5/7/2023 0 Comments Lc dragon landscape![]() ![]() We utilized a unique set of global reference data containing four years of records (2015-2018) at 29,263 land cover change/no-change 100 × 100-m sites. This paper aims to examine and compare the performance in LCC monitoring using three satellite sensors: PROBA-V, Landsat 8 OLI, and Sentinel-2 MSI. 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.Ĭomparing the performance of different satellite sensors in global land cover change (LCC) monitoring is necessary to assess their potential and limitations for more accurate and operational LCC estimations. This paper illustrates a proof of concept for cloud-based “big-data” driven land cover monitoring. 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. 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). We apply our approach to two use cases for a large study area over West Africa: land- and forest cover classification. 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. Cloud-based processing platforms can leverage large scale land cover monitoring based on such finer time series. This improves spatial detail and resilience against high cloud cover, but increases the data load. 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. Higher spatial resolution remote sensing time series data can improve classification results under these difficult conditions. 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. ![]()
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