, Part 7B 
In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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DETECTION OF FOREST MANAGEMENT OPERATIONS USING BI-TEMPORAL 
AERIAL PHOTOGRAPHS 
P. Hyvonen a *, J. Heinonen a , A. Haara b 
a Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland - (pekka.hyvonen, 
j aakko .heinonen)@metla.fi 
b Dept, of Forest Science, University of Helsinki, FI00014, Helsinki, Finland - arto.haara@metla.fi 
KEY WORDS: Change detection, aerial photograph, segmentation, A>NN, rectification 
ABSTRACT: 
The increased need for timely forest information is leading to the continuous updating of stand databases. In continuous updating, 
stand attributes are estimated in the field following a forest operation (cutting or silvicultural treatment) and stored in databases. To 
determine the changes caused by forest operations and forest damage, a semi-automatic method was developed based on bi-temporal 
aerial photographs. 
The field data consisted of 2 362 forest stands, from which the changes between years 2001 and 2004 were collected from different 
databases. Stands were divided into three classes according to the type of change. The No-change class (1 890) included stands with 
no changes other than growth. The Moderate-change class (373) included stands with changes such as thinning, partly operated 
stand and improvement of young stand. The Considerable-change class (99) included stands with major changes such as clear 
cutting and severe storm damage. The data were randomly divided into training and test data. The aerial photographs were acquired 
for the years 2001 and 2004 with almost the same image specifications and the photographs were temporally registrated. As change 
detection is sensitive to location errors, locational adjustments were made at the stand and segment levels. Linear stepwise 
discriminant analysis and the non-linear ^-nearest neighbour (&-NN) method were tested 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. 
1. INTRODUCTION 
Stand attributes in Finland have been traditionally gathered by 
periodical field inventories with inventory cycles of 10-15 
years. The increased need for timely forest information is 
leading to the continuous updating of stand databases. In 
continuous updating, a stand database is kept up-to-date 
computationally using statistical growth models. After a forest 
operation (i.e. cutting or silvicultural treatment), stand attributes 
are estimated in the field. 
A forest operation can be reported at the time of the work, but 
forest damage, for example, must be determined by some other 
method. There are also errors that should be controlled in the 
databases. Medium-resolution satellite images (e.g. Landsat TM 
images) have been successfully used for detecting considerable 
changes such as clear cuttings, removals of hold-over trees, soil 
preparations or drastic damage. The detection of moderate 
changes such as thinnings, preparatory cuttings or slight 
damage, has been difficult (Holmgren & Thuresson, 1998; 
Wilson & Sader, 2002; Heikkonen & Varjo, 2004). The reason 
for this is that typical thinnings, where about 20-40% of the 
basal area is removed, cause only subtle changes in reflectance 
(Olsson, 1994). 
The reflectance captured by a single pixel in medium-resolution 
images is the average of the reflectance from an area of more 
than 100 m 2 on the ground. However, with high-resolution 
remote sensing materials, the disappearance of individual trees 
can be detected. For example, using data from airborne laser 
scanning (ALS), Yu et al. (2004) found 61 of 83 harvested 
trees. However, ALS is very expensive compared to aerial 
photography. 
Operational high-resolution applications for change detection of 
vegetation cover are based on the visual interpretation of aerial 
photographs, although more automatic methods have also been 
proposed. Hudak and Wessman (1998) investigated a transition 
from grassland to shrubland using historical aerial photographs. 
The images were classified using variograms that characterised 
the image texture. Changes were determined by post 
classification comparison. Kadmon and Harari-Kremer (1999) 
also studied vegetation dynamics using pixel-level spectral 
classification and then averaging the results to larger cells and 
differencing the cell values. In both studies, good results using 
automatic change detection were achieved; but compared to the 
context of the present study, the time-intervals were much wider 
and the changes more radical. In the study of Saksa et al. 
(2003), clear cuttings were detected using three approaches: 
pixel-by-pixel differencing and segmentation, pixel block-level 
differencing and thresholding, and presegmentation and 
unsupervised classification. Each method was found to be 
suitable for operational use. Hyppanen (1999) applied image 
differencing to bi-temporal aerial photographs. In that study, 
considerable changes were detected while moderate changes 
were not. Consequently, the problem of how to detect moderate