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| 基于历史时序植被指数库的多源数据作物面积自动提取方法 | |
| 引用本文： | 郝鹏宇,牛 铮,王 力,王秀兰,王长耀.基于历史时序植被指数库的多源数据作物面积自动提取方法[J].农业工程学报,2012,28(23):123-131. |
| 作者姓名： | 郝鹏宇 牛 铮 王 力 王秀兰 王长耀 |
| 作者单位： | 1. 中国科学院遥感与数字地球研究所,遥感科学国家重点实验室,北京 100101 2. 中国科学院大学资源与环境学院,北京 100049 3. 北京林业大学 林学院,北京 100083 |
| 基金项目： | 国家重点基础研究发展规划项目（2010CB950603）；公益性行业（气象）科研专项经费（GYHY201006042）；国家自然科学基金（40971202）；国家自然科学基金（41001209）；欧盟项目CEOP-AEGIS（FP7-ENV-2007-1 Grant nr. 212921） |
| 摘 要： | 针对目前作物提取工作中难以综合应用多源遥感数据进行自动分类的现状,该文以新疆博乐市为试验区,使用多年MODIS数据建立各类作物历史参考时序植被指数曲线库。对TM和环境星数据共同构成的当年时序数据通过植被指数转换、曲线相似性比较,并结合区分不同作物的关键时相,在长时期种植制度变化不大的区域,自动提取作物种植面积。结果表明:该方法使用多源(环境星+TM)中高分辨率遥感数据构建的时序植被指数提取作物的总体精度可达到90%以上;与传统的监督分类方法相比,省去了人工采集训练区的步骤,实现了作物种植面积的自动提取。 |
| 关 键 词： | 时间序列 植被 遥感 MODIS TM 环境星 |
| 收稿时间： | 2012/6/27 0:00:00 |
| 修稿时间： | 2012/11/23 0:00:00 |
Multi-source automatic crop pattern mapping based on historical vegetation index profiles |
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| Hao Pengyu,Niu Zheng,Wang Li,Wang Xiulan and Wang Changyao.Multi-source automatic crop pattern mapping based on historical vegetation index profiles[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(23):123-131. | |
| Authors: | Hao Pengyu Niu Zheng Wang Li Wang Xiulan and Wang Changyao |
| Institution: | 1(1.The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;2.College of Resoures and Environment,University of Chinese Academy of Science,Beijing 100049,China;3.The Academy of Forestry,Beijing Forestry University,Beijing 100083,China) |
| Abstract: | Multi-temporal vegetation index (VI) reflects phonological calendar feature, which is one of the most distinguishing features between different crops. It has been widely used in detecting the dynamics of vegetation characteristics over time and land cover classification. However, medium to high spatial resolution time series VI data (spatial resolution > 30m) has not been utilized because single sensor cannot provide the images of all time phrases. In addition, the comprehensive utilization of multi-source images is limited as the difference between sensors. The objective of this research was to evaluate the applicability of crop mapping using multi-source medium resolution time series VI data (TM + HJ-1) based on reference historical time series VI profiles in Bole County. Since all of MODIS, TM and HJ-1 have red band and NIR band, three VIs: NDVI, EVI2 and WRDVI calculated from these two bands were employed in this paper. Then, MODIS and field-plot data were utilized to build annual time series VI profiles of different crops between 2006 and 2010, and the historical references profiles by iteratively calculating the weighted average of annual VI profiles were got. Therefore, to reduce VI difference among TM, HJ-1 and MODIS, TM/HJ-1 VI to MODIS VI using linear regression method, and got medium spatial resolution time series VI data composed of TM and HJ-1 in 2011. To extract crop patterns were translated, historical reference profiles were interpolated by time phrases of TM/HJ-1, Euclidean distance between time series VI data and reference profiles was computed and threshold of crop mapping using field samples between 2006 and 2010 was calculated. Nevertheless, as reference time series VI profiles of cotton and corn were similar (correlation coefficient > 0.99), the sixth time phrase (DOY=253) where cotton and corn reference profiles had the largest difference was utilized to distinguish cotton and corn. Finally, the crop mapping accuracy was compared with supervised classification, the result showed that crop mapping accuracy based on historical profiles using NDVI, EVI2 and WRDVI were 90.53%, 91.35% and 90.83%, and Kappa coefficient were 0.78, 0.81 and 0.80, which were better than that of supervised classification. It was concluded that crop mapping based on historical reference time series VI profiles omitted choosing training sites procedure, and interfaced the automatic crop mapping. What is more, this paper also offered a new method to comprehensive utilization of multi-source remote sensing data. |
| Keywords: | time series analysis vegetation remote sensing MODIS TM HJ-1 |
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