| 基于改进N-FINDR算法的华北平原冬小麦面积提取 |
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| 引用本文： | 郝虑远,孙 睿,谢东辉,唐 侥,汪 艳.基于改进N-FINDR算法的华北平原冬小麦面积提取[J].农业工程学报,2013,29(15):153-161. |
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| 作者姓名： | 郝虑远 孙 睿 谢东辉 唐 侥 汪 艳 |
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| 作者单位： | 1. 遥感科学国家重点实验室，北京 1008752. 北京师范大学地理学与遥感科学学院，北京 1008753. 环境遥感与数字城市北京市重点实验室，北京 100875;1. 遥感科学国家重点实验室，北京 1008752. 北京师范大学地理学与遥感科学学院，北京 1008753. 环境遥感与数字城市北京市重点实验室，北京 100875;1. 遥感科学国家重点实验室，北京 1008752. 北京师范大学地理学与遥感科学学院，北京 1008753. 环境遥感与数字城市北京市重点实验室，北京 100875;1. 遥感科学国家重点实验室，北京 1008752. 北京师范大学地理学与遥感科学学院，北京 1008753. 环境遥感与数字城市北京市重点实验室，北京 100875;1. 遥感科学国家重点实验室，北京 1008752. 北京师范大学地理学与遥感科学学院，北京 1008753. 环境遥感与数字城市北京市重点实验室，北京 100875 |
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| 基金项目： | 国家自然科学基金（40971221）；公益性行业（气象）科研专项经费项目（200906022）；欧盟FP7-ENV-2007-1 Grant NO（212921） |
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| 摘 要： | 为了解决MODIS数据中普遍存在的混合像元问题，该文利用2008年和2009年多时相的MODIS13Q1影像，以经过优化的N-FINDR算法进行线性混合像元分解提取冬小麦种植面积，各省的误差均控制在正负4%左右。利用同期多时相的HJ-1星分类数据作为参考值，在试验区域选择14个均匀分布的样区验证混合像元分解结果。结果显示6个样区的相对误差在10%以内，其余8个样区的误差基本在15%左右。该研究可为冬小麦种植面积的监测提供参考。
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| 关 键 词： | 遥感，监测，提取，面积，线性混合像元分解，冬小麦，MODIS，N-FINDR |
| 收稿时间： | 2013/1/15 0:00:00 |
| 修稿时间： | 7/8/2013 12:00:00 AM |
Planting area extraction of winter wheat in North China Plain based on optimized N-FINDR algorithm |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Beijing 100875, China2. School of Geography, Beijing Normal University, Beijing 100875, China3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China;1. State Key Laboratory of Remote Sensing Science, Beijing 100875, China2. School of Geography, Beijing Normal University, Beijing 100875, China3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China;1. State Key Laboratory of Remote Sensing Science, Beijing 100875, China2. School of Geography, Beijing Normal University, Beijing 100875, China3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China;1. State Key Laboratory of Remote Sensing Science, Beijing 100875, China2. School of Geography, Beijing Normal University, Beijing 100875, China3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China;1. State Key Laboratory of Remote Sensing Science, Beijing 100875, China2. School of Geography, Beijing Normal University, Beijing 100875, China3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China |
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| Abstract: | Abstract: Winter wheat is one of the main food crops in the north of China. It is significant to monitor winter wheat planting areas for China's grain policy and economic planning. The MODIS products are outstanding with the characteristics of large area coverage, frequent repeat, and free access to download. It offers a valuable application on long-term and large-area detection of winter wheat. Because of the coarse spatial resolution of MODIS products, the mixed pixels become the common problem existing in MODIS data. Therefore, it is necessary to solve the problem of mixed pixels in crop area extraction with MODIS data,In this study, we chose the Huanghuaihai Plain (including Hebei province, Shandong province, Henan province, Beijing, and Tianjin) as the study area, and used multi-temporal MODIS data in 2008 and 2009 to extract the winter wheat area with an optimized N-FINDR algorithm and linear unmixing method. In a traditional N-FINDR algorithm, all pixels in the image would be traversed to find the pixel group that can form a simplex with the maximum area. The optimized N-FINDR algorithm we used simplifies the procedure by finding the points set that can form a triangle with the maximum area in a two-dimensional plane composed by any two bands first, then the vertex of the triangle are taken as candidate endmembers, and final endmembers are obtained by traversing all the candidate endmembers. In order to find points set in a two-dimensional plane, we used the convex hull property of a polygon with rotating calipers. This optimized algorithm can improve time complexity from O(n3) to O(n2).Comparing this with national statistical data in 2009, the relative error of the extracted winter wheat planting area was less than 4% for each province. The results showed that the method we used was applicable for winter wheat area extraction on a large scale. In order to further validate the results, we selected 14 sample areas, and multi-temporal HJ-1 data at same period were taken to produce the winter wheat planting map as a reference for each sample area. The validation results showed that the spatial distribution of the unmixing results agreed with the classification maps of HJ-1. The relative error of winter wheat planting area was less than 5% for 5 sample areas, and larger than 15% for 4 sample areas. The error was relatively larger for the sample areas located in the urban area and the mountain area. The error was mainly caused by the error of endmember extraction, the internal difference of the winter wheat phenology and spectra for large area, the fragmentation of crop land, and the complexity of the land surface in the mountain area. |
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| Keywords: | remote sensing monitoring area winter wheat linear pixel unmixing MODIS N-FINDR |
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