干旱区生态环境知识资源中心(Arid): 多尺度遥感数据协同的干旱地区植被覆盖度提取

Arid

	多尺度遥感数据协同的干旱地区植被覆盖度提取
其他题名	Extraction of vegetation coverage in arid regions using multi-scale remote sensing data synergistically
	李秀瑞; 孙林; 朱金山; 韦晶
来源期刊	生态学杂志
ISSN	1000-4890
出版年	2016
卷号	35 期号:5 页码:1394-1402
中文摘要	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MO-DIS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(R~2= 0.75),均方根误差较低(RMSE= 0.10)o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。
英文摘要	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.
中文关键词	干旱地区 ; 多尺度遥感数据 ; 光谱转换 ; 植被覆盖度 ; 像元二分模型
英文关键词	arid region multi-scale remote sensing data spectral transformation vegetation coverage dimidiate pixel model
类型	Article
语种	中文
国家	中国
收录类别	CSCD
WOS类目	Life Sciences & Biomedicine - Other Topics
WOS研究方向	Life Sciences & Biomedicine - Other Topics
CSCD记录号	CSCD:5699048
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234862
作者单位	李秀瑞, 山东科技大学测绘科学与工程学院, 青岛, 山东 266590, 中国.; 孙林, 山东科技大学测绘科学与工程学院, 青岛, 山东 266590, 中国.; 朱金山, 山东科技大学测绘科学与工程学院, 青岛, 山东 266590, 中国.; 韦晶, 山东科技大学测绘科学与工程学院, 青岛, 山东 266590, 中国.
推荐引用方式 GB/T 7714	李秀瑞,孙林,朱金山,等. 多尺度遥感数据协同的干旱地区植被覆盖度提取[J],2016,35(5):1394-1402.
APA	李秀瑞,孙林,朱金山,&韦晶.(2016).多尺度遥感数据协同的干旱地区植被覆盖度提取.生态学杂志,35(5),1394-1402.
MLA	李秀瑞,et al."多尺度遥感数据协同的干旱地区植被覆盖度提取".生态学杂志 35.5(2016):1394-1402.