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| 陕北黄土丘陵地区坡耕地遥感分类方法研究 | |
| 引用本文： | 刘咏梅,杨勤科,汤国安.陕北黄土丘陵地区坡耕地遥感分类方法研究[J].水土保持通报,2004,24(4):51-54. |
| 作者姓名： | 刘咏梅 杨勤科 汤国安 |
| 作者单位： | 1. 西北大学,城市与资源学系,陕西,西安,710069;中国科学院水利部,水土保持研究所,陕西,杨凌,712100 2. 中国科学院水利部,水土保持研究所,陕西,杨凌,712100 3. 西北大学,城市与资源学系,陕西,西安,710069 |
| 基金项目： | 国家自然科学基金项目(40271089)，测绘遥感信息工程国家重点实验室高访基金项目 |
| 摘 要： | 陡坡开垦使坡耕地面积扩大是造成黄土高原水土流失严重、生态恶化的根本原因。应用遥感技术,及时准确地掌握坡耕地的分布特征及面积数据,对合理实施退耕还林还草工程意义重大。以陕北黄土丘陵沟壑区为实验样区,以TM图像为主要信息源,采用非监督分类与监督分类相结合的混合分类方法提取坡耕地信息。通过改进采样方法,在非监督分类生成的初始训练样本的基础上,进行删除、增补、合并等样本调整,使训练样本的选取精度大大提高,明显提高了分类精度。研究证明,在黄土丘陵沟壑区,采用混合分类方法进行坡耕地分类,较好地解决了大量存在的坡耕地同物异谱的现象,是进行坡耕地调查与监测的理想方法。 |
| 关 键 词： | 黄土丘陵区 遥感 坡耕地 混合分类 |
| 文章编号： | 1000-288X(2004)04-0051-04 |
| 收稿时间： | 2003/12/6 0:00:00 |
| 修稿时间： | 2003年12月6日 |
Remote Sensing Classification Method of Slope Field in Loess Hilly and Gully Area of Northern Shaanxi Province |
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| LIU Yong-mei,YANG Qin-ke and TANG Guo-an.Remote Sensing Classification Method of Slope Field in Loess Hilly and Gully Area of Northern Shaanxi Province[J].Bulletin of Soil and Water Conservation,2004,24(4):51-54. | |
| Authors: | LIU Yong-mei YANG Qin-ke and TANG Guo-an |
| Institution: | LIU Yong-mei~ |
| Abstract: | Reclaiming steep slope land to extend the area of slope fields plays a very important role in soil and water loss and eco-environment deterioration in the Loess Plateau. In order to convert farmland into forest on slope land effectively, obtaining up-to-date and reliable information on the spatial distribution and regional extent of slope fields by remote sensing is of critical importance. Using Landsat TM5 data of the loess hilly and gully area of northern Shaanxi Province, integrated supervised and unsupervised classification were applied to extract slope field and other categories. By improving the signature selection accuracy, this method improves classification accuracy greatly. The result shows that integrated classification is suitable to slope field investigation in the loess hill and gully area. |
| Keywords: | loess hill and gully area remote sensing slope field integrated classification |
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