Applying a unidimensional convolutional neural network for accurate land cover mapping in large areas: A case of study of the Guadiana Hydrographic Demarcation (Spain)

 

Land use changes are one of the main drivers of global change, occurring at an accelerating rate. Therefore, obtaining accurate and up-to-date knowledge of the Earth's surface is essential. This paper aims to produce a land cover map for the Guadiana Hydrographic Demarcation (Spain), a region under diverse environmental pressures and part of one of the largest basins on the Iberian Peninsula. A 1D convolutional neural network (1D-CNN) deep learning method was applied to Sentinel-2 satellite imagery, yielding promising results with high accuracy when compared to other methods. A land cover map for the summer of 2022 was generated with a resolution of 10 x 10 m. Several differences were detected in the coverage of various classes when compared to the previously available data from the Spain's Land Occupation Information System (SIOSE) 2014 reference layer. Notably, “agricultural lands”, which cover more than half of the study area, showed a 7.34 % increase, while “broadleaf” areas exhibited a 7.75 % decrease over the total study area. Greater congruences were found in the larger classes between the two maps. The methodology demonstrated a remarkably high accuracy of 0.96. However, only 59.97 % agreement with the SIOSE layer was observed, due to differences in time periods, minimum representation sizes, and classification accuracies. The high accuracy achieved over such a large area underscores the potential of Sentinel imagery and neural networks for land cover classification, addressing some of the limitations of existing land cover products.