The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 707–714, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-707-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 707–714, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-707-2020

  25 Aug 2020

25 Aug 2020

BENCHMARKING OF HIGH-RESOLUTION LAND COVER MAPS IN AFRICA

G. Bratic1, S. Peng2, and M. A. Brovelli3 G. Bratic et al.
  • 1Politecnico di Milano, Dept. of Civil and Environmental Engineering, Via Gaetano Previati 1/c, 23900 Lecco, Italy
  • 2National Geomatics Center of China, Lianhuachi West Road 28, 100830 Beijing, China
  • 3Politecnico di Milano, Dept. of Civil and Environmental Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy

Keywords: Global Land Cover, Benchmarking, Inter-comparison, Validation, Accuracy Metrics

Abstract. This paper addresses the issue of increased validation demands due to growth in the production of land cover (LC) maps, especially those with large coverage and high-resolution. The inter-comparison of two high-resolution LC (HRLC) maps – GlobeLand30 for the year 2015 (GL30-2015) and S2 Prototype LC 20m map of Africa for 2016 (CCI Africa Prototype) – was done to estimate the degree to which they share the information, as this can serve as a benchmark of their accuracy. Since the two maps compared are independently classified, there is a higher probability that areas where they share information are correctly classified. CCI Africa Prototype and GL30- 2015 have not been yet validated for whole Africa and therefore benchmark accuracy can be used to better design the validation and to make it more efficient. Based on the pixel-by-pixel comparison of GL30-2015 and CCI Africa Prototype, the error matrix and accuracy indexes (Overall, User’s and Producer’s accuracy) were derived. Overall accuracy on the continent level is estimated to be around 66%, which is not considered satisfactory. The low value of overall accuracy is mostly due to the low accuracy of classes Shrubland, Wetland, and Permanent ice and snow, as their User’s and Producer’s accuracies are below 0.4. On the opposite, benchmark accuracy is fairly high for Forest (0.68), Water bodies (0.86) and Bareland (0.93). Nevertheless, class benchmark accuracies are different from country to country, so as the Overall accuracy. Benchmark accuracy was not estimated for Cultivated, Grassland and Artificial surface classes due to the large difference between User’s and Producer’s accuracies.