Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies
Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenh...
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| Veröffentlicht in: | Environmental science and pollution research international 2023-10, Vol.30 (48), p.106671-106686 |
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| creator | Chen, Wei Li, Jiajia Wang, Dongliang Xu, Yameng Liao, Xiaohan Wang, Qingpeng Chen, Zhenting |
| description | Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km
2
, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management. |
| doi_str_mv | 10.1007/s11356-023-29802-0 |
| format | Article |
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2
, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management.</description><identifier>ISSN: 1614-7499</identifier><identifier>ISSN: 0944-1344</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-023-29802-0</identifier><identifier>PMID: 37733202</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>agricultural land ; Agricultural production ; Agriculture ; Agriculture - methods ; Algorithms ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; China ; Climate effects ; Conservation of Natural Resources ; Cultivated lands ; Deep Learning ; Earth and Environmental Science ; Ecotoxicology ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental management ; Environmental protection ; Farm buildings ; Greenhouses ; High resolution ; Image resolution ; Internet ; issues and policy ; Machine learning ; Regional development ; Remote sensing ; Remote Sensing Technology ; Research Article ; Spatial distribution ; Vegetables ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Environmental science and pollution research international, 2023-10, Vol.30 (48), p.106671-106686</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3103-3e6bb487bf21bdacfb6efbd11ede03d1c47604a97cbcca29f90bb524b55a4d353</cites><orcidid>0000-0002-2585-9984</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11356-023-29802-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11356-023-29802-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37733202$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Li, Jiajia</creatorcontrib><creatorcontrib>Wang, Dongliang</creatorcontrib><creatorcontrib>Xu, Yameng</creatorcontrib><creatorcontrib>Liao, Xiaohan</creatorcontrib><creatorcontrib>Wang, Qingpeng</creatorcontrib><creatorcontrib>Chen, Zhenting</creatorcontrib><title>Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies</title><title>Environmental science and pollution research international</title><addtitle>Environ Sci Pollut Res</addtitle><addtitle>Environ Sci Pollut Res Int</addtitle><description>Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km
2
, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management.</description><subject>agricultural land</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Agriculture - methods</subject><subject>Algorithms</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>China</subject><subject>Climate effects</subject><subject>Conservation of Natural Resources</subject><subject>Cultivated lands</subject><subject>Deep Learning</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental management</subject><subject>Environmental protection</subject><subject>Farm buildings</subject><subject>Greenhouses</subject><subject>High resolution</subject><subject>Image resolution</subject><subject>Internet</subject><subject>issues and policy</subject><subject>Machine learning</subject><subject>Regional development</subject><subject>Remote sensing</subject><subject>Remote Sensing Technology</subject><subject>Research Article</subject><subject>Spatial distribution</subject><subject>Vegetables</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1614-7499</issn><issn>0944-1344</issn><issn>1614-7499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkTtvFDEURi0EIiHwBygiSzQ0Dn6Od0oU8Yi0Eg3Ulh93Zify2Is9IyU1fzzebAgoBVS27PN9V1cHobeMXjBK9YfKmFAdoVwQ3m8oJ_QZOmUdk0TLvn_-1_0Evar1mlJOe65fohOhtRCc8lP0a2vLCKR6GwHbdcmzXSaP4WYp1i9TTjgP2I5l8mtc1mIjHgtA2uW1QsXOVgi4Qbtp3JECNcf1PlRgzgvgCqlOacQ2BRwA9jiCLenwsoDfpRzzOEF9jV4MNlZ483CeoR-fP32__Eq2375cXX7cEi8YFURA55zcaDdw5oL1g-tgcIExCEBFYF7qjkrba--8t7wfeuqc4tIpZWUQSpyh98fefck_V6iLmafqIUaboO1jBFNCKblh8r8o33SaKdbzQ-u7J-h1XktqizRKa6U5l7xR_Ej5kmstMJh9mWZbbg2j5mDTHG2aZtPc2zS0hc4fqlc3Q3iM_NbXAHEEavtKI5Q_s_9Rewe67K1X</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Chen, Wei</creator><creator>Li, Jiajia</creator><creator>Wang, Dongliang</creator><creator>Xu, Yameng</creator><creator>Liao, Xiaohan</creator><creator>Wang, Qingpeng</creator><creator>Chen, Zhenting</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SN</scope><scope>7T7</scope><scope>7TV</scope><scope>7U7</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-2585-9984</orcidid></search><sort><creationdate>20231001</creationdate><title>Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies</title><author>Chen, Wei ; Li, Jiajia ; Wang, Dongliang ; Xu, Yameng ; Liao, Xiaohan ; Wang, Qingpeng ; Chen, Zhenting</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3103-3e6bb487bf21bdacfb6efbd11ede03d1c47604a97cbcca29f90bb524b55a4d353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>agricultural land</topic><topic>Agricultural production</topic><topic>Agriculture</topic><topic>Agriculture - 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Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environmental science and pollution research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Wei</au><au>Li, Jiajia</au><au>Wang, Dongliang</au><au>Xu, Yameng</au><au>Liao, Xiaohan</au><au>Wang, Qingpeng</au><au>Chen, Zhenting</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies</atitle><jtitle>Environmental science and pollution research international</jtitle><stitle>Environ Sci Pollut Res</stitle><addtitle>Environ Sci Pollut Res Int</addtitle><date>2023-10-01</date><risdate>2023</risdate><volume>30</volume><issue>48</issue><spage>106671</spage><epage>106686</epage><pages>106671-106686</pages><issn>1614-7499</issn><issn>0944-1344</issn><eissn>1614-7499</eissn><abstract>Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km
2
, accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37733202</pmid><doi>10.1007/s11356-023-29802-0</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-2585-9984</orcidid></addata></record> |
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| subjects | agricultural land Agricultural production Agriculture Agriculture - methods Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution China Climate effects Conservation of Natural Resources Cultivated lands Deep Learning Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental management Environmental protection Farm buildings Greenhouses High resolution Image resolution Internet issues and policy Machine learning Regional development Remote sensing Remote Sensing Technology Research Article Spatial distribution Vegetables Waste Water Technology Water Management Water Pollution Control |
| title | Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies |
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