Global mean estimation using a self-organizing dual-zoning method for preferential sampling
文献类型： 外文期刊
作者： Pan, Yuchun 1 ; Ren, Xuhong 3 ; Gao, Bingbo 1 ; Liu, Yu 1 ; Gao, YunBing 1 ; Hao, Xingyao 1 ; Chen, Ziyue;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.North China Inst Aerosp Engn, Dept Comp Sci & Engn, Langfang City 065000, Hebei Province, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci
关键词： Preferential sampling;Global mean estimation;Self-organizing dual-zoning method
期刊名称：ENVIRONMENTAL MONITORING AND ASSESSMENT （ 影响因子：2.513； 五年影响因子：2.871 ）
ISSN：
年卷期：
页码：
收录情况： SCI
摘要： Giving an appropriate weight to each sampling point is essential to global mean estimation. The objective of this paper was to develop a global mean estimation method with preferential samples. The procedure for this estimation method was to first zone the study area based on self-organizing dual-zoning method and then to estimate the mean according to stratified sampling method. In this method, spreading of points in both feature and geographical space is considered. The method is tested in a case study on the metal Mn concentrations in Jilin provinces of China. Six sample patterns are selected to estimate the global mean and compared with the global mean calculated by direct arithmetic mean method, polygon method, and cell method. The results show that the proposed method produces more accurate and stable mean estimates under different feature deviation index (FDI) values and sample sizes. The relative errors of the global mean calculated by the proposed method are from 0.14 to 1.47 % and they are the largest (4.83-8.84 %) by direct arithmetic mean method. At the same time, the mean results calculated by the other three methods are sensitive to the FDI values and sample sizes.
- 相关文献
作者其他论文 更多>>
-
Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm
作者：Tang, Boyi;Zhao, Chunjiang;Tang, Boyi;Zhou, Jingping;Pan, Yuchun;Qu, Xuzhou;Cui, Yanglin;Liu, Chang;Li, Xuguang;Zhao, Chunjiang;Gu, Xiaohe;Li, Xuguang
关键词：Object detection; Maize seedlings; UAV RGB images; YOLOv5; Attention mechanism
-
Extraction of the upright maize straw by integrating UAV multispectral and DSM data
作者：Chao, Aosheng;Xing, Enguang;Gao, Yunbing;Li, Cunjun;Qin, Yuan;Zhu, Chengyang;Liu, Yu;Chao, Aosheng;Zhu, Chengyang;Zhu, Qingwei
关键词：Upright maize straw; UAV; New straw index; Spectral characteristics; Digital surface model
-
Estimation of SOC using VNIR and MIR hyperspectral data based on spectral-to-image transforming and multi-channel CNN
作者：Tang, Aohua;Yang, Guijun;Li, Zhenhong;Chen, Weinan;Zhang, Jing;Tang, Aohua;Yang, Guijun;Pan, Yuchun;Liu, Yu;Long, Huiling;Chen, Weinan;Zhang, Jing;Yang, Yue;Yang, Xiaodong;Xu, Bo;Yang, Yue
关键词：MIR spectral; Multi-channel-CNN; SIT; Soil organic carbon; VNIR spectral
-
Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed
作者：Tang, Boyi;Zhou, Jingping;Zhao, Chunjiang;Pan, Yuchun;Lu, Yao;Liu, Chang;Ma, Kai;Sun, Xuguang;Gu, Xiaohe;Tang, Boyi;Zhou, Jingping;Zhang, Ruifang
关键词：Object detection; Maize seedlings; Weed disturbance; YOLO; UAV multispectral images
-
Two sexually compatible monokaryons from a heterokaryotic Lentinula edodes strain respond differently to heat stress
作者：Guo, Yuan;Gao, Qi;Liu, Yu;Wang, Shouxian;Jiao, Wenyu;Zhang, Yajie;Tan, Meiting
关键词：
Lentinula edodes ; heat stress response; metabolomics; transcriptomics; multi-omics integration -
A machine learning system to evaluate physiological parameters and heat stress for sows in gestation crates
作者：Zhuang, Yanrong;Ji, Hengyi;Liu, Yu;Li, Shulei;Wang, Chaoyuan;Teng, Guanghui;Zhuang, Yanrong;Ji, Hengyi;Liu, Yu;Li, Shulei;Wang, Chaoyuan;Teng, Guanghui;Zhuang, Yanrong;Ji, Hengyi;Liu, Yu;Li, Shulei;Wang, Chaoyuan;Teng, Guanghui;Zhuang, Yanrong;Zhuang, Yanrong;Cao, Mengbing;Zhang, Jinrui
关键词：Sow; Heat stress; Physiological parameters prediction; Machine learning; LabVIEW
-
Improving Winter Wheat Yield Estimation Under Saline Stress by Integrating Sentinel-2 and Soil Salt Content Using Random Forest
作者：Lu, Chuang;Dong, Shiwei;Li, Yinkun;Lu, Chuang;Dong, Shiwei;Liu, Yu;Pan, Yuchun;Yang, Maowei
关键词：yield estimation; saline stress; growth period; vegetation index; salt index; random forest
