中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Monitoring Annual Ecosystem Disturbance Caused Urbanization with Landsat on Google Earth Engine (GEE)
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Monitoring Annual Ecosystem Disturbance Caused Urbanization with Landsat on Google Earth Engine (GEE)
Qingling Zhang; Bhartendu Pandey; Karen C. Seto; Kai Chen; Shanxin Guo
2016
Conference NameUrban Transitions Global Summit 2016
Conference Place中国上海
AbstractUrban expansion often causes significant disturbance to ecosystems surrounding cities, sometimes resulting in the removal of large amounts of biomass and in turn putting the human-nature systems at risk. High temporal frequency monitoring is critical to assessing land policy outcomes in addition to gaining an in-depth understanding of the size, type, and rate dynamics of urban areas. Landsat imagery has long been utilized to monitor urbanization and ecosystem change at regional and local scales. However, few studies use Landsat time series to monitor urbanization at higher temporal frequencies, especially for large area applications, mainly due to the lack of efficient algorithms and computation facilities to handle large data volume. Here we extract annual ecosystem disturbance information with Landsat time series and implement it on GEE for large area applications. We develop a compositing algorithm to generate annual Landsat cloud/shadow-free NDVI mosaics and then time series spanning 2000-2012. Changes due to the removal of large amounts of biomass can lead to sudden drop in NDVI values, which can be well captured by the constructed Landsat NDVI time series, considering the relatively small spatial scales of annual urban expansion. We apply this method in Shanghai, China, which has experienced rapid urbanization during the past few decades. Results show annual ecosystem disturbance caused by urbanization is well captured, with a change detection accuracy larger than 80%. Annual cropland (the dominant ecosystem in Shanghai) loss trend from our results is generally comparable to reports from the Statistic Yearbooks, but at faster rates in most years except for 2006 when a special policy implemented to relax the prime agricultural land protection requirement for Shanghai, which might have encouraged local officials to report a larger number. Our method is fast, simple and can be easily extended to large areas on the Google Earth Engine cloud-computing platform.
Department空间信息研究中心
Indexed By其他
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/10293
Collection数字所
Affiliation2016
Recommended Citation
GB/T 7714
Qingling Zhang,Bhartendu Pandey,Karen C. Seto,et al. Monitoring Annual Ecosystem Disturbance Caused Urbanization with Landsat on Google Earth Engine (GEE)[C],2016.
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