Arid
DOI10.1117/12.2188158
Combining geostatistical models and remotely sensed data to improve vegetation classification in horqin sandy land
Liao Chujiang
通讯作者Liao Chujiang
会议名称International Conference on Optical Instruments and Technology - Optical Sensors and Applications
会议日期MAY 17-19, 2015
会议地点Beijing, PEOPLES R CHINA
英文摘要

On different degrees of desertification land, there exists different vegetation communities, and spatial structure differences are obvious among different vegetation communities. This study implemented variogram calculation using typical sample selected from the image, adopting a common global optimization method to fit them into the spherical model. The results showed that the difference is obvious among different vegetation communities for the sill and range, such as, the sill and range are smaller for sample variogram of Artemisia halodendron and Salix flavida community than that of Artemisia halodendron and Caragana microphylla community, and the range for sample variogram of Agriophyllum arenarium community is bigger than that of Artemisia halodendron and Salix flavida community, but smaller than that of Artemisia halodendron and Caragana microphylla community. Incorporating the difference of the spatial structure characterization into the vegetation classification can improve sample separation, thereby increasing the overall classification accuracy.


英文关键词geostatistical model image area variogram spherical model
来源出版物2015 INTERNATIONAL CONFERENCE ON OPTICAL INSTRUMENTS AND TECHNOLOGY: OPTICAL SENSORS AND APPLICATIONS
ISSN0277-786X
出版年2015
卷号9620
EISBN978-1-62841-801-9
出版者SPIE-INT SOC OPTICAL ENGINEERING
类型Proceedings Paper
语种英语
国家Peoples R China
收录类别CPCI-S
WOS记录号WOS:000360391800022
WOS关键词VARIOGRAMS ; DESERT
WOS类目Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Optics ; Physics, Applied
WOS研究方向Engineering ; Instruments & Instrumentation ; Optics ; Physics
资源类型会议论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/304532
作者单位(1)China Acad Space Technol, Qian Xuesen Lab Space Technol, Beijing 100094, Peoples R China
推荐引用方式
GB/T 7714
Liao Chujiang. Combining geostatistical models and remotely sensed data to improve vegetation classification in horqin sandy land[C]:SPIE-INT SOC OPTICAL ENGINEERING,2015.
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