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An index of non-sampling error in area frame sampling based on remote sensing data
Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237113/ https://www.ncbi.nlm.nih.gov/pubmed/30473929 http://dx.doi.org/10.7717/peerj.5824 |
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author | Wu, Mingquan Peng, Dailiang Qin, Yuchu Niu, Zheng Yang, Chenghai Li, Wang Hao, Pengyu Zhang, Chunyang |
author_facet | Wu, Mingquan Peng, Dailiang Qin, Yuchu Niu, Zheng Yang, Chenghai Li, Wang Hao, Pengyu Zhang, Chunyang |
author_sort | Wu, Mingquan |
collection | PubMed |
description | Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units. |
format | Online Article Text |
id | pubmed-6237113 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62371132018-11-23 An index of non-sampling error in area frame sampling based on remote sensing data Wu, Mingquan Peng, Dailiang Qin, Yuchu Niu, Zheng Yang, Chenghai Li, Wang Hao, Pengyu Zhang, Chunyang PeerJ Agricultural Science Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units. PeerJ Inc. 2018-11-12 /pmc/articles/PMC6237113/ /pubmed/30473929 http://dx.doi.org/10.7717/peerj.5824 Text en ©2018 Wu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Agricultural Science Wu, Mingquan Peng, Dailiang Qin, Yuchu Niu, Zheng Yang, Chenghai Li, Wang Hao, Pengyu Zhang, Chunyang An index of non-sampling error in area frame sampling based on remote sensing data |
title | An index of non-sampling error in area frame sampling based on remote sensing data |
title_full | An index of non-sampling error in area frame sampling based on remote sensing data |
title_fullStr | An index of non-sampling error in area frame sampling based on remote sensing data |
title_full_unstemmed | An index of non-sampling error in area frame sampling based on remote sensing data |
title_short | An index of non-sampling error in area frame sampling based on remote sensing data |
title_sort | index of non-sampling error in area frame sampling based on remote sensing data |
topic | Agricultural Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237113/ https://www.ncbi.nlm.nih.gov/pubmed/30473929 http://dx.doi.org/10.7717/peerj.5824 |
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