Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring
1
Institute for Geoinformatics, Westfälische Wilhelms-Universität Münster (WWU), Heisenbergstraße 2, 48149 Münster, Germany
2
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 1025; https://doi.org/10.3390/rs9101025
Received: 13 July 2017 / Revised: 19 September 2017 / Accepted: 19 September 2017 / Published: 4 October 2017
(This article belongs to the Section Forest Remote Sensing)
In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI), which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a “suitable” index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444) with strong seasonality and a moist tropical forest in Brazil (time series length: 225) with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.
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MDPI and ACS Style
Lu, M.; Hamunyela, E.; Verbesselt, J.; Pebesma, E. Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring. Remote Sens. 2017, 9, 1025. https://doi.org/10.3390/rs9101025
AMA Style
Lu M, Hamunyela E, Verbesselt J, Pebesma E. Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring. Remote Sensing. 2017; 9(10):1025. https://doi.org/10.3390/rs9101025
Chicago/Turabian StyleLu, Meng, Eliakim Hamunyela, Jan Verbesselt, and Edzer Pebesma. 2017. "Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring" Remote Sensing 9, no. 10: 1025. https://doi.org/10.3390/rs9101025
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