Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products
Global land cover (GLC) products have been widely used in land use and land cover change (LUCC). It is an urgent need to investigate the harmonization and comparative methods for different products derived from various satellite imagery and classification schemes. MCD12Q1 and GlobCover were comparat...
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description | Global land cover (GLC) products have been widely used in land use and land cover change (LUCC). It is an urgent need to investigate the harmonization and comparative methods for different products derived from various satellite imagery and classification schemes. MCD12Q1 and GlobCover were comparatively used to identify the changes of land cover at a provincial scale in China. The European Space Agency (ESA) GlobCover and the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) datasets of 2005 and 2009 were used and compared comprehensively. The two products were harmonized and reclassified into six types at 500-m resolution, namely, forestland, grassland, cropland, wetland, artificial area, and others. Four aspects of land cover changing rate (
K
), land use integrated index (
L
d
), land use dynamic degree (
LUDD
), and transfer analysis were respectively identified and analyzed. The analysis results show that the two datasets have similar mapping capabilities in monitoring the LUCC, but there are also obvious differences due to the classification schemes, especially for the forestland and grassland. The
K
of forestland and grassland for GlobCover is respectively − 4.94 and 2.90%, while they are 7.63 and − 47.73% for MCD12Q1. The
L
d
difference is, respectively, 10 and 1 for MCD12Q1 and GlobCover. The
LUDD
in Anhui Province from 2005 to 2009 is 3.06% for MCD12Q1 and 0.90% for GlobCover. Additionally, the transfer matrix is also constructed to explore the land cover changes. The significant differences between the two datasets are attributed to forestland and grassland. We conclude that the differences are primarily ascribed to the classification systems. The study provides an important reference for identifying provincial LUCC by jointly using different GLC products. |
doi_str_mv | 10.1007/s12517-020-05780-2 |
format | Article |
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K
), land use integrated index (
L
d
), land use dynamic degree (
LUDD
), and transfer analysis were respectively identified and analyzed. The analysis results show that the two datasets have similar mapping capabilities in monitoring the LUCC, but there are also obvious differences due to the classification schemes, especially for the forestland and grassland. The
K
of forestland and grassland for GlobCover is respectively − 4.94 and 2.90%, while they are 7.63 and − 47.73% for MCD12Q1. The
L
d
difference is, respectively, 10 and 1 for MCD12Q1 and GlobCover. The
LUDD
in Anhui Province from 2005 to 2009 is 3.06% for MCD12Q1 and 0.90% for GlobCover. Additionally, the transfer matrix is also constructed to explore the land cover changes. The significant differences between the two datasets are attributed to forestland and grassland. We conclude that the differences are primarily ascribed to the classification systems. The study provides an important reference for identifying provincial LUCC by jointly using different GLC products.</description><identifier>ISSN: 1866-7511</identifier><identifier>EISSN: 1866-7538</identifier><identifier>DOI: 10.1007/s12517-020-05780-2</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Agricultural land ; Classification ; Classification schemes ; Classification systems ; Datasets ; Earth and Environmental Science ; Earth science ; Earth Sciences ; Forests ; Grasslands ; Image classification ; Imagery ; Land cover ; Land use ; Mapping ; Original Paper ; Resolution ; Satellite imagery ; Satellites ; Spaceborne remote sensing ; Spectroradiometers ; Transfer matrices</subject><ispartof>Arabian journal of geosciences, 2020-08, Vol.13 (16), Article 792</ispartof><rights>Saudi Society for Geosciences 2020</rights><rights>Saudi Society for Geosciences 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-49971c3710c0a0916c1a6b5ba267c378e0bdc7715abefb76d3f6b4b5c4d1d4d83</citedby><cites>FETCH-LOGICAL-c368t-49971c3710c0a0916c1a6b5ba267c378e0bdc7715abefb76d3f6b4b5c4d1d4d83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12517-020-05780-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12517-020-05780-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Zhao, Jingling</creatorcontrib><creatorcontrib>Dong, Yingying</creatorcontrib><creatorcontrib>Zhang, Mingmei</creatorcontrib><creatorcontrib>Huang, Linsheng</creatorcontrib><title>Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products</title><title>Arabian journal of geosciences</title><addtitle>Arab J Geosci</addtitle><description>Global land cover (GLC) products have been widely used in land use and land cover change (LUCC). It is an urgent need to investigate the harmonization and comparative methods for different products derived from various satellite imagery and classification schemes. MCD12Q1 and GlobCover were comparatively used to identify the changes of land cover at a provincial scale in China. The European Space Agency (ESA) GlobCover and the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) datasets of 2005 and 2009 were used and compared comprehensively. The two products were harmonized and reclassified into six types at 500-m resolution, namely, forestland, grassland, cropland, wetland, artificial area, and others. Four aspects of land cover changing rate (
K
), land use integrated index (
L
d
), land use dynamic degree (
LUDD
), and transfer analysis were respectively identified and analyzed. The analysis results show that the two datasets have similar mapping capabilities in monitoring the LUCC, but there are also obvious differences due to the classification schemes, especially for the forestland and grassland. The
K
of forestland and grassland for GlobCover is respectively − 4.94 and 2.90%, while they are 7.63 and − 47.73% for MCD12Q1. The
L
d
difference is, respectively, 10 and 1 for MCD12Q1 and GlobCover. The
LUDD
in Anhui Province from 2005 to 2009 is 3.06% for MCD12Q1 and 0.90% for GlobCover. Additionally, the transfer matrix is also constructed to explore the land cover changes. The significant differences between the two datasets are attributed to forestland and grassland. We conclude that the differences are primarily ascribed to the classification systems. The study provides an important reference for identifying provincial LUCC by jointly using different GLC products.</description><subject>Agricultural land</subject><subject>Classification</subject><subject>Classification schemes</subject><subject>Classification systems</subject><subject>Datasets</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>Forests</subject><subject>Grasslands</subject><subject>Image classification</subject><subject>Imagery</subject><subject>Land cover</subject><subject>Land use</subject><subject>Mapping</subject><subject>Original Paper</subject><subject>Resolution</subject><subject>Satellite imagery</subject><subject>Satellites</subject><subject>Spaceborne remote sensing</subject><subject>Spectroradiometers</subject><subject>Transfer matrices</subject><issn>1866-7511</issn><issn>1866-7538</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOKd_wKuA19EkbT56KVWnMBFBr0OSprOja7qkFfbvzVZRr7w6h8PzvgceAC4JviYYi5tIKCMCYYoRZkJiRI_AjEjOkWCZPP7ZCTkFZzGuMeYSCzkD29Jveh2a6Dvoa9hUrhuaetd0K9jqroLWf7oAB7fpPYq9HhrdQvuhu5WLcIx7bNF6Ux6oPf9c3hH6SuAqXRP6p6MPvhrtEM_BSa3b6C6-5xy8P9y_lY9o-bJ4Km-XyGZcDigvCkFsJgi2WOOCcEs0N8xoykU6S4dNZYUgTBtXG8GrrOYmN8zmFanySmZzcDX1psfb0cVBrf0YuvRS0TyjsmBMFomiE2WDjzG4WvWh2eiwUwSrvVo1qVVJrTqoVTSFsikUE5xUhN_qf1JfwMJ82A</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Zhao, Jingling</creator><creator>Dong, Yingying</creator><creator>Zhang, Mingmei</creator><creator>Huang, Linsheng</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>20200801</creationdate><title>Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products</title><author>Zhao, Jingling ; Dong, Yingying ; Zhang, Mingmei ; Huang, Linsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-49971c3710c0a0916c1a6b5ba267c378e0bdc7715abefb76d3f6b4b5c4d1d4d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agricultural land</topic><topic>Classification</topic><topic>Classification schemes</topic><topic>Classification systems</topic><topic>Datasets</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>Forests</topic><topic>Grasslands</topic><topic>Image classification</topic><topic>Imagery</topic><topic>Land cover</topic><topic>Land use</topic><topic>Mapping</topic><topic>Original Paper</topic><topic>Resolution</topic><topic>Satellite imagery</topic><topic>Satellites</topic><topic>Spaceborne remote sensing</topic><topic>Spectroradiometers</topic><topic>Transfer matrices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Jingling</creatorcontrib><creatorcontrib>Dong, Yingying</creatorcontrib><creatorcontrib>Zhang, Mingmei</creatorcontrib><creatorcontrib>Huang, Linsheng</creatorcontrib><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Arabian journal of geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Jingling</au><au>Dong, Yingying</au><au>Zhang, Mingmei</au><au>Huang, Linsheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products</atitle><jtitle>Arabian journal of geosciences</jtitle><stitle>Arab J Geosci</stitle><date>2020-08-01</date><risdate>2020</risdate><volume>13</volume><issue>16</issue><artnum>792</artnum><issn>1866-7511</issn><eissn>1866-7538</eissn><abstract>Global land cover (GLC) products have been widely used in land use and land cover change (LUCC). It is an urgent need to investigate the harmonization and comparative methods for different products derived from various satellite imagery and classification schemes. MCD12Q1 and GlobCover were comparatively used to identify the changes of land cover at a provincial scale in China. The European Space Agency (ESA) GlobCover and the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) datasets of 2005 and 2009 were used and compared comprehensively. The two products were harmonized and reclassified into six types at 500-m resolution, namely, forestland, grassland, cropland, wetland, artificial area, and others. Four aspects of land cover changing rate (
K
), land use integrated index (
L
d
), land use dynamic degree (
LUDD
), and transfer analysis were respectively identified and analyzed. The analysis results show that the two datasets have similar mapping capabilities in monitoring the LUCC, but there are also obvious differences due to the classification schemes, especially for the forestland and grassland. The
K
of forestland and grassland for GlobCover is respectively − 4.94 and 2.90%, while they are 7.63 and − 47.73% for MCD12Q1. The
L
d
difference is, respectively, 10 and 1 for MCD12Q1 and GlobCover. The
LUDD
in Anhui Province from 2005 to 2009 is 3.06% for MCD12Q1 and 0.90% for GlobCover. Additionally, the transfer matrix is also constructed to explore the land cover changes. The significant differences between the two datasets are attributed to forestland and grassland. We conclude that the differences are primarily ascribed to the classification systems. The study provides an important reference for identifying provincial LUCC by jointly using different GLC products.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s12517-020-05780-2</doi></addata></record> |
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subjects | Agricultural land Classification Classification schemes Classification systems Datasets Earth and Environmental Science Earth science Earth Sciences Forests Grasslands Image classification Imagery Land cover Land use Mapping Original Paper Resolution Satellite imagery Satellites Spaceborne remote sensing Spectroradiometers Transfer matrices |
title | Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products |
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