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• 方法与技术 • 上一篇    

多尺度遥感数据协同的干旱地区植被覆盖度提取

李秀瑞,孙林*,朱金山,韦晶   

  1. (山东科技大学测绘科学与工程学院, 山东青岛 266590)
  • 出版日期:2016-05-10 发布日期:2016-05-10

Extraction of vegetation coverage in arid regions using multiscale remote sensing data synergistically.

LI Xiu-rui, SUN Lin*, ZHU Jin-shan, WEI Jing   

  1. (Geomatics College, Shandong University of Science and Technology, Qingdao 266590, Shandong, China).
  • Online:2016-05-10 Published:2016-05-10

摘要: 纯植被像元获取是植被覆盖信息遥感反演的必要环节。干旱地区植被分布零散稀疏,使用中、低分辨率遥感数据提取植被覆盖度时,难以获取纯植被像元,致使植被覆盖度提取精度较低。针对上述问题,本文提出一种基于多尺度遥感数据协同的干旱区植被覆盖度反演方法。该方法利用空间分辨率较高的Landsat-8 OLI数据确定纯植被像元,考虑到不同传感器之间的光谱差异,使用实测地物光谱数据进行光谱转换,代替中等分辨率MODIS数据的纯植被像元,应用于像元二分模型,选择典型的干旱区新疆阜康市为研究区,进行植被覆盖度反演实验,最后使用无人机航拍影像对反演结果进行精度验证。结果表明,植被覆盖度反演结果精度较高,与实测值间存在较高的相关性(R2=0.75),均方根误差较低(RMSE=0.10)。该方法能够有效提高干旱区植被覆盖度反演精度,可为利用中低分辨率数据研究干旱地区生态环境变化提供一种新思路。

关键词: 间套作, 铁, 锌, 根际相互作用, 生物强化

Abstract: The acquisition of pure vegetation pixel is a necessary step in the extraction of vegetation coverage. However, the vegetation distributes sparsely in arid regions and it is difficult to extract the pure vegetation pixel using low spatial resolution remote sensing data. Consequently, the vegetation coverage extraction accuracy is low. This paper proposed a new method for vegetation coverage retrieval in arid regions using multiscale remote sensing data synergistically. This method used Landsat-8 OLI data, which has a spatial resolution of 30 m, to determine the pure vegetation pixel. In order to eliminate the difference of NDVI from both MODIS and OLI data, induced by the spectral difference of these two sensors, a spectrum transformation procedure was conducted by using ground measured vegetation and soil spectra. A typical arid area located at Fukang City, Xinjiang was selected as the study area to conduct the inversion experiment of vegetation coverage based on this method. In order to verify the accuracy of the new method, validation using the very high resolution aerial image was conducted. The validation showed that the coefficient of determination between the retrieved value and measured value was 0.75, and the RMSE was 0.10. Thus, the method can effectively improve the accuracy of vegetation coverage retrieval and provide a new way to study the ecological environment in arid areas by using low resolution data.

Key words: intercropping, iron, zinc, rhizosphere interactions, biofortification.