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

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. 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.

Copernicus Global Land Cover Layers—Collection 2

“…Additionally, four external metrics including the height, slope, aspect, and purity derived at 100 m were derived from a Digital Elevation Model (DEM). Overall, 270 metrics were extracted from the PROBA-V UTM ARD++ archive for the 2015 epoch which includes spectral, temporal, and spatial features as suggested by Zhai et al [18] and Eberenz et al [19].…”

“…It was derived from 100 m time-series of the vegetation instrument on board of the PROBA satellite (PROBA-V) [16,17], a database of high-quality LC reference sites and several ancillary datasets. We integrated spectral, temporal and spatial features derived from the PROBA-V time series to improve classification accuracy as recommended by Zhai et al [18] and Eberenz et al [19]. Based on the success of this demonstration product for Africa, showing high quality with an overall accuracy of 74.3% and alignment to other continental LC maps [20][21][22][23], Collection 2 of the CGLS-LC100 product was released in May 2019, extending the map to a global coverage for the reference year 2015 [15].…”

Floodwater harvesting to manage irrigation water and mesquite encroachment in a data‐sparse river basin: an eco‐hydrological approach

“…For the purpose of floodwater harvesting (FWH) analysis, a readily available long‐term average land‐cover released in 2010 for global coverage with a spatial resolution of 300 m from the European space agency (ESA) was used in this study (ESA, 2010). This product has been used in many studies around the world (e.g., Eberenz et al, 2016; Herold, See, Tsendbazar, & Fritz, 2016). However, to obtain long‐term annual land‐cover data, which were needed to detect the land cover change in the study area, the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6 data were used.…”

Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

“…The northern part consists of grassland planes with patchy bushes, while more to the south, perennial grasses and woodland are common [2]. Additionally, the region is characterized by a high amount of landscape fragmentation including small agricultural fields and crop/fallow rotation cycles [3,4], both of which represent a real challenge for robust land cover mapping [5]. The Sahel gained specific attention in the 1980s as extreme droughts severely impacted vegetation.…”