MLAs land cover mapping performance across varying geomorphology with Landsat OLI-8 and minimum human intervention

Abstract

The machine learning algorithms (MLAs) are capable of automatic land cover classification with a huge volume of data and are prevalent in land mapping applications. Minimal human intervention is desired when producing land cover products over a large area and the choice of an algorithm may determine the precision of the map. The study aims to compare the performance of random forest (RF), decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) algorithms in the context of mapping three typical landscapes (plain, foothill, and mountain) in Hunan Province, China, with minimal human interventions. Performance comparisons among the four machine learning algorithms are based on ROC curves, AUC value, confusion matrix, overall accuracy, spatial comparisons and inconsistency along with altitude and slope. RF produced the most accurate maps (93.0% in mountain area, 93.1% in plain region, and 95.2% in foothill) across various geomorphology with minimal human interventions, and was most resistant to landscape pattern complexity. The accuracy of DT was similar to RF including similar ROC curves and slightly lower accuracy. SVM and ANN showed relatively poor performance without significant human intervention. RF produced robust and highly accurate land cover maps over large areas and various complex geomorphology with little human intervention.