| Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site | |
Yin, Gaofei1 ; Li, Ainong1; Verger, Aleixandre2
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| 2017 | |
| Source Publication | REMOTE SENSING
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| EISSN | 2072-4292 |
| Volume | 9Issue:12 |
| Subtype | Article |
| Abstract | Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI) imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA) between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study in Wanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7%) compared to the single-temporally constrained (OA = 68.7%) and the random sampling (OA = 63.1%) strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet) which need a spatially distributed and temporally fixed sampling design. |
| Keyword | Spatiotemporal Representative Cost-efficient Sampling Design Heterogeneity Validation |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.3390/rs9121217 |
| WOS Subject Extended | Remote Sensing |
| Indexed By | SCI |
| WOS Keyword | LEAF-AREA INDEX ; FRACTIONAL VEGETATION COVER ; REMOTE-SENSING DATA ; SPATIAL HETEROGENEITY ; SURFACE REFLECTANCE ; PRODUCT VALIDATION ; LAI ; VARIABILITY ; DERIVATION ; MAPS |
| Language | 英语 |
| Quartile | 2区 |
| TOP | 否 |
| WOS Subject | Remote Sensing |
| WOS ID | WOS:000419235700013 |
| Funding Organization | National Key Research and Development Program of China(2016YFA0600103) ; National Natural Science Foundation of China(41601403 ; Youth Talent Team Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences(SDSQB-2015-02) ; CAS "Light of West China" Program ; EC(CGLOPS-1 ; 41631180 ; 199494-JRC) ; 41571373 ; 41531174) |
| Publisher | MDPI AG |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/20756 |
| Collection | 数字山地与遥感应用中心 |
| Corresponding Author | Li, Ainong |
| Affiliation | 1.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Sichuan, Peoples R China 2.CREAF, E-08193 Catalonia, Spain |
| First Author Affilication | 中国科学院水利部成都山地灾害与环境研究所 |
| Corresponding Author Affilication | 中国科学院水利部成都山地灾害与环境研究所 |
| Recommended Citation GB/T 7714 | Yin, Gaofei,Li, Ainong,Verger, Aleixandre. Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site[J]. REMOTE SENSING,2017,9(12). |
| APA | Yin, Gaofei,Li, Ainong,&Verger, Aleixandre.(2017).Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site.REMOTE SENSING,9(12). |
| MLA | Yin, Gaofei,et al."Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site".REMOTE SENSING 9.12(2017). |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Spatiotemporally Rep（1330KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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