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Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies

文献类型：期刊论文

作者Chen, Wei1; Li, Jiajia1; Wang, Dongliang2; Xu, Yameng; Liao, Xiaohan1,2; Wang, Qingpeng1; Chen, Zhenting3
刊名ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
出版日期2023-10-01
卷号30期号:48页码:106671-106686
关键词Facility agriculture Agricultural greenhouses Automatic extraction Remote sensing Deep learning
ISSN号0944-1344
DOI10.1007/s11356-023-29802-0
通讯作者Chen, Wei(chenw@cumtb.edu.cn)
英文摘要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 (similar to 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.
WOS关键词OBJECT-BASED CLASSIFICATION ; PLASTIC GREENHOUSES ; IMAGES ; DEMAND ; URBAN
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001129394300113
出版者SPRINGER HEIDELBERG
源URL[http://ir.igsnrr.ac.cn/handle/311030/201994]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Wei
作者单位1.China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
3.Kunming Univ, Sch Informat Engn, Kunming 650000, Yunnan, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Wei,Li, Jiajia,Wang, Dongliang,et al. Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2023,30(48):106671-106686.
APA Chen, Wei.,Li, Jiajia.,Wang, Dongliang.,Xu, Yameng.,Liao, Xiaohan.,...&Chen, Zhenting.(2023).Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(48),106671-106686.
MLA Chen, Wei,et al."Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.48(2023):106671-106686.

入库方式： OAI收割

来源：地理科学与资源研究所

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