In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.

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A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free observations for the tropics consistently every 6–12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advantage for the rapid detection of small-scale forest disturbances such as subsistence agriculture and selective logging. Alerts were detected with latest available Sentinel-1 images and results are presented from January 2019 to July 2020. We mapped 4 million disturbance events during this period, totalling 1.4 million ha with nearly 80% of events smaller than 0.5 ha. Monthly distribution of alert totals varied widely across the Congo Basin countries and can be linked to regional differences in wet and dry season cycles, with more forest disturbances in the dry season. Results indicated high user’s and producer’s accuracies and the rapid confirmation of alerts within a few weeks. Our disturbance alerts provide confident detection of events larger than or equal to 0.2 ha but do not include smaller events, which suggests that disturbance rates in the Congo Basin are even higher than presented in this study. The new alert product can help to better study the forest dynamics in the Congo Basin with improved spatial and temporal detail and near real-time detections, and highlights the value of dense Sentinel-1 time series data for large-area tropical forest monitoring. The research contributes to the Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement. The alerts are available via the https://www.globalforestwatch.org and http://radd-alert.wur.nl.

Abstract:Along with the creation of new maps, current efforts for improving global land cover (GLC) maps focus on integrating maps by accounting for their relative merits, e.g., agreement amongst maps or map accuracy. Such integration efforts may benefit from the use of multiple GLC reference datasets. Using available reference datasets, this study assesses spatial accuracy of recent GLC maps and compares methods for creating an improved land cover (LC) map. Spatial correspondence with reference dataset was modeled for Globcover-2009, Land Cover-CCI-2010, MODIS-2010 and Globeland30 maps for Africa. Using different scenarios concerning the used input data, five integration methods for an improved LC map were tested and cross-validated. Comparison of the spatial correspondences showed that the preferences for GLC maps varied spatially. Integration methods using both the GLC maps and reference data at their locations resulted in 4.5%-13% higher correspondence with the reference LC than any of the input GLC maps. An integrated LC map and LC class probability maps were computed using regression kriging, which produced the highest correspondence (76%). Our results demonstrate the added value of using reference datasets and geostatistics for improving GLC maps. This approach is useful as more GLC reference datasets are becoming publicly available and their reuse is being encouraged.

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13The production of global land cover products has accelerated significantly over the past decade thanks 14 to the availability of higher spatial and temporal resolution satellite data and increased computation 15 capabilities. The quality of these products should be assessed according to internationally promoted 16 requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and 17 Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 18 validation). Providing updated accuracies for the yearly maps would require considerable effort for 19 collecting validation datasets. To save time and effort on data collection, validation datasets should be 20 designed to suit multiple map assessments and should be easily adjustable for a timely validation of new 21 releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-22 purpose assessments and its applicability is demonstrated in three different assessments focusing on 23 validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. 24 The validation dataset is generated primarily to validate the newly released 100m spatial resolution land 25 cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset 26 includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 27 100m×100m. Within site, reference land cover information was collected at 100 subpixels of 10m×10m 28 allowing the land cover information to be suitable for different resolution and legends. Firstly, using this 29 dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. 30The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6+/-2.1% (at 95% 31 confidence level) for the African continent. Fraction cover products were found to have mean absolute 32 errors of 9. 3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. 33 Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from 34 four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). 35 Overall accuracies for these perspectives vary between 73.7% +/-2.1% and 93.5% ±0.9%, depending on 36 the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the 37 Globeland30-2010 map at 30m spatial resolution. Using the subpixel level validation data, we derived 38 15252 sample pixels at 30m spatial resolution. Based on these sample pixels, the overall accuracy of the 39 Globeland30-2010 map was found to be 66.6 ±2.4% for Africa. The three assessments exemplify the 40 applicability of multi-purpose validation datasets which are recommended to increase map validation 41 efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or 42 updating the dataset with sample sites in identi...