JOURNAL OF NATURAL RESOURCES ›› 2016, Vol. 31 ›› Issue (10): 1773-1782.doi: 10.11849/zrzyxb.20151259

• Resource Research Method • Previous Articles     Next Articles

Spatialization of Statistical Crop Planting Area Based on Geographical Regression

XIA Tian1, 2, WU Wen-bin2, ZHOU Qing-bo2, ZHOU Yong1, LUO Jing1, YANG Peng2, LI Zheng-guo2   

  1. 1. Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province/ College of Urban & Environmental Science, Central China Normal University, Wuhan 430079, China;
    2. Key Laboratory of Agri-Informatics, Ministry of Agriculture / Institute of Agricultural Resources and RegionalPlanning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2015-11-16 Revised:2016-03-01 Online:2016-10-20 Published:2016-10-20
  • Supported by:
    National Natural Science Foundation of China, No.41201089 and 41271112; Fundamental Research Funds for the Central Universities, No.CCNU15A05058

Abstract: The sptial pattern of crops reflects the planting structure and characteristics of crops, which is an important basis for understanding agricultural resource utilization and adjusting crop planting structure. This study aims to explore the method for specializing statistical data of crop planting area, and thus spatially express historial agricultural statistics data. This study used the traditional agricultural statistical survey data and remote sensing imagery data with geographic information technologies. The spatial probability distributions of suitabilities of crops are estimated using the Binary Logistic regression analysis that characterizes the relationships between the crop planting structure and the geographical factors as well as social-economic factors. Based on the spatial probability distribution, the statistical data of crop planting area were spatially distributed by using spatial iteratative allocation. Northeast China was taken as the study area and the spatial expression of sown area in this area during 2000-2010 was completed. The spatial accuracy of 0.76 was achieved by using this multi-scale and multi-resolution analysis method, which demonstrated it is superior in spatially expressing statistical data of crop planting. The method can be taken as an effective complement for crop field survey and remote sensing-based crop interpretation, and thus provides novel technical means for enriching crop spatial data.

Key words: crop, geographical regression, planting area, spatialization, statistical data

CLC Number: 

  • F302.5