首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Monitoring the main crops status of Beijing area with multi-source remotely sensed information
【24h】

Monitoring the main crops status of Beijing area with multi-source remotely sensed information

机译:用多源遥感信息监测北京地区的主要作物状况

获取原文

摘要

With the urbanizing development of Beijing, the planting area and spatial distribution structure of Beijing suburb crops are changing constantly. Because the TM (Thematic Mapper) images of Beijing area during 2003 summer suffered from weather conditions, such as cloud or haze, it is difficult to obtain the satisfying classification with only one kind of remote sensing data. To get the first data of the exact area and spatial distribution of Beijing crops, a perfect classification strategy is designed in This work. The images of 2003's TM/ETM+ (Enhanced Thematic Mapper Plus) in comparative better weather conditions, MODIS (Moderate Resolution Imaging Spectroradiometer), NDVI (Normalized Difference Vegetation Index) standard products in 2003 and DEM (Digital Elevation Model) at 30 m spatial resolution of Beijing area are adopted. The classification system for main crops in Beijing is constructed which includes winter wheat, maize, soybean, clover, garden, orchard, etc. Based on the phenological features of the main crops, the different crops extracting plan is constructed respectively. The orchard and vegetable in greenhouse planting information are from the supervised classification with maximum likelihood methods. By means of the decision tree classification function of the ENVI (The Environment for Visualizing Images) software, the winter wheat and clover planting information are extracted by the logic operation algorithm among the 4 NDVI data from the TM/ETM+ images adopted in This work while the spring maize and soybean are extracted with the logic algorithm with MODIS NDVI. The results indicated that this classification strategy can not only improve the classification precision, but also decrease the post classification work.
机译:随着北京的城市化发展,北京郊区农作物的种植区和空间分布结构不断变化。由于2003年北京地区的TM(主题映射器)图像遭受了云或雾度等天气条件,因此难以获得一种遥感数据的令人满意的分类。为了获得北京作物的确切区域和空间分布的第一个数据,在这项工作中设计了完美的分类策略。 2003年的TM / ETM +(增强专题MAPPER)的图像在比较更好的天气条件下,MODIS(适度分辨率成像光谱仪),NDVI(归一化差异植被指数)标准产品2003年和DEM(数字高程模型)在30米的空间分辨率下北京地区采用。北京主要农作物的分类系统被建造,包括冬小麦,玉米,大豆,三叶草,花园,果园等。基于主要作物的候选特征,分别构建了不同的作物提取计划。果园和蔬菜在温室种植信息来自监督分类,最大可能性方法。借助于ENVI的决策树分类功能(用于可视化图像的环境)软件,冬小麦和三叶草种植信息由来自TM / ETM +图像中的4个NDVI数据中的逻辑操作算法提取春天玉米和大豆用MODIS NDVI用逻辑算法提取。结果表明,该分类策略不仅可以提高分类精度,而且还减少了职位分类工作。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号