CROP DISCRIMINATION IN SHANDONG PROVINCE BASED ON PHENOLOGY ANALYSIS OF MULTI-YEAR TIME SERIES
文献类型: 外文期刊
作者: Xu, Qingyun 1 ; Yang, Guijun 1 ; Long, Huiling 1 ; Wang, Chongchang 2 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Liaoning Tech Univ, Fuxing 123000, Peoples R China
3.Liaoning Tech Univ, Inst Surveying & Mapping, Fuxing 123000, Peoples R China
4.Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China
关键词: Crop;Phenology;Identification;SPOT_VGT NDVI Time Series;Multi-year;Shandong Province
期刊名称:INTELLIGENT AUTOMATION AND SOFT COMPUTING ( 影响因子:1.647; 五年影响因子:1.469 )
ISSN: 1079-8587
年卷期: 2013 年 19 卷 4 期
页码:
收录情况: SCI
摘要: Crop type identification plays an important role in extracting crop acreage, assessing crop growth and arable land productivity. In this study, the main crops (winter wheat, summer maize and cotton) of Shandong Province as research objects, and the SPOT_VGT normalized difference vegetation index (NDVI) remote sensing datasets from 1999 to 2011 covering Shandong Province were acquired. The NDVI characteristic curves of typical features were extracted by combining the SPOT_VGT NDVI time series datasets, the HJ-1B image and the phenological information. Moreover, the reasonable dynamic thresholds were settled, the non-cultivated land areas were removed and the crop patterns and the crop types were identified based on the annual NDVI variation and the phenological information of the typical features. The accuracy assessment was performed through the spatial contrast and quantitative description. The overall accuracy is 77.10% in the spatial accuracy assessment compared with standard land cover classification map, and the overall relative errors of winter wheat, summer maize and cotton are 25.52%, 25.97% and 7.11% in the quantitative accuracy assessment compared with the statistical datasets. The results of research show that it is feasible to identify the crop planting patterns and crop types using the proposed classification method by combining the SPOT_VGT NDVI time series datasets with the phenological information.
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