Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio
文献类型：期刊论文
| 作者 | Wang Fang1,2 ; Li Yixi1,3; Dong Ying1,3; Zhu Xiaohua4
|
| 刊名 | JOURNAL OF ARID LAND
 |
| 出版日期 | 2018-04-01 |
| 卷号 | 10期号:2页码:316-333 |
| 关键词 | administrative units geotagged photos landscape locality networks regional differences |
| ISSN号 | 1674-6767 |
| DOI | 10.1007/s40333-018-0003-4 |
| 通讯作者 | Wang Fang(wfphd@pku.edu.cn) |
| 英文摘要 | Cross-regional locality research reflects the influences of natural environment and the human activities due to the abundant land types and the multiple landscape combinations in related regions. The Chinese farming-pastoral ecotone is a typical large-scale region but few studies were conducted. This research contributed to the understanding of cross-regional locality of the Chinese farming-pastoral ecotone from different scales, including national, sectional, and provincial administrative units by utilizing geotagged photos (GTPs) obtained from the Panoramio website. The major results were as follows: (1) the locality elements of the Chinese farming-pastoral ecotone included 52 free nodes classified into 8 types of scene attributes; (2) there were huge differences between locality elements of different regions, and there was a negative correlation between the similarity degree of elements of different provinces and their spatial distances; (3) the Chinese farming-pastoral ecotone could be divided into the northern, central and southern sections, whose localities had differences in element constitution, association structure and the strength of elements, system stability and the anti-interference capability; and (4) the evolution of the localities of the northern and central sections was mainly influenced by human activities, while the locality of southern section retained more natural features. On a theoretical level, this research aimed to establish the research methodology of locality from the perspective of open data on the web with strong operability and replicability. On a practical level, this research could enrich the structuring recognition of the locality of the Chinese farming-pastoral ecotone and the comprehension of its dynamic mechanism. The results provide a reference for locality differentiation protection and the development of a cross-regional scale. |
| WOS关键词 | TWITTER ; LAND |
| 资助项目 | Sino-German Center (National Natural Science Foundation of China) ; Sino-German Center (German Science Foundation)[GZ1201] ; Postgraduate Courses Project of Peking University[2014-40] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000429038200011 |
| 出版者 | SPRINGER HEIDELBERG |
| 资助机构 | Sino-German Center (National Natural Science Foundation of China) ; Sino-German Center (German Science Foundation) ; Postgraduate Courses Project of Peking University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/57420]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang Fang |
| 作者单位 | 1.Peking Univ, Coll Architecture & Landscape Architecture, Sinogerman Joint Lab Urbanizat & Local Res UAL, Beijing 100871, Peoples R China 2.Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China 3.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang Fang,Li Yixi,Dong Ying,et al. Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio[J]. JOURNAL OF ARID LAND,2018,10(2):316-333. |
| APA | Wang Fang,Li Yixi,Dong Ying,&Zhu Xiaohua.(2018).Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio.JOURNAL OF ARID LAND,10(2),316-333. |
| MLA | Wang Fang,et al."Regional difference and dynamic mechanism of locality of the Chinese farming-pastoral ecotone based on geotagged photos from Panoramio".JOURNAL OF ARID LAND 10.2(2018):316-333. |
入库方式： OAI收割
来源：地理科学与资源研究所
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