Knowledge Resource Center for Ecological Environment in Arid Area
Phenology-tuned karst rocky desertification monitoring using satellite image time series | |
Xie, Xiangjian; Du, Peijun; Xue, Zhaohui; Alim, Samat; Luo, Jieqiong | |
通讯作者 | Du, Peijun |
会议名称 | 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA) |
会议日期 | JUN 11-14, 2014 |
会议地点 | Changsha, PEOPLES R CHINA |
英文摘要 | Remote sensing has been used in karst studies. The aim of this work is to investigate the use of MODIS time series to monitor and estimate karst rocky desertification (KRD). We propose a new phenology-tuned KRD model based BFAST (Breaks For Additive Seasonal and Trend) approach, Our strategy for KRD monitoring involving several steps: (a) phenology decomposition with BFAST; (b) seasonal parameters extraction; (c) phenology-tuned KRD modeling and ecological responses assessment. The strategy is applied in KRD monitoring and ecological responses assessment with the 4-year MODIS NDVI products that cover Qiubei and Yanshan counties, Yunnan province, and the results indicate that our approach is valid in ecological responses assessment of KRD. |
英文关键词 | Karst rocky desertification remote sensing time series BFAST |
来源出版物 | 2014 THIRD INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA 2014) |
ISSN | 2380-8039 |
出版年 | 2014 |
EISBN | 978-1-4799-4184-1 |
出版者 | IEEE |
类型 | Proceedings Paper |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | CPCI-S |
WOS记录号 | WOS:000366526900070 |
WOS类目 | Engineering, Electrical & Electronic ; Remote Sensing |
WOS研究方向 | Engineering ; Remote Sensing |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/302915 |
作者单位 | (1)Nanjing Univ, Key Lab Satellite Mapping Technol & Applicat Stat, Nanjing 210008, Jiangsu, Peoples R China;(2)Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210008, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Xie, Xiangjian,Du, Peijun,Xue, Zhaohui,et al. Phenology-tuned karst rocky desertification monitoring using satellite image time series[C]:IEEE,2014. |
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