Mapping global distribution of mangrove forests at 10-m resolution

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, exi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Science bulletin 2023-06, Vol.68 (12), p.1306-1316
Hauptverfasser: Jia, Mingming, Wang, Zongming, Mao, Dehua, Ren, Chunying, Song, Kaishan, Zhao, Chuanpeng, Wang, Chao, Xiao, Xiangming, Wang, Yeqiao
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1316
container_issue 12
container_start_page 1306
container_title Science bulletin
container_volume 68
creator Jia, Mingming
Wang, Zongming
Mao, Dehua
Ren, Chunying
Song, Kaishan
Zhao, Chuanpeng
Wang, Chao
Xiao, Xiangming
Wang, Yeqiao
description Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.
doi_str_mv 10.1016/j.scib.2023.05.004
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2818053661</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2095927323003110</els_id><sourcerecordid>2818053661</sourcerecordid><originalsourceid>FETCH-LOGICAL-c400t-4d5bcb5ba5f245d48f4a845b4721aa8184ec77ce6dfbc07c2621a68b0f4aa7b03</originalsourceid><addsrcrecordid>eNp9kD1PwzAQhi0EolXpH2BAGVkSzo4dJxILVHxJRSwwW7bjVK6SuNhJJf49DoWOTOeznnt19yB0iSHDgIubbRa0VRkBkmfAMgB6guYEKpZWpMSnxzfPZ2gZwhYAMK0IBX6OZjknmFNSzdH9q9ztbL9JNq1Tsk1qGwZv1ThY1yeuSTrZb7zbm6Rx3oQhJHJIMKRdEjvX_mAX6KyRbTDL37pAH48P76vndP329LK6W6eaAgwprZnSiinJGkJZTcuGypIyReMuUpa4pEZzrk1RN0oD16SI_0WpIHKSK8gX6PqQu_Puc4zLiM4GbdpW9saNQcSzS2B5UeCIkgOqvQvBm0bsvO2k_xIYxKRPbMWkT0z6BDAR9cWhq9_8UXWmPo78yYrA7QEw8cq9NX7KML02tfVGD6J29r_8b64GgIY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2818053661</pqid></control><display><type>article</type><title>Mapping global distribution of mangrove forests at 10-m resolution</title><source>Alma/SFX Local Collection</source><creator>Jia, Mingming ; Wang, Zongming ; Mao, Dehua ; Ren, Chunying ; Song, Kaishan ; Zhao, Chuanpeng ; Wang, Chao ; Xiao, Xiangming ; Wang, Yeqiao</creator><creatorcontrib>Jia, Mingming ; Wang, Zongming ; Mao, Dehua ; Ren, Chunying ; Song, Kaishan ; Zhao, Chuanpeng ; Wang, Chao ; Xiao, Xiangming ; Wang, Yeqiao</creatorcontrib><description>Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.</description><identifier>ISSN: 2095-9273</identifier><identifier>EISSN: 2095-9281</identifier><identifier>DOI: 10.1016/j.scib.2023.05.004</identifier><identifier>PMID: 37217429</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Object-based image analysis ; Ramsar convention sites ; Remote sensing ; Sentinel-2 ; World heritage sites</subject><ispartof>Science bulletin, 2023-06, Vol.68 (12), p.1306-1316</ispartof><rights>2023</rights><rights>Copyright © 2023. Published by Elsevier B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-4d5bcb5ba5f245d48f4a845b4721aa8184ec77ce6dfbc07c2621a68b0f4aa7b03</citedby><cites>FETCH-LOGICAL-c400t-4d5bcb5ba5f245d48f4a845b4721aa8184ec77ce6dfbc07c2621a68b0f4aa7b03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27907,27908</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37217429$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jia, Mingming</creatorcontrib><creatorcontrib>Wang, Zongming</creatorcontrib><creatorcontrib>Mao, Dehua</creatorcontrib><creatorcontrib>Ren, Chunying</creatorcontrib><creatorcontrib>Song, Kaishan</creatorcontrib><creatorcontrib>Zhao, Chuanpeng</creatorcontrib><creatorcontrib>Wang, Chao</creatorcontrib><creatorcontrib>Xiao, Xiangming</creatorcontrib><creatorcontrib>Wang, Yeqiao</creatorcontrib><title>Mapping global distribution of mangrove forests at 10-m resolution</title><title>Science bulletin</title><addtitle>Sci Bull (Beijing)</addtitle><description>Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.</description><subject>Object-based image analysis</subject><subject>Ramsar convention sites</subject><subject>Remote sensing</subject><subject>Sentinel-2</subject><subject>World heritage sites</subject><issn>2095-9273</issn><issn>2095-9281</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EolXpH2BAGVkSzo4dJxILVHxJRSwwW7bjVK6SuNhJJf49DoWOTOeznnt19yB0iSHDgIubbRa0VRkBkmfAMgB6guYEKpZWpMSnxzfPZ2gZwhYAMK0IBX6OZjknmFNSzdH9q9ztbL9JNq1Tsk1qGwZv1ThY1yeuSTrZb7zbm6Rx3oQhJHJIMKRdEjvX_mAX6KyRbTDL37pAH48P76vndP329LK6W6eaAgwprZnSiinJGkJZTcuGypIyReMuUpa4pEZzrk1RN0oD16SI_0WpIHKSK8gX6PqQu_Puc4zLiM4GbdpW9saNQcSzS2B5UeCIkgOqvQvBm0bsvO2k_xIYxKRPbMWkT0z6BDAR9cWhq9_8UXWmPo78yYrA7QEw8cq9NX7KML02tfVGD6J29r_8b64GgIY</recordid><startdate>20230630</startdate><enddate>20230630</enddate><creator>Jia, Mingming</creator><creator>Wang, Zongming</creator><creator>Mao, Dehua</creator><creator>Ren, Chunying</creator><creator>Song, Kaishan</creator><creator>Zhao, Chuanpeng</creator><creator>Wang, Chao</creator><creator>Xiao, Xiangming</creator><creator>Wang, Yeqiao</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20230630</creationdate><title>Mapping global distribution of mangrove forests at 10-m resolution</title><author>Jia, Mingming ; Wang, Zongming ; Mao, Dehua ; Ren, Chunying ; Song, Kaishan ; Zhao, Chuanpeng ; Wang, Chao ; Xiao, Xiangming ; Wang, Yeqiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-4d5bcb5ba5f245d48f4a845b4721aa8184ec77ce6dfbc07c2621a68b0f4aa7b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Object-based image analysis</topic><topic>Ramsar convention sites</topic><topic>Remote sensing</topic><topic>Sentinel-2</topic><topic>World heritage sites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Mingming</creatorcontrib><creatorcontrib>Wang, Zongming</creatorcontrib><creatorcontrib>Mao, Dehua</creatorcontrib><creatorcontrib>Ren, Chunying</creatorcontrib><creatorcontrib>Song, Kaishan</creatorcontrib><creatorcontrib>Zhao, Chuanpeng</creatorcontrib><creatorcontrib>Wang, Chao</creatorcontrib><creatorcontrib>Xiao, Xiangming</creatorcontrib><creatorcontrib>Wang, Yeqiao</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Science bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Mingming</au><au>Wang, Zongming</au><au>Mao, Dehua</au><au>Ren, Chunying</au><au>Song, Kaishan</au><au>Zhao, Chuanpeng</au><au>Wang, Chao</au><au>Xiao, Xiangming</au><au>Wang, Yeqiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping global distribution of mangrove forests at 10-m resolution</atitle><jtitle>Science bulletin</jtitle><addtitle>Sci Bull (Beijing)</addtitle><date>2023-06-30</date><risdate>2023</risdate><volume>68</volume><issue>12</issue><spage>1306</spage><epage>1316</epage><pages>1306-1316</pages><issn>2095-9273</issn><eissn>2095-9281</eissn><abstract>Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km2 mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>37217429</pmid><doi>10.1016/j.scib.2023.05.004</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2095-9273
ispartof Science bulletin, 2023-06, Vol.68 (12), p.1306-1316
issn 2095-9273
2095-9281
language eng
recordid cdi_proquest_miscellaneous_2818053661
source Alma/SFX Local Collection
subjects Object-based image analysis
Ramsar convention sites
Remote sensing
Sentinel-2
World heritage sites
title Mapping global distribution of mangrove forests at 10-m resolution
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T19%3A37%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mapping%20global%20distribution%20of%20mangrove%20forests%20at%2010-m%20resolution&rft.jtitle=Science%20bulletin&rft.au=Jia,%20Mingming&rft.date=2023-06-30&rft.volume=68&rft.issue=12&rft.spage=1306&rft.epage=1316&rft.pages=1306-1316&rft.issn=2095-9273&rft.eissn=2095-9281&rft_id=info:doi/10.1016/j.scib.2023.05.004&rft_dat=%3Cproquest_cross%3E2818053661%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2818053661&rft_id=info:pmid/37217429&rft_els_id=S2095927323003110&rfr_iscdi=true