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

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Title: The effect of seasonal variation on automated land cover mapping from multispectral airborne laser scanning data
Author: Karila, Kirsi; Matikainen, Leena; Litkey, Paula; Hyyppä, Juha; Puttonen, Eetu
Publisher: Taylor & Francis
Date: 2018
Belongs to series: International Journal of Remote Sensing
ISSN: 0143-1161
URI: http://hdl.handle.net/10138/299465
Abstract: Multispectral airborne laser scanning (MS-ALS) sensors are a new promising source of data for auto-mated mapping methods. Finding an optimal time for data acquisition is important in all mapping applica-tions 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 classi-fication 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 map-ping, however small differences in accuracies between land cover classes exist.
Subject: multitemporal
multispectral
ALS
lidar
land cover
classification
roads
laser scanning


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