作　　者： ; ; ; ;

机构地区： 中国科学院广州地球化学研究所有机地球化学国家重点实验室

出　　处： 《华南师范大学学报（自然科学版）》 2007年第2期131-136,共6页

摘　　要： 利用混合像元线性分解方法(LSMM),对澳门ETM+图像(2003/1/10)进行像元分解提取植被信息.同时利用同一图像的归一化植被指数(NDVI)、缨帽变换的“绿度”分量(KT2)对提取的植被信息进行对比分析,发现用LSMM方法提取的植被信息与NDVI的相关系数达到0.93与KT2的相关系数达到了0.74.同时发现用LSMM方法提取的植被面积(4.19 km2)比NDVI阈值法、KT2阈值法提取的植被面积(分别为8.26 km2、8.68 km2)更接近真实植被面积(5.79 km2).结果表明混合像元线性分解方法能有效地提取植被信息,比以像元为单位的常规遥感提取方法精度更高,为快速、准确、高效的植被监测提供了新思路. The vegetation information of Macao was quantificationally extracted from Landsat ETM ＋ data of 2003 by using linear spectral un -mixing approach （LSMM）. At the same time, the Normalized Difference Vegetation Index （NDVI） image and ＂greenness＂ image （ KT2 ）, which also obtained based on the ETM ＋ image （2003） of Macao, were used as two important comparison indexes to evaluate the extracted vegetation information by LSMM. The result shows that the three images have high correlation. At the same time, the vegetation areas extracted by LSMM, NDVI and KT2 are 4.19 km^2, 8.26 km^2 and 8.68 km^2 respectively. The areas by LSMM are closer to the actual vegetable areas （5.79 km^2）. The resuits show that the linear spectral un -mixing approach is not only an efficient way to extract vegetation information but also a more accurate measure than routine pixel -based re- mote sensing methods. Therefore, LSMM provides a novel way for monitoring vegetation more accurately and efficiently.

关 键 词： 像元分解 线性混合模型 植被覆盖 澳门

领　　域： [经济管理]