An index of non-sampling error in area frame sampling based on remote sensing data

Mingquan Wu; Dailiang Peng; Yuchu Qin; Zheng Niu; Chenghai Yang; Wang Li; Pengyu Hao; Chunyang Zhang

doi:10.7717/peerj.5824

An index of non-sampling error in area frame sampling based on remote sensing data

PeerJ. 2018 Nov 12:6:e5824. doi: 10.7717/peerj.5824. eCollection 2018.

Authors

Mingquan Wu¹, Dailiang Peng², Yuchu Qin¹, Zheng Niu¹, Chenghai Yang³, Wang Li¹, Pengyu Hao⁴, Chunyang Zhang⁵

Affiliations

¹ The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
² Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
³ Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX, United States of America.
⁴ Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, China/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beiijng, China.
⁵ National Engineering Center for Geoinformatics, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.

Abstract

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.

Keywords: Crop area statistics; Crops; Landsat; Non-sampling errors; Remote sensing.

Grants and funding

This project was conducted as part of a visiting scholar research program. The first author was financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA19030404), the Youth Innovation Promotion Association CAS (2017089), the China Scholarship Council (201504910261), the National Natural Science Foundation of China (41301390), the National Science and Technology Major Project (2014AA06A511), and the Major State Basic Research Development Program of China (2013CB733405 and 2010CB950603). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.