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《Journal of Geo-Information Science》 2017-08
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Review on Landsat Time Series Change Detection Methods

TANG Dongmei;FAN Hui;ZHANG Yao;Institute of International Rivers and Eco-Security, Yunnan University;Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security;  
Change detection based on Landsat time series has become one of the most popular methods of remote sensing change detection. This paper reviews the status of Landsat time series change detection,including comparison of change detection algorithms, Landsat time series construction and accuracy assessment of change detection results. Major problems and challenges of performing Landsat time series change detection are presented. Landsat time series change detection algorithms can roughly be classified into three categories, i.e.,trajectory fitting methods, spectral-temporal trajectory methods, and model-based methods. These algorithms are mostly developed based on forest disturbance. Only few of them were used to detect changes in other land use/land cover types(e.g. urban expansion). Their applications in other fields need further verification. In particular,the comparative study of those different algorithms should be strengthened, which would provide better guidance for users to select optimal detection methods in specific fields. These indices commonly used for Landsat time series change detection can be divided into four groups, including spectral band, vegetation index, linear transformation and their combinations. It is often suggested to combine the advantages of various indices to detect different disturbance types. Although change detection methods based on Landsat time series have developed rapidly, many challenges remain. Upon now, the lack of consistent reference data set for accuracy assessment of Landsat time series change detection is the most serious challenge. Confronted with new challenges, new approaches are needed to calibrate the time series change detection algorithms.
【Key Words】: Landsat images time series data change detection detection indices
【Fund】: 国家自然科学基金项目(41461017);; 国家重点研发计划课题(2016YFA0601601);; 云南省中青年学术技术带头人后备人才培育计划(2014HB005);; 云南大学青年英才培育计划
【CateGory Index】: P237
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