Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 103-108, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B8/103/2012/
doi:10.5194/isprsarchives-XXXIX-B8-103-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
27 Jul 2012
OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE
J. Feng1,3, L. Bai2, S. Liu1,3, X. Su3, and H. Hu3 1Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, Beijing, 100081, China
2Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Beijing, 100101, China
3Institute of Agriculture Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Keywords: Agricultural Spatial Sampling, Remote Sensing, a Priori Knowledge, Spatial Structure Characteristics, RIP(s)/RIV(s), Sampling efficiency Abstract. In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.
Conference paper (PDF, 446 KB)


Citation: Feng, J., Bai, L., Liu, S., Su, X., and Hu, H.: OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 103-108, doi:10.5194/isprsarchives-XXXIX-B8-103-2012, 2012.

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