Abstract
Abstract. The purpose of this paper is to provide decision support for the adjustment and optimization of crop planting structure in Jingxian County. The object-oriented information extraction method is used to extract corn and cotton from Jingxian County of Hengshui City in Hebei Province, based on multi-period GF-1 16-meter images. The best time of data extraction was screened by analyzing the spectral characteristics of corn and cotton at different growth stages based on multi-period GF-116-meter images, phenological data, and field survey data. The results showed that the total classification accuracy of corn and cotton was up to 95.7 %, the producer accuracy was 96 % and 94 % respectively, and the user precision was 95.05 % and 95.9 % respectively, which satisfied the demand of crop monitoring application. Therefore, combined with multi-period high-resolution images and object-oriented classification can be a good extraction of large-scale distribution of crop information for crop monitoring to provide convenient and effective technical means.
Highlights
Remote sensing technology has been widely used in various fields of national economy and social development due to its macroscopic, comprehensive, dynamic and rapid characteristics
The spatial distribution results are shown in Fig.7.It can be concluded that corn in Jingxian County, Hengshui City, Hebei Province has a wide distribution and covers almost the entire county
Based on the multi-period GF-1 imagery, the object-oriented information extraction method was used to extract the spatial distribution of corn and cotton in Jingxian County, Hengshui City, Hebei Province in 2016
Summary
Remote sensing technology has been widely used in various fields of national economy and social development due to its macroscopic, comprehensive, dynamic and rapid characteristics. (Zhang et al, 2012)Liu Kebao et al utilized the multi-period RapidEye images to extract the spatial distribution of crop planting structure in Zhaodong City in 2011 based on the maximum likelihood supervised classification method(Liu et al, 2014).HaoWeiping et al selected 14 MODIS NDVI images of major crops in 2007 in Northeast China and 2005 Landsat ETM + 30m images and a large amount of ground survey data to extract the spatial information of the main crops based on unsupervised classification algorithm(Hao et al, 2011).most of the researches use the foreign remote sensing images to extract crops based on the pixel classification method and only use the spectral information of the images, making it difficult to distinguish between the categories of "same object different spectrums" and "same spectrum with different objects". Object-oriented information extraction method was used to extract corn and cotton, the main autumn crop in Jingxian County, Hengshui City, Hebei Province. The data used in this study mainly include remote sensing image data and other auxiliary data, as shown in Table 1 and Table 2 respectively
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