Long-term forest mapping from classification of MODIS time series: best practices

Abstract

Coarse spatial resolution (CSR) time series have been successfully used at regional scale to produce homogeneous and up-to-date forest cover maps. This study aims to classify CSR time series using a nomenclature as detailed as national forest inventories nomenclatures. To identify best practices for classifying time series, three algorithms are compared: maximum likelihood, support vector machine, and random forest. For each algorithm, training, temporal compositing, and selection of input features have been optimized. Results establish a clear improvement in classification accuracy when red, near-infrared, and short-wave infrared spectral bands are used instead of vegetation indices. Temporal compositing has a major impact when the whole phenological cycle is used for 3 consecutive years. Random forest produces the best classification, support vector machine appears to be sensitive to overtraining, and maximum likelihood is unable to deal with the complex characteristics of forest and natural vegetation classes.