干旱区生态环境知识资源中心(Arid): 高原草被退化程度的遥感定量监测以甘肃省玛曲县为例

Arid

	高原草被退化程度的遥感定量监测以甘肃省玛曲县为例
其他题名	Quantitatively monitoring undergoing degradation of plateau grassland by remote sensing data: a case study in Maqu County,Gansu Province,China
	周坚华 1; 魏怀东 2; 陈芳 2; 郭晓华 1
来源期刊	生态学报
ISSN	1000-0933
出版年	2012
卷号	32 期号:6 页码:1663-1676
中文摘要	以加权草被盖度作为草被退化的一种标识值,来标记草场沙化和毒杂草侵蚀的程度,并以机器辨识方法从TM/ETM数据中定量提取加权草被盖度现状及变迁信息。变迁研究的时段从1994年至2008年。为了使图像辨识特征量对高寒草被盖度敏感,提出了植被指数分级密度、植被相对饱和度分级密度等8个新的数学描述符,逐一进行了与加权草被盖度的相关性分析或分割实验;并通过它们的组合训练学习机和实现了对不同盖度草被的划分。通过野外采样数据检核,这种分类的准确率接近或达到80%。在草被盖度正确分类的基础上,通过调整减少不同时相图像照度差异的影响,进一步实现了加权草被盖度变迁信息的自动化提取。
英文摘要	Degradation of grassland is a complex and gradual process.In order to make it more capable for monitoring the changes in undergoing-degradation and not-being-completely-deserted grassland with remote sensing data,one of available ways is to identify spectral characteristics in response to the differences in grassland coverage by machine discerning.However,it is challenging since the differences are subtle in a TM / ETM image or other images with similar spatial and spectral resolution.This paper discusses the methods for a learning machine to discern the subtle differences from TM / ETM spectral data.The key technologies can be summarized as: replacing the grass cover(D) with the so called "weighted grass cover(Dw)" to solve the difficulties in the fitting between Normalized Difference Vegetation Index(NDVI) from TM/ETM spectral data and field measured D,designing some mathematical descriptors which are independent of image types and acquisition environment and discerning the indicative differences in response to Dw in a multi-descriptor space so that the error rate in the discerning will be surely reduced. Dw is a weighted sum of the D,the grass height(H) and the proportion of the poisonous in all grass(Dup).With the use of Dw,The regression equations between Dw,calculated by D,H and Dup measured in the field,and the normalized vegetation indexes,such as NDVI,SAVI,etc.,calculated with remote sensing spectral data will fit much better than that with the replacement of D for Dw.In order to improve the sensitivity to the differences in grass coverage by the learning machine,eight new mathematical descriptors,as the supplements of those vegetation indexes,have been proposed and then analyzed one by one in their correlation to Dw or tested in their validity for Dw segmentation.It is verified that some of the combinations of these descriptors are helpful for the learning machine to work out the laws of identifying pixels or cells belonging to different Dw.Otherwise,by comparing the classification accuracy in the use of two kinds of learning machines,i.e.DT and SVM,the types of learning machines are not so crucially to be able to decide discrimination accuracy as mostly desirable and,on the contrary,the construction of search space may be a more decisive factor. Our study shows that in the multi-descriptor spaces,the grasslands belonging to different Dw can reliably be classified with TM / ETM data.Being inspected by field sampling data,it is verified that the classification accuracy is nearer to or better than 80%.Based on the results in Dw classification,then the Dw changes can also be reliably deduced after adjusting the differences of luminance level between two images in different acquisition years.
中文关键词	加权草被盖度 ; 数学描述符 ; 机器辨识 ; TM/ETM图像
英文关键词	powered grass cover mathematical descriptor machine discerning TM/ETM
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:4500145
来源机构	甘肃省治沙研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/228440
作者单位	1.华东师范大学, 地理信息科学教育部重点实验室, 上海 200062, 中国; 2.甘肃省治沙研究所, 武威, 甘肃 733000, 中国
推荐引用方式 GB/T 7714	周坚华,魏怀东,陈芳,等. 高原草被退化程度的遥感定量监测以甘肃省玛曲县为例[J]. 甘肃省治沙研究所,2012,32(6):1663-1676.
APA	周坚华,魏怀东,陈芳,&郭晓华.(2012).高原草被退化程度的遥感定量监测以甘肃省玛曲县为例.生态学报,32(6),1663-1676.
MLA	周坚华,et al."高原草被退化程度的遥感定量监测以甘肃省玛曲县为例".生态学报 32.6(2012):1663-1676.