Maximizing land cover classification accuracies produced by decision trees at continental to global scales

Classification of land cover from remotely sensed data at continental to global scales requires sophisticated algorithms and feature selection techniques to optimize classifier performance, We examine methods to maximize classification accuracies using decision trees to map land cover from multitemporal AVHRR imagery at continental and global scales, As part of our anal, sis ne test the utility of "boosting," a new technique developed to increase classification accuracy by forcing the learning (classification) algorithm to concentrate on those training observations that are most difficult to classify, Our results show that boosting consistently reduces misclassification rates by approximate to 20-50% depending on the data set in question, and that most of the benefit gained by boosting is achieved after seven boosting iterations, We also assess the utility of including phenological metrics and geographic position as additional features to the classification algorithm, We find that using derived phenological metrics produces little improvement in classification accuracy relative to using an annual time series of NDVI data, but that geographic position provides substantial pou er for predicting land cover types at continental and global scales. However, in order to avoid generating spurious classification accuracies using geographic position, training data must be distributed evenly; in geographic space.