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Remote Sens. 2015, 7(7), 8705-8727; doi:10.3390/rs70708705

Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm

Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA
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Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 20 April 2015 / Revised: 1 July 2015 / Accepted: 2 July 2015 / Published: 9 July 2015
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Abstract

Multi-temporal satellite images are available at very high revisit frequency, allowing the characterization of land-cover transitions and trajectories in greater detail. However, most change detection methods aim to capture a snapshot of land cover, for instance on an annual scale, which do not describe changes that occurred between the annual time points. In this study, we present a sub-annual change detection (SCD) approach to detect change dates from dense satellite time series. SCD estimates change dates by analyzing differences between two consecutive annual segments in two steps. To validate the proposed method, SCD was applied to real and simulated time series of MODIS 16-day NDVI from 2000 to 2012 for MODIS tile h11v05 and a sub-area in southeast Ohio, USA. The results show that SCD can successfully detect from dense time series the dates of changes representing land-cover transitions among various vegetated land covers. In addition, SCD can achieve comparable accuracy on the date of abrupt land-cover changes for the real time series tested in the study area when compared with the trend and seasonal changes identified by a Breaks for Additive Seasonal and Trend (BFAST) method. Future effort will be given to apply the proposed approach to various remotely sensed time series for other areas with extreme climates. View Full-Text
Keywords: change detection; land cover; sub-annual; time series; NDVI; MODIS change detection; land cover; sub-annual; time series; NDVI; MODIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cai, S.; Liu, D. Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm. Remote Sens. 2015, 7, 8705-8727.

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