Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1117/12.2323687 |
Quantifying the spatio-temporal variations and impact factors for vegetation coverage in the karst regions of Southwest China using Landsat data and Google Earth engine | |
Pei, Jie1,2; Niu, Zheng1,2; Wang, Li1; Huang, Ni1; Cao, Jianhua3,4 | |
通讯作者 | Pei, Jie |
会议名称 | Conference on Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII |
会议日期 | SEP 24-26, 2018 |
会议地点 | Honolulu, HI |
英文摘要 | This study proposed a remote sensing-based approach to quantify the spatio-temporal patterns of vegetation dynamics and associated impact factors in typical karst regions of Southwest China. Google Earth engine (GEE), the world's most advanced geospatial data cloud computing platform, was employed to construct long time series satellite data set with 30 m resolution, composed of nearly 4,000 Landsat scenes from 1988 to 2016. Image preprocessing was also conducted on the GEE platform. The maximum value composite (MVC) method was used to produce annual maximum normalized difference vegetation index (NDVI) of the study areas. Annual maximum fractional vegetation cover (annFVC) was thus quantitatively estimated based on Dimidiate Pixel Model (DPM). Ordinary least squares (OLS) regression was adopted to identify the spatial patterns of the direction and rate of change in annFVC at a pixel scale. In addition, a terrain niche index (TNI) was used to investigate the influence of topographic factors on vegetation trends. Moreover, the relationships between annFVC and climatic factors were identified using correlation analysis. The results show that annFVC significantly increased at a rate of 0.0032/year in Nandong and 0.0041/year in Xiaojiang watershed for the period 1988-2016. Furthermore, 26.97% and 27.16% of pixels were found to undergo significant increase in terms of annFVC in Nandong and Xiaojiang, respectively. For both Nandong and Xiaojiang, decreasing vegetation trend was curbed with the increase of elevation and slope. Additionally, correlation analysis demonstrated that annFVC was more strongly and positively correlated with temperature than with precipitation in spite of insignificance. |
英文关键词 | Google Earth engine Landsat NDVI Southwest China karst rocky desertification fractional vegetation cover Dimidiate Pixel Model trend analysis |
来源出版物 | MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL REMOTE SENSING TECHNOLOGY, TECHNIQUES AND APPLICATIONS VII |
ISSN | 0277-786X |
EISSN | 1996-756X |
出版年 | 2018 |
卷号 | 10780 |
EISBN | 978-1-5106-2136-7 |
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:000454578000009 |
WOS关键词 | ROCKY DESERTIFICATION ; GREEN VEGETATION ; AVHRR |
WOS类目 | Remote Sensing ; Optics |
WOS研究方向 | Remote Sensing ; Optics |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/307744 |
作者单位 | 1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.CAGS, Inst Karst Geol, MLR Guangxi, Key Lab Karst Dynam, Guilin 541004, Peoples R China; 4.UNESCO, Int Res Ctr Karst, Guilin 541004, Peoples R China |
推荐引用方式 GB/T 7714 | Pei, Jie,Niu, Zheng,Wang, Li,et al. Quantifying the spatio-temporal variations and impact factors for vegetation coverage in the karst regions of Southwest China using Landsat data and Google Earth engine[C]:SPIE-INT SOC OPTICAL ENGINEERING,2018. |
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