nicobar Islands: a post tsunami survey. In Recent Trends in Biodiversity of Andaman and Nicobar Islands, Zoological Survey of India, Kolkata, 2010, Chapter 11, pp. 157–160.
- Savurirajan, M., Equbal, J., Lakra, R. K., Satyam, K. and Thiruchitrambalam, G., Species diversity and distribution of seagrasses from the South Andaman, Andaman and Nicobar Islands, India. Bot. Mar., 2018, 61(3), 225–234.
- Paulose, N. E., Dilipan, E. and Thangaradjou, T., Integrating Indian remote sensing multi -spectral satellite and field data to estimate seagrass cover change in the An daman and Nicobar Islands, India. Ocean Sci. J., 2013, 48(2), 173–181.
- Dsouza, E. and Patankar, V., First underwater sighting and preliminary behavioural observations of D ugongs (Dugong dugon) in the wild from Indian waters, Andaman Islands. J. Threatened Taxa, 2009, 1(1), 49–53.
- NITI Aayog Report, Transforming the Islands through Creativity and Innovation, 2019.
- Island wise – Area and Population, Census 2011.
- Sen2Cor Configuration and User Manual, Ref. S2-PDGS-MPC-L2A-SUM- V2.5.5 (European Space Agency), 2018.
- Qasim, S. Z. and Ansari, Z. A., Food components of the Andaman Sea, Indian J. Marine Sci., 1981, 10(3), 276–279.
- D’Souza, E., Patankar, V., Arthur, R., Marbà, N. and Alcoverro, T., Seagrass herbivory levels sustain site-fidelity in a remnant dugong population. PLoS ONE, 2015, 10(10), e0141224.
- Waycott, M., Duarte, C. M., Carruthers, T. J., Orth, R. J., Den-nison, W. C., Olyarnik, S. and Kendrick, G. A., Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci., 2009, 106(30), 12377–12381.
- Short, F. T., Polidoro, B., Livingstone, S. R., Carpenter, K. E., Bandeira, S., Bujang, J. S. and Erftemeijer, P. L., Extinction risk assessment of the world’s seagrass species. Biol. Conserv., 2011, 144(7), 1961–1971.
- Sridhar, R., Thangaradjou, T., Kannan, L. and Astalakshmi, S., Assessment of coastal bio-resources of the Palk Bay, India, using IRS-LISS-III data. J. Indian Soc. Remote Sensing, 2010, 38(3), 565–575.
- Landsat 8-Based Surface Temperature Anomaly and Hydrocarbon Prospectivity: A Study in Part of Assam–Arakan Fold Belt, North East India
Abstract Views :250 |
PDF Views:79
Authors
Affiliations
1 Oil and Natural Gas Corporation Limited, Assam–Arakan Fold Belt Exploratory Asset, Silchar 788 026, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
3 Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad 826 004, IN
1 Oil and Natural Gas Corporation Limited, Assam–Arakan Fold Belt Exploratory Asset, Silchar 788 026, IN
2 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN
3 Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad 826 004, IN
Source
Current Science, Vol 119, No 1 (2020), Pagination: 128-133Abstract
Subsurface hydrocarbon reservoirs act as effective thermal barriers to the Earth’s heat flow from the interior to the surface. As a result, a positive thermal anomaly below a hydrocarbon reservoir and a negative thermal anomaly on the surface above the reservoir are observed. The use of remote sensing satellite images is a rapid, cost-effective and accurate method of determining land surface temperature of a region. The present study uses recent Landsat 8 operational land imager-thermal infrared sensor images to detect land surface temperature distribution in a part of the Assam–Arakan Fold Belt, North East India, using a single-channel algorithm. Two anomalous negative surface temperature zones to the south of the study area are found to be important. High-resolution Landsat 8 panchromatic image, surface geological map, NDVI map and SRTM data rule out the effects of artefacts, urban settlements, and variations in lithology, vegetation or topography on these anomalous zones. The superimposition of the surface temperature map over the previously determined hydrocarbon prospect map reveals that these negative surface temperature anomalies lie over two significant hydrocarbon prospect zones. Thus, the effect of subsurface petroleum reservoirs is evident on the land surface temperature distribution of the area. Therefore, satellite image-based land surface temperature mapping can be used as an aid in detecting potential target areas for hydrocarbon exploration in the entire basin.Keywords
Hydrocarbon Exploration, Satellite Images Remote Sensing, Surface Temperature Anomaly.Full Text
- Space-Based Observations on The Impact of COVID-19-Induced Lockdown on Aerosols over India
Abstract Views :229 |
PDF Views:82
Authors
Affiliations
1 Indian Institute of Remote Sensing, Indian Space Research Organisation, Department of Space, Dehradun 248 001, IN
1 Indian Institute of Remote Sensing, Indian Space Research Organisation, Department of Space, Dehradun 248 001, IN
Source
Current Science, Vol 119, No 3 (2020), Pagination: 539-544Abstract
The lockdown period in India due to COVID-19 came into effect from 25 March 2020 onwards. The present study analyses the changes and trends in aerosol optical depth (AOD) levels during the last few months and particularly during lockdown period. MODIS observations showed an average reduction of 20–37% in aerosol loading during the lockdown period (25 March–3 May 2020), compared to 2017–2019 across India. A clear-cut and drastic reduction in AOD (which includes both PM2.5 and PM10) was observed at many places across India, in particular over North India (46%), eastern Indo-Gangetic Plains (42%) and peninsular India (30%). Maximum decrease in AOD was seen during 25 March to 5 April 2020 with values as low as 0.18–0.22, which are 72–87% lower than the February–March 2020 level. AOD then slightly increased in the third week of April due to stubble burning and a few cloudy days, and then fell subsequently. Ground measurements on air pollutants at selected Indian cities revealed reduction of 40–50% in surface concentration prior to the lockdown period. Thus, study shows a clear-cut improvement in air quality leading to significant improvement in visibility and more blue skies.Keywords
Aerosol Optical Depth, Air Quality, Coronavirus, Lockdown.Full Text