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Remote Sens. 2016, 8(12), 987; doi:10.3390/rs8120987

Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes

Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, 6708PB Wageningen, The Netherlands
ESA Research and Service Support, via Galileo Galilei, 1, 00044 Frascati, Italy
Progressive Systems Srl, Parco Scientifico di Tor Vergata, 00133 Rome, Italy
Authors to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 26 April 2016 / Revised: 2 November 2016 / Accepted: 21 November 2016 / Published: 30 November 2016
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Satellite based land cover classification for Africa’s semi-arid ecosystems is hampered commonly by heterogeneous landscapes with mixed vegetation and small scale land use. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. While most large scale land cover mapping attempts rely on moderate resolution data, PROBA-V provides five-daily time series at 100 m spatial resolution. This improves spatial detail and resilience against high cloud cover, but increases the data load. Cloud-based processing platforms can leverage large scale land cover monitoring based on such finer time series. We demonstrate this with PROBA-V 100 m time series data from 2014–2015, using temporal metrics and cloud filtering in combination with in-situ training data and machine learning, implemented on the ESA (European Space Agency) Cloud Toolbox infrastructure. We apply our approach to two use cases for a large study area over West Africa: land- and forest cover classification. Our land cover classification reaches a 7% to 21% higher overall accuracy when compared to four global land cover maps (i.e., Globcover-2009, Cover-CCI-2010, MODIS-2010, and Globeland30). Our forest cover classification shows 89% correspondence with the Tropical Ecosystem Environment Observation System (TREES)-3 forest cover data which is based on spatially finer Landsat data. This paper illustrates a proof of concept for cloud-based “big-data” driven land cover monitoring. Furthermore, we show that a wide range of temporal metrics can be extracted from detailed PROBA-V 100 m time series data to continuously optimize land cover monitoring. View Full-Text
Keywords: time series; land cover; classification; PROBA-V; semi-arid; Africa; machine learning time series; land cover; classification; PROBA-V; semi-arid; Africa; machine learning

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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Eberenz, J.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Sabatino, G.; Rivolta, G. Evaluating the Potential of PROBA-V Satellite Image Time Series for Improving LC Classification in Semi-Arid African Landscapes. Remote Sens. 2016, 8, 987.

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