Controlling continuously updated forest data by satellite remote sensing

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Abstract
A method has been developed for controlling the quality of continuously updated forest information by satellite remote sensing. In this work Landsat Thematic Mapper (TM) satellite data, used as an economic repetitive form of information, was combined with continuously updated field information. The aim was to direct field inspection on to areas where updating had been absent or erroneous. The multi-temporal Landsat TM image was radiometrically calibrated by band to band regression. In the change classification, changes between the acquisitions were detected by standwise linear nonparametric discrimination. The correct classification percentage was 98 on mineral soil and 91 on peat land. When comparing the image classification results to recorded treatments, 6·9 per cent of stands were recommended for field inspection within a 2-year test period due to unrecorded manmade or unexpected natural change. For a 10-year inventory cycle, this means a recommendation for inspection of only one third of the stands when compared to present updating using repetitive total field inventory.