Pekka Hyvönen

Segment-level stand inventory for forest management

The aim of this study was to develop a method for segment-based forest inventory and determine whether segment-level inventories can be used in forest management planning. The study area covered 76 ha located in two different aerial photographs in eastern Finland. The study area was segmented into 220 segments with the aid of aerial photographs and the segment-level forest characteristics were assessed in the field using relascope sample plots and a field computer which displayed the aerial photographs, segment borders and surveyors location on the screen. The segment estimates were calculated as weighted averages of k nearest neighbours (kNN) for the segments and the sample plots. The estimates were tested with a cross-validation technique. The averages and the standard deviations of the spectral values of aerial images extracted for the segments and the sample plots were used in the kNN estimation. The relative root mean square error of the mean volume was 58.1% (bias –6.4%) at the segment level and 57.9% (bias –0.9%) at the sample plot level. The segment-based approach studied here needs further research and improvement before it can be applied to forest management planning.

Change detection using bi-temporal aerial photographs and registration at the stand level

There is a trend to continuously update forest data in forest management planning systems. Thus, changes in forest stands caused by, e.g. operations and storm damages should be detected in order to ensure the accuracy of forest data and beneficial decisions related to the treatments of the stands. This justifies the application of aerial photographs in change detection as being reasonable because they are already used in forest management planning. This study presents a semi-automatic method based on bi-temporal aerial photographs and registration at the stand and segment levels for the detection of changes in boreal forests. Linear stepwise discriminant analysis and the non-linear k-nearest neighbour (k-NN) method were tested and statistically compared in classification. The classification results at the stand level were found to be better than at the segment level. Compared to previous studies, the results of this study demonstrate remarkable improvement in the classification accuracy of moderate changes. The results showed that change detection substantially improved when the registration at the stand level was used, especially in the detection of thinned stands. To some extent, the method can be already applied operationally.