Abstract
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.
Highlights
Semi-arid ecosystems with typically heterogeneous landscapes consisting of mixed vegetation and small scale land use patterns pose challenges for remote sensing (RS) based medium resolution land cover (LC) mapping [1,2]
PROBA-V data proofs are suited for large scale LC analysis, with its dense image time series at a finer spatial resolution (100 m) than common medium-resolution products (e.g., MODIS, SPOT-VEGETATION), and lighter data handling and processing compared to Landsat-like products
The high importance of variables, namely the seasonal metrics, supports our assumption that with the temporal density of the PROBA-V product, reconstruction of seasonal patterns in the tropics is possible despite high cloud contamination
Summary
Semi-arid ecosystems with typically heterogeneous landscapes consisting of mixed vegetation (mosaics of trees, shrubs, and grassland) and small scale land use patterns pose challenges (e.g., mixed pixels) for remote sensing (RS) based medium resolution land cover (LC) mapping [1,2]. This is reflected by relatively low map accuracies in medium resolution (250–500 m) global LC datasets for those areas [3,4]. Cover-CCI-2010, MODIS-2010, and Globeland30-2010) with independent reference data as derived derived in [5] and the extent of our study area over West Africa. Note that the maximum in [5] correspondence and the extentfor of our area largestudy parts of theover studyWest area Africa. is belowNote
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