Resumen
The development of high nature value farmland (HNVf) can effectively improve the problems of biodiversity reduction, non-point source pollution and carbon loss in intensive farmland. To this end, we developed a set of general indicators based on Landsat 8 OLI imagery, including land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH) and Simpson?s index (SI). Combined with a Kohonen neural network (KNN), we assigned weights and developed the first potential HNVf map of the Yellow River Delta in China. The results showed that the four indicators were very effective for the expression of HNVf characteristics in the study area, and that SH and SI, in particular, could reflect the potential characteristics of HNVf at the edge of intensive farmland. LC, NDVI, SH and SI were weighted as 0.45, 0.25, 0.15 and 0.15, respectively. It was found that the potential HNVf type 2 (i.e., low-intensity agriculture, and natural and structural elements such as shrubs, woodlands and small rivers) in the study area was concentrated at the edges of intensive farmland, the transition zones from farmland to rivers and the estuary wetland areas of northern and eastern rivers. LC played a leading role in identifying HNVf. Based on six randomly selected real-world verification data from Map World, it was found that the accuracy of the validation set for HNVf type 2 was 83.33%, which exhibited the good development potential of HNVf in the study area. This is the first potential HNVf type 2 map of the Yellow River Delta in China and could provide a great deal of potential guidance for the development and protection of farmland biodiversity and regional carbon sequestration.