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| 基于ETM+图像的混合像元线性分解方法(LSMM)在澳门植被信息提取中的 应用及效果评价 | |
| 引用本文： | 邹蒲,王云鹏,王志石,樊风雷.基于ETM+图像的混合像元线性分解方法(LSMM)在澳门植被信息提取中的 应用及效果评价[J].华南师范大学学报(自然科学版),2007,1(2):131-136. |
| 作者姓名： | 邹蒲 王云鹏 王志石 樊风雷 |
| 作者单位： | 1. 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,广东广州510640;中国科学院研究生院,北京,100039 2. 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,广东广州510640 3. 澳门大学科技学院科技研究中心,澳门邮政信箱3001号,澳门 4. 华南师范大学地理科学学院,广东广州,510631 |
| 基金项目： | 广东省科技攻关资助项目(2005B30801007,2004A30401001)，广东省自然科学基金资助项目(04002143) |
| 摘 要： | 本文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． |
| 关 键 词： | 像元分解 线性混合模型 植被覆盖 澳门 |
| 文章编号： | 1000-5463（2007）02-0131-06 |
| 修稿时间： | 2006-10-26 |
ACCESSING THE LINEAR SPECTRAL UN-MIXING APPROACH FOR EXTRACTING VEGETATION INFORMATION USING LANDSAT ETM +DATA IN MACAO |
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| ZOU Pu,WANG Yun-peng,WANG Zhi-shi,FAN Feng-lei.ACCESSING THE LINEAR SPECTRAL UN-MIXING APPROACH FOR EXTRACTING VEGETATION INFORMATION USING LANDSAT ETM +DATA IN MACAO[J].Journal of South China Normal University(Natural Science Edition),2007,1(2):131-136. | |
| Authors: | ZOU Pu WANG Yun-peng WANG Zhi-shi FAN Feng-lei |
| Institution: | 1. State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, P.O. Box 1131, Guangzhou 510640, China; 2. Center of Science and Technology Research, University of Macao, P. O. Box 3001, Macao, China; 3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China; 4. School of Geography, South China Normal University, Guangzhou 510631, China |
| Abstract: | 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 km2,8.26 km2 and 8.68 km2 respectively.The areas by LSMM are closer to the actual vegetable areas (5.79 km2).The results 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 remote sensing methods.Therefore,LSMM provides a novel way for monitoring vegetation more accurately and efficiently. |
| Keywords: | pixel unmixing linear spectral unmixing model vegetation coverage Macao |
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