• 论文 •

### 东亚飞蝗生境的遥感分类——以河北省黄骅地区为例

1. 1. 南京师范大学地理科学学院,南京210097;
2. 中国科学院南京土壤研究所,南京210097
• 收稿日期:2005-12-08 修回日期:2006-05-25 出版日期:2006-08-15 发布日期:2006-08-15
• 作者简介:李开丽(1977-),女,山东临沂人,博士研究生。主要从事遥感与GIS应用研究。 E-mail:lklcelery@163.com
• 基金资助:

国家自然科学基金资助项目(遥感与GIS支持的东亚飞蝗发生机理与预测模型研究,40371081)

### Breeding area classification for oriental migratory locust assisted by remote sensing:a case study of the Huanghua Region in Hebei Province

LI Kai-li1,2, NI Shao-xiang1

1. 1. College of Geographical Science,Nanjing Normal University,Nanjing 210097,China;
2. Insititute of Soil Science,CAS,Nanjing 210008,China
• Received:2005-12-08 Revised:2006-05-25 Online:2006-08-15 Published:2006-08-15

Abstract:

The classification of breeding area for oriental migratory locust（Locust migratoria manilensis Meyen） is one of the most important tasks in terms of the monitoring and controlling of the damages induced by the locusts.In this study,the Huanghua region along the Bohai Bay in Hebei Province was selected as the study area and the locust breeding areas were classified based on the Landsat-5 TM images dated on August 14,2003（TMⅠ） and May 28,2004（TMⅡ） respectively.Three different schemes of image band combination and two kinds of classifiers were used in the breeding area classification,i.e.the maximum likelihood classifier and the knowledge-based layered classifier.In more detail,they are 1) the combination of bands 3,4 and 5 of TMⅠplus bands 3,4 and 5 of TMⅡwith the maximum likelihood classifier;2)the combination of bands 3,4 and 5 of TMⅠplus bands 3,4 and 5 of TMⅡand the homogeneity index derived from the image of NDVITMⅠas a band which contains the spatial texture information of the images,with also the maximum likelihood classifier;and 3)the combination of bands 3,4 and 5 of TMⅠplus bands 3,4 and 5 of TMⅡand the NDVITMⅠas a band,with the knowledge-based layered classifier.The results show that,firstly,there is no obvious difference among these different combination schemes in terms of the overall accuracy of the locust breeding area.Relatively speaking,the overall accuracy of the second combination scheme（89.319） is somewhat higher than those of the other two combination schemes,which indicates that it is beneficial to accuracy improvement of locust breeding area classification if adding the spatial texture information of the images into the classification.Secondly,although the overall accuracy of locust breeding area classification with the third combination scheme is somewhat lower than those of the other two combination schemes,its variation range of locust breeding area classification accuracy among all individual locust breeding area types is relatively small,which means that the knowledge-based layered classifier still has a certain of advantages in the locust breeding area classification.