The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data

Abstract

ABSTRACTMultispectral airborne laser scanning (MS-ALS) sensors are a new promising source of data for automated mapping methods. Finding an optimal time for data acquisition is important in all mapping applications based on remotely sensed datasets. In this study, three MS-ALS datasets acquired at different times of the growing season were compared for automated land cover mapping and road detection in a suburban area. In addition, changes in the intensity were studied. An object-based random forest classification was carried out using reference points. The overall accuracy of the land cover classification was 93.9% (May dataset), 96.4% (June) and 95.9% (August). The use of the May dataset acquired under leafless conditions resulted in more complete roads than the other datasets acquired when trees were in leaf. It was concluded that all datasets used in the study are applicable for suburban land cover mapping, however small differences in accuracies between land cover classes exist.