摘要
以森林资源遥感分类为切入点,以黄土高原丘陵沟壑区陕西黄龙县境内的水土保持林作为对象,针对ETM+遥感影像在森林信息提取中存在的大量混合像元的问题,引入一种基于针叶林-阔叶林-灌草(C-B-G)模型的混合像元分解方法,通过这种方法,分别得到研究区针叶林、阔叶林、灌草的覆盖图像,并提取出了针阔混交林的分布情况。采用ER-DAS 9.1对分类结果进行精度评价,结果表明针阔混交林的分类精度相对于通用分类方法——监督分类的精度提高了20%,说明该方法可以改善植被信息提取的效果。
According to the remote sensing classification of forest resources, the water and soil conservation forests in Huanglong County of Shaanxi Province, a hilly and gully area of Loess Plateau, were chosen as research objects, and a mixed pixel decomposition method based on coniferous-broad leaved-grass (C-B-G) model was introduced for solving the mixed pixel problems in forest information extraction by ETM ^+ remote sensing images. Using this method, the cover images of coniferous forest, broad-leaved forest, bush, and grass were obtained respectively, and the distribution of coniferous and broad-leaved mixed forest was extracted. Erdas 9. 1 was used to evaluate the classification accuracy. Comparing with the supervised classification, a general clas- sification method, our method had 20% higher classification accuracy on the coniferous and broad-leaved mixed forest, suggesting its improved effect in vegetation information extraction.
出处
《生态学杂志》
CAS
CSCD
北大核心
2009年第9期1737-1742,共6页
Chinese Journal of Ecology
基金
中国科学院知识创新工程重大项目(KZCX1-YW-08-02)
国家自然科学基金项目资助(40701185)
关键词
ETM^+影像
混合像元
针阔混交林
分类精度
ETM ^+ image
mixed pixel
coniferous and broad-leaved forest
classification accuracy.