Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985
Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanizati...
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Veröffentlicht in: | Journal of environmental management 2018-01, Vol.206, p.1192-1203 |
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creator | Behera, M.D. Tripathi, P. Das, P. Srivastava, S.K. Roy, P.S. Joshi, C. Behera, P.R. Deka, J. Kumar, P. Khan, M.L. Tripathi, O.P. Dash, T. Krishnamurthy, Y.V.N. |
description | Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate.
•Deforestation rate was nearly double fold in Brahmaputra than Mahanadi river basin•Satellite imagery based mapping offered LULC change with greater accuracy•Human disturbances is evident as major cause of LULC changes in the two river basins•Modelled LULC map of 2025 well predicted the past trends in both the river basins |
doi_str_mv | 10.1016/j.jenvman.2017.10.015 |
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•Deforestation rate was nearly double fold in Brahmaputra than Mahanadi river basin•Satellite imagery based mapping offered LULC change with greater accuracy•Human disturbances is evident as major cause of LULC changes in the two river basins•Modelled LULC map of 2025 well predicted the past trends in both the river basins</description><identifier>ISSN: 0301-4797</identifier><identifier>EISSN: 1095-8630</identifier><identifier>DOI: 10.1016/j.jenvman.2017.10.015</identifier><identifier>PMID: 29153551</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Brahmaputra ; Classification ; Drivers ; Dyna-CLUE ; Mahanadi ; Mapping ; β-coefficient</subject><ispartof>Journal of environmental management, 2018-01, Vol.206, p.1192-1203</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright © 2017 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c365t-7349822a59c6bb72adc8210fc44c3592d693d1b0db2aa8afaf06ad9b2fc138113</citedby><cites>FETCH-LOGICAL-c365t-7349822a59c6bb72adc8210fc44c3592d693d1b0db2aa8afaf06ad9b2fc138113</cites><orcidid>0000-0002-0508-7219</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jenvman.2017.10.015$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29153551$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Behera, M.D.</creatorcontrib><creatorcontrib>Tripathi, P.</creatorcontrib><creatorcontrib>Das, P.</creatorcontrib><creatorcontrib>Srivastava, S.K.</creatorcontrib><creatorcontrib>Roy, P.S.</creatorcontrib><creatorcontrib>Joshi, C.</creatorcontrib><creatorcontrib>Behera, P.R.</creatorcontrib><creatorcontrib>Deka, J.</creatorcontrib><creatorcontrib>Kumar, P.</creatorcontrib><creatorcontrib>Khan, M.L.</creatorcontrib><creatorcontrib>Tripathi, O.P.</creatorcontrib><creatorcontrib>Dash, T.</creatorcontrib><creatorcontrib>Krishnamurthy, Y.V.N.</creatorcontrib><title>Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985</title><title>Journal of environmental management</title><addtitle>J Environ Manage</addtitle><description>Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate.
•Deforestation rate was nearly double fold in Brahmaputra than Mahanadi river basin•Satellite imagery based mapping offered LULC change with greater accuracy•Human disturbances is evident as major cause of LULC changes in the two river basins•Modelled LULC map of 2025 well predicted the past trends in both the river basins</description><subject>Brahmaputra</subject><subject>Classification</subject><subject>Drivers</subject><subject>Dyna-CLUE</subject><subject>Mahanadi</subject><subject>Mapping</subject><subject>β-coefficient</subject><issn>0301-4797</issn><issn>1095-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqFkNFrFDEQxoMo9mz7Jyh59GXPTLLJbp5Ei62FiiD6HGaT2TbHbfZM9g7635vjTl-FgWE-vm-G-TH2FsQaBJgPm_WG0mHCtJYCuqqtBegXbAXC6qY3SrxkK6EENG1nuwv2ppSNEEJJ6F6zC2lBK61hxR5_0DQvxAulEtMjH7BQ4IHGOVNZcIlz4phw-1xi4THxb_hUxxCrGPjnjE8T7vZLRp7jgfIxXk217lOIyOvgiYPt9RV7NeK20PW5X7Jft19-3nxtHr7f3d98emi8MnppOtXaXkrU1pth6CQG30sQo29br7SVwVgVYBBhkIg9jjgKg8EOcvSgegB1yd6f9u7y_HtfX3BTLJ62W0w074sDa0zb9sKIatUnq89zKZlGt8txwvzsQLgjY7dxZ8buyPgoV8Y19-58Yj9MFP6l_kKtho8nA9VHD5GyKz5SJRFiJr-4MMf_nPgDwOyQRw</recordid><startdate>20180115</startdate><enddate>20180115</enddate><creator>Behera, M.D.</creator><creator>Tripathi, P.</creator><creator>Das, P.</creator><creator>Srivastava, S.K.</creator><creator>Roy, P.S.</creator><creator>Joshi, C.</creator><creator>Behera, P.R.</creator><creator>Deka, J.</creator><creator>Kumar, P.</creator><creator>Khan, M.L.</creator><creator>Tripathi, O.P.</creator><creator>Dash, T.</creator><creator>Krishnamurthy, Y.V.N.</creator><general>Elsevier Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0508-7219</orcidid></search><sort><creationdate>20180115</creationdate><title>Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985</title><author>Behera, M.D. ; Tripathi, P. ; Das, P. ; Srivastava, S.K. ; Roy, P.S. ; Joshi, C. ; Behera, P.R. ; Deka, J. ; Kumar, P. ; Khan, M.L. ; Tripathi, O.P. ; Dash, T. ; Krishnamurthy, Y.V.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-7349822a59c6bb72adc8210fc44c3592d693d1b0db2aa8afaf06ad9b2fc138113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Brahmaputra</topic><topic>Classification</topic><topic>Drivers</topic><topic>Dyna-CLUE</topic><topic>Mahanadi</topic><topic>Mapping</topic><topic>β-coefficient</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Behera, M.D.</creatorcontrib><creatorcontrib>Tripathi, P.</creatorcontrib><creatorcontrib>Das, P.</creatorcontrib><creatorcontrib>Srivastava, S.K.</creatorcontrib><creatorcontrib>Roy, P.S.</creatorcontrib><creatorcontrib>Joshi, C.</creatorcontrib><creatorcontrib>Behera, P.R.</creatorcontrib><creatorcontrib>Deka, J.</creatorcontrib><creatorcontrib>Kumar, P.</creatorcontrib><creatorcontrib>Khan, M.L.</creatorcontrib><creatorcontrib>Tripathi, O.P.</creatorcontrib><creatorcontrib>Dash, T.</creatorcontrib><creatorcontrib>Krishnamurthy, Y.V.N.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of environmental management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Behera, M.D.</au><au>Tripathi, P.</au><au>Das, P.</au><au>Srivastava, S.K.</au><au>Roy, P.S.</au><au>Joshi, C.</au><au>Behera, P.R.</au><au>Deka, J.</au><au>Kumar, P.</au><au>Khan, M.L.</au><au>Tripathi, O.P.</au><au>Dash, T.</au><au>Krishnamurthy, Y.V.N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985</atitle><jtitle>Journal of environmental management</jtitle><addtitle>J Environ Manage</addtitle><date>2018-01-15</date><risdate>2018</risdate><volume>206</volume><spage>1192</spage><epage>1203</epage><pages>1192-1203</pages><issn>0301-4797</issn><eissn>1095-8630</eissn><abstract>Land use and land cover (LULC) change has been recognized as a key driver of global climate change by influencing land surface processes. Being in constant change, river basins are always subjected to LULC changes, especially decline in forest cover to give way for agricultural expansion, urbanization, industrialization etc. We used on-screen digital interpretation technique to derive LULC maps from Landsat images at three decadal intervals i.e., 1985, 1995 and 2005 of two major river basins of India. Rain-fed, Mahanadi river basin (MRB) attributed to 55% agricultural area wherein glacier-fed, Brahmaputra river basin (BRB) had only 16% area under agricultural land. Though conversion of forest land for agricultural activities was the major LULC changes in both the basins, the rate was higher for BRB than MRB. While water body increased in MRB could be primarily attributed to creation of reservoirs and aquaculture farms; snow and ice melting attributed to creation of more water bodies in BRB. Scrub land acted as an intermediate class for forest conversion to barren land in BRB, while direct conversion of scrub land to waste land and crop land was seen in MRB. While habitation contributed primarily to LULC changes in BRB, the proximity zones around habitat and other socio-economic drivers contributed to LULC change in MRB. Comparing the predicted result with actual LULC of 2005, we obtained >97% modelling accuracy; therefore it is expected that the Dyna-CLUE model has very well predicted the LULC for the year 2025. The predicted LULC of 2025 and corresponding LULC changes in these two basins acting as early warning, and with the past 2-decadal change analysis this study is believed to help the land use planners for improved regional planning to create balanced ecosystem, especially in a changing climate.
•Deforestation rate was nearly double fold in Brahmaputra than Mahanadi river basin•Satellite imagery based mapping offered LULC change with greater accuracy•Human disturbances is evident as major cause of LULC changes in the two river basins•Modelled LULC map of 2025 well predicted the past trends in both the river basins</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>29153551</pmid><doi>10.1016/j.jenvman.2017.10.015</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-0508-7219</orcidid></addata></record> |
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subjects | Brahmaputra Classification Drivers Dyna-CLUE Mahanadi Mapping β-coefficient |
title | Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985 |
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