Browsing by Subject "multitemporal"

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  • Peng, Shikui (Suomen metsätieteellinen seura, 1987)
    A study of a 194.3 ha area of forest land dominated by Norway spruce and Scots pine in central S. Finland. A general procedure is presented and discussed for integrating data from permanent sample plots and from satellite images for continuous forest inventory of large areas and for compartment estimations. Methods for estimation, updating and statistical analysis are examined.
  • Karila, Kirsi; Matikainen, Leena; Litkey, Paula; Hyyppä, Juha; Puttonen, Eetu (Taylor & Francis, 2018)
    International Journal of Remote Sensing
    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.
  • Matikainen, Leena; Pandzic, Milos; Li, Fashuai; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Kukko, Antero; Lehtomäki, Matti; Karjalainen, Mika; Puttonen, Eetu (SPIE, 2019)
    Journal of Applied Remote Sensing
    The rapid development of remote sensing technologies pro-vides interesting possibilities for the further development of nationwide mapping procedures that are currently based mainly on passive aerial images. In particular, we assume that there is a large undiscovered potential in multitemporal airborne laser scanning (ALS) for topographic mapping. In this study, automated change detection from multitemporal multispectral ALS data was tested for the first time. The results showed that direct comparisons between height and intensity data from different dates reveal even small chang-es related to the development of a suburban area. A major challenge in future work is to link the changes with objects that are interesting in map production. In order to effectively utilize multisource remotely sensed data in mapping in the future, we also investigated the potential of satellite images and ground-based data to complement multispectral ALS. A method for continuous change monitoring from a time series of Sentinel-2 satellite images was developed and tested. Finally, a high-density point cloud was acquired with terres-trial mobile laser scanning and automatically classified into four classes. The results were compared with the ALS data, and the possible roles of the different data sources in a fu-ture map updating process were discussed.