Remote sensing based change analysis of rice environments in Odisha, India

The rainfed rice-growing environment is perhaps one of the most vulnerable to water stress such as drought and floods. It is important to determine the spatial extent of the stress-prone areas to effectively and efficiently promote proper technologies (e.g., stress-tolerant varieties) to tackle the...

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Autores principales: Gumma, Murali K., Mohanty, Samarendu, Andrew, N., Rala, A, Irshad, A.M., Das, S.R.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Elsevier 2015
Materias:
Acceso en línea:https://hdl.handle.net/10568/76717
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author Gumma, Murali K.
Mohanty, Samarendu
Andrew, N.
Rala, A
Irshad, A.M.
Das, S.R.
author_browse Andrew, N.
Das, S.R.
Gumma, Murali K.
Irshad, A.M.
Mohanty, Samarendu
Rala, A
author_facet Gumma, Murali K.
Mohanty, Samarendu
Andrew, N.
Rala, A
Irshad, A.M.
Das, S.R.
author_sort Gumma, Murali K.
collection Repository of Agricultural Research Outputs (CGSpace)
description The rainfed rice-growing environment is perhaps one of the most vulnerable to water stress such as drought and floods. It is important to determine the spatial extent of the stress-prone areas to effectively and efficiently promote proper technologies (e.g., stress-tolerant varieties) to tackle the problem of sustainable food production. This study was conducted in Odisha state located in eastern India. Odisha is predominantly a rainfed rice ecosystem (71% rainfed and 29% canal irrigated during kharif-monsoon season), where rice is the major crop and staple food of the people. However, rice productivity in Odisha is one of the lowest in India and a significant decline (9%) in rice cultivated area was observed in 2002 (a drought year). The present study analyzed the temporal rice cropping pattern in various ecosystems and identified the stress-prone areas due to submergence (flooding) and water shortage. The spatial distribution of rice areas was mapped using MODIS (MOD09Q1) 250-m 8-day time-series data (2000?2010) and spectral matching techniques. The mapped rice areas were strongly correlated (R2 = 90%) with district-level statistics. Also the class accuracy based on field-plot data was 84.8%. The area under the rainfed rice ecosystem continues to dominate, recording the largest share among rice classes across all the years. The use of remote-sensing techniques is rapid, cost-effective, and reliable to monitor changes in rice cultivated area over long periods of time and estimate the reduction in area cultivated due to abiotic stress such as water stress and submergence. Agricultural research institutes and line departments in the government can use these techniques for better planning, regular monitoring of land-use changes, and dissemination of appropriate technologies.
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spelling CGSpace767172024-06-26T10:18:09Z Remote sensing based change analysis of rice environments in Odisha, India Gumma, Murali K. Mohanty, Samarendu Andrew, N. Rala, A Irshad, A.M. Das, S.R. rice cultivation modis stress-tolerant varieties drought and submergence odisha The rainfed rice-growing environment is perhaps one of the most vulnerable to water stress such as drought and floods. It is important to determine the spatial extent of the stress-prone areas to effectively and efficiently promote proper technologies (e.g., stress-tolerant varieties) to tackle the problem of sustainable food production. This study was conducted in Odisha state located in eastern India. Odisha is predominantly a rainfed rice ecosystem (71% rainfed and 29% canal irrigated during kharif-monsoon season), where rice is the major crop and staple food of the people. However, rice productivity in Odisha is one of the lowest in India and a significant decline (9%) in rice cultivated area was observed in 2002 (a drought year). The present study analyzed the temporal rice cropping pattern in various ecosystems and identified the stress-prone areas due to submergence (flooding) and water shortage. The spatial distribution of rice areas was mapped using MODIS (MOD09Q1) 250-m 8-day time-series data (2000?2010) and spectral matching techniques. The mapped rice areas were strongly correlated (R2 = 90%) with district-level statistics. Also the class accuracy based on field-plot data was 84.8%. The area under the rainfed rice ecosystem continues to dominate, recording the largest share among rice classes across all the years. The use of remote-sensing techniques is rapid, cost-effective, and reliable to monitor changes in rice cultivated area over long periods of time and estimate the reduction in area cultivated due to abiotic stress such as water stress and submergence. Agricultural research institutes and line departments in the government can use these techniques for better planning, regular monitoring of land-use changes, and dissemination of appropriate technologies. 2015-01 2016-09-01T11:12:55Z 2016-09-01T11:12:55Z Journal Article https://hdl.handle.net/10568/76717 en Limited Access Elsevier Gumma, M.K.; Mohanty, S.; Andrew, N.; Rala, A; Irshad, A.M.; Das, S.R. 2015. Remote sensing based change analysis of rice environments in Odisha, India. Journal of Environmental Management 148(2015):31-41.
spellingShingle rice cultivation
modis
stress-tolerant varieties
drought and submergence
odisha
Gumma, Murali K.
Mohanty, Samarendu
Andrew, N.
Rala, A
Irshad, A.M.
Das, S.R.
Remote sensing based change analysis of rice environments in Odisha, India
title Remote sensing based change analysis of rice environments in Odisha, India
title_full Remote sensing based change analysis of rice environments in Odisha, India
title_fullStr Remote sensing based change analysis of rice environments in Odisha, India
title_full_unstemmed Remote sensing based change analysis of rice environments in Odisha, India
title_short Remote sensing based change analysis of rice environments in Odisha, India
title_sort remote sensing based change analysis of rice environments in odisha india
topic rice cultivation
modis
stress-tolerant varieties
drought and submergence
odisha
url https://hdl.handle.net/10568/76717
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AT mohantysamarendu remotesensingbasedchangeanalysisofriceenvironmentsinodishaindia
AT andrewn remotesensingbasedchangeanalysisofriceenvironmentsinodishaindia
AT ralaa remotesensingbasedchangeanalysisofriceenvironmentsinodishaindia
AT irshadam remotesensingbasedchangeanalysisofriceenvironmentsinodishaindia
AT dassr remotesensingbasedchangeanalysisofriceenvironmentsinodishaindia