| 基于决策树和混合像元分解的江苏省冬小麦种植面积提取 |
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| 引用本文： | 王连喜,徐胜男,李琪,薛红喜,吴建生.基于决策树和混合像元分解的江苏省冬小麦种植面积提取[J].农业工程学报,2016,32(5):182-187. |
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| 作者姓名： | 王连喜 徐胜男 李琪 薛红喜 吴建生 |
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| 作者单位： | 1. 江苏省农业气象重点实验室，南京 210044; 南京信息工程大学应用气象学院，南京 210044;2. 中国气象局国家气象中心,北京,100081 |
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| 基金项目： | 国家科技支撑计划（2012BAH29B03） |
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| 摘 要： | 归一化植被指数(normalized difference vegetation index,NDVI)时间序列曲线能提供作物生长动态变化信息,将其应用于农作物种植面积提取具有一定优势。该文以江苏省为研究区域,采用2013年1月1日-2014年12月19日46景250 m空间分辨率的MODIS-NDVI时间序列数据、2014年4月23日的MOD09A1反射率影像及Landsat数据,开展冬小麦种植面积的遥感识别,首先利用MODIS数据建立作物的归一化植被指数时间序列曲线,再采用Savitzky-Golay滤波方法对NDVI时间序列数据进行重构,并基于农作物物候历、种植结构和种植模式等信息,提取研究区域典型地物物候生长期的关键值,在分析冬小麦、林地、水稻物候期(生长期开始时间、生长期结束时间、生长期幅度、生长期长度及生长期的NDVI最大值)变化趋势的基础上,综合比较分析不同地物平滑重构后的NDVI时间序列曲线特征,界定作物种类,确定训练规则,利用快速、高效的决策树方法,通过多阈值限定进行分类,初步提取冬小麦的空间分布范围;但是由于存在混合像元,阈值范围的设定会影响冬小麦种植面积的提取精度,针对此类问题,运用地表反射率影像数据提取冬小麦端元波谱曲线,结合线性光谱混合模型进行混合像元分解,进而根据冬小麦丰度比例精确提取冬小麦种植面积;最后利用统计数据和空间分辨率较高的Landsat TM 8影像数据对提取结果进行县域级验证。精度评价结果表明,研究区域的冬小麦种植面积提取精度达到90%,能够较准确地反映研究区域冬小麦的分布情况,表明运用中高分辨率遥感时间序列影像数据可以准确提取作物种植面积,为农作物种植面积信息提取提供参考。
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| 关 键 词： | 决策树 滤波 分类 归一化植被指数NDVI 混合像元 分解 冬小麦 |
| 收稿时间： | 2015/8/10 0:00:00 |
| 修稿时间： | 2016/1/13 0:00:00 |
Extraction of winter wheat planted area in Jiangsu province using decision tree and mixed-pixel methods |
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| Wang Lianxi,Xu Shengnan,Li Qi,Xue Hongxi and Wu Jiansheng.Extraction of winter wheat planted area in Jiangsu province using decision tree and mixed-pixel methods[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(5):182-187. |
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| Authors: | Wang Lianxi Xu Shengnan Li Qi Xue Hongxi and Wu Jiansheng |
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| Institution: | 1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing 210044, China; 2. School of Applied Meteorology, Nanjing University of Information Science& Technology, Nanjing 210044, China;,1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing 210044, China; 2. School of Applied Meteorology, Nanjing University of Information Science& Technology, Nanjing 210044, China;,1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing 210044, China; 2. School of Applied Meteorology, Nanjing University of Information Science& Technology, Nanjing 210044, China;,3. National Meteorological Center of China Meteorological Administration, Beijing 100081, China; and 1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing 210044, China; 2. School of Applied Meteorology, Nanjing University of Information Science& Technology, Nanjing 210044, China; |
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| Abstract: | Abstract: Remote sensing images with the medium spatial resolution can provide long-time series data of the same area, thus are suitable for remote sensing monitoring of major crops in a large scale. Based on the analysis of the time-series spectrum character curve, crop type identification and acreage extraction can be effectively achieved. The time series curve of normalized difference vegetation index (NDVI) can provide the information of crop growth dynamic change, thus is suitable for remote sensing extracting of major crops planting area. We used Jiangsu province as a research area and employed NDVI (normalized difference vegetation index) time-series data from 46 scenes of MODIS images with spatial resolution of 250 m collected from January 1st 2013 to December 31st 2014, reflectance image data of MODIS collected on April 23rd and image data of Landsat to carry out the remote sensing study for winter wheat planting area. First, a time-series curve of NDVI was built from the MODIS data, which was smoothed by an improved Savitzky-Golay filter. The improved Savitzky-Golay filter reserved the authenticity of data at both ends of the NDVI time series while further improving the smoothness of the curve. Based on the reconstruction of NDVI time series analysis, phenology, plant structure and the samples of ground survey, we extracted the key value of typical objects in their phenological growth period emphatically. At the same time, we analyzed the variation trend of winter wheat, woodland and rice (starting time, range, extent and maximum of NDVI) during the growth period. Through comparing and analyzing the characteristics of NDVI time series curves of different objects after smoothing, we defined the different crops, determined the training rules and build the construction of decision tree so that we can extract the distribution of winter wheat preliminarily. The decision tree classification method can be done quickly and efficiently using multi-threshold, however whose threshold is difficult to select accurately as a result of mixed pixel problem. The range of threshold would affect the accuracy of winter wheat planting area extraction. Therefore, in order to solve the problem of mixed pixels, we used surface reflectance image data to extract the endmember spectral curve of winter wheat. With linear spectral mixture model, according to the abundance ratio of winter wheat, we further extracted winter wheat planting area accurately. The distribution of winter wheat in Jiangsu Province was obtained. At last, the experimental results agreed well with the statistical data and high precision of Landsat8 TM image. According to the results of Landsat TM8 supervised classification, the area of winter wheat was extracted and the results were compared to the frontal results. The precision of decomposing the endmember purified was better than ever. Moreover, it accurately reflected the real situation of winter wheat distribution in Jiangsu province. It was concluded that the method in this paper was simple and easy. Accuracy evaluation results showed that the study area of winter wheat planting area extraction accuracy reached 90%, which can accurately reflect the distribution of winter wheat in the study area, The method also indicated that the application in high resolution remote sensing time-series image data with medium resolution image can be accurately extracted crop acreage, for crop planting area of information extraction to provide reference. |
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| Keywords: | decision trees filtration classification NDVI mixed pixel decomposition winter wheat |
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