Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in core corridor of Pearl River Delta (China) by using TM and ETM+ images.
Identifieur interne : 000340 ( PubMed/Corpus ); précédent : 000339; suivant : 000341Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in core corridor of Pearl River Delta (China) by using TM and ETM+ images.
Auteurs : Fenglei Fan ; Yunpeng Wang ; Zhishi WangSource :
- Environmental monitoring and assessment [ 0167-6369 ] ; 2008.
English descriptors
- KwdEn :
- MESH :
- geographic : China.
- Agriculture, Ecosystem, Environmental Monitoring, Geographic Information Systems, Markov Chains, Photography, Rivers, Spacecraft.
Abstract
Land use/land cover (LULC) has a profound impact on economy, society and environment, especially in rapid developing areas. Rapid and prompt monitoring and predicting of LULC's change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC's change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC's change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)-81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC's changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China's urban expansion and cropland loss during 1990-2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998-2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.
DOI: 10.1007/s10661-007-9734-y
PubMed: 17564805
Links to Exploration step
pubmed:17564805Le document en format XML
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Rapid and prompt monitoring and predicting of LULC's change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC's change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC's change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)-81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC's changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China's urban expansion and cropland loss during 1990-2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998-2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">17564805</PMID><DateCreated><Year>2008</Year><Month>01</Month><Day>14</Day></DateCreated><DateCompleted><Year>2008</Year><Month>09</Month><Day>16</Day></DateCompleted><DateRevised><Year>2009</Year><Month>05</Month><Day>11</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0167-6369</ISSN><JournalIssue CitedMedium="Print"><Volume>137</Volume><Issue>1-3</Issue><PubDate><Year>2008</Year><Month>Feb</Month></PubDate></JournalIssue><Title>Environmental monitoring and assessment</Title><ISOAbbreviation>Environ Monit Assess</ISOAbbreviation></Journal><ArticleTitle>Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in core corridor of Pearl River Delta (China) by using TM and ETM+ images.</ArticleTitle><Pagination><MedlinePgn>127-47</MedlinePgn></Pagination><Abstract><AbstractText>Land use/land cover (LULC) has a profound impact on economy, society and environment, especially in rapid developing areas. Rapid and prompt monitoring and predicting of LULC's change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC's change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC's change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)-81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC's changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China's urban expansion and cropland loss during 1990-2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998-2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fan</LastName><ForeName>Fenglei</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, GD, 510640, People's Republic of China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yunpeng</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Zhishi</ForeName><Initials>Z</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2007</Year><Month>06</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Environ Monit Assess</MedlineTA><NlmUniqueID>8508350</NlmUniqueID><ISSNLinking>0167-6369</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="Y">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N" Type="Geographic">China</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017753" MajorTopicYN="Y">Ecosystem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="Y">Environmental Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008390" MajorTopicYN="N">Markov Chains</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010781" MajorTopicYN="N">Photography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D045483" MajorTopicYN="N">Rivers</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018531" MajorTopicYN="N">Spacecraft</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2006</Year><Month>08</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2007</Year><Month>03</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2007</Year><Month>6</Month><Day>15</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2008</Year><Month>9</Month><Day>17</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2007</Year><Month>6</Month><Day>15</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">17564805</ArticleId><ArticleId IdType="doi">10.1007/s10661-007-9734-y</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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