Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018
文献类型：期刊论文
| Author | Ou, Cong2,3,4; Yang, Jianyu2,4; Du, Zhenrong1,2,4; Zhang, Tingting2,4; Niu, Bowen2,4; Feng, Quanlong2,4; Liu, Yiming2,4,5; Zhu, Dehai2,4 |
| Source | REMOTE SENSING
 |
| Issued Date | 2021-12-01 |
| Volume | 13Issue:23Pages:22 |
| Keyword | agricultural greenhouse annual mapping Landsat Google Earth Engine |
| DOI | 10.3390/rs13234830 |
| Corresponding Author | Yang, Jianyu(ycjyyang@cau.edu.cn) |
| English Abstract | Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User's Accuracy, Producer's Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of "Human-Nature" interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture. |
| WOS Keyword | ANNUAL URBAN-DYNAMICS ; REMOTE-SENSING DATA ; VEGETATION INDEX ; TIME-SERIES ; SATELLITE IMAGERY ; SURFACE-WATER ; 8 OLI ; COVER ; CLASSIFICATION ; NDVI |
| Funding Project | Ministry of land and resources industry public welfare projects[201511010-06] |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| Language | 英语 |
| WOS ID | WOS:000735108000001 |
| Publisher | MDPI |
| Funding Organization | Ministry of land and resources industry public welfare projects |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/168995]  |
| Collection | 中国科学院地理科学与资源研究所 |
| Corresponding Author | Yang, Jianyu |
| Affiliation | 1.Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ,Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China 2.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Minist Nat Resources, Key Lab Agr Land Qual Monitoring & Control, Beijing 100083, Peoples R China 5.China Mobile Commun Grp Guangdong Co Ltd, Ctr Prod Res & Dev, Guangzhou 510623, Peoples R China |
| Recommended Citation GB/T 7714 | Ou, Cong,Yang, Jianyu,Du, Zhenrong,et al. Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018[J]. REMOTE SENSING,2021,13(23):22. |
| APA | Ou, Cong.,Yang, Jianyu.,Du, Zhenrong.,Zhang, Tingting.,Niu, Bowen.,...&Zhu, Dehai.(2021).Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018.REMOTE SENSING,13(23),22. |
| MLA | Ou, Cong,et al."Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018".REMOTE SENSING 13.23(2021):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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