Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set

Phenological differences among broadly defined vegetation types can be a basis for global scale landcover classification at a very coarse spatial scale. Using an annual sequence of composited normalized difference vegetation index (NDVI) values from AVHRR data set composited to 1° DeFries and Townshend (1994) classified eleven global land-cover types with a maximum likelihood classifier. Classification of these same data using a neural network architecture called fuzzy ARTMAP indicate the following: i) When fuzzy ARTMAP is trained using 80% of the data and tested on the remaining (unseen) 20% of the data, classification accuracy is more than 85% compared with 78% using the maximum likelihood classifier; ii) classification accuracies for various splits of training/testing data show that an increase in the size of training data does not result in improved accuracies; iii) classification results vary depending on the use of latitude as an input variable similar to the results of DeFries and Townshend; and iv) fuzzy ARTMAP dynamics including a voting procedure and the number of internal nodes can be used to describe uncertainty in classification. This study shows that artificial neural networks are a viable alternative for global scale landcover classification due to increased accuracy and the ability to provide additional information on uncertainty.