COMPARISON OF STATISTICAL METHODS AND NEURAL NETWORKS IN 
A POST-OBJECT CLASSIFICATION FOR FORESTRY REGISTRATION 
Bobo Nordahl, Dr.Ing. student 
Department of Surveying and Mapping 
Norwegian University of Science and Technology, NTNU 
N-7034 Trondheim, NORWAY 
E-mail: Bobo.Nordahl(aiko.unit.no 
Commission VII, Working Group 3 
KEY WORDS: Remote sensing, Forestry, SPOT, Neural Networks, Post-Object Classification 
ABSTRACT 
This paper presents a project where post-object classification has been used for forest stand registration. The 
classification method is divided in two steps where the first is pixel-specific and the second is object-based. In the 
pixel-specific classification, statistical and neural network classifications have been compared which is a particular focus 
of this paper. The stands from the previous forest inventory were used as objects in the second step. Ancillary data such 
as slope, aspect and stand with information such as boundaries and classes from the previous inventory, were combined 
with the image data. The project method is designed to prolong the forest inventory intervals rather than replace them 
in order to reduce the cost of forest management planning. The conclusion for the low productive areas is that they can 
not be classified with these methods. The result from the area which is dominated by medium-high site classes, indicated 
that homogeneous stands are better classified with a statistical classifier but a neural network classifier could perform 
a better result with heterogenous stands. After the object-based reclassification the differences are smaller. The results 
do not indicate that neural network classifier would be useful enough to this kind of forest stand classification. 
1. INTRODUCTION scale test have been carried out in [Kolstad 1993]. The 
main differences from Swedish and Finnish conditions are 
The aim of this study is to investigate the possibility of the hilly topography and the smaller average size of the 
using remote sensing images in an alternative forestry — stands in Trgndelag. 
inventory. The project focuses on the coniferous forests in 
a region in the middle of Norway (Trgndelag), with ^ The project consist of three main parts: 
  
Norway spruce (Picea abies) and Scots pine (Pinus 
sylvestris). The initiative to start this project came from 
the local forestry authorities who wanted to reduce the 
high costs of the traditional inventories. It was presumed 
that the traditional stand forestry inventory could not be 
replaced but the intervals of 10 years could be prolonged 
to 20-25 years (the rotation age is 100 to 150 years). 
During these intervals, registrations with remote sensing 
images to update the previous forest inventory should be 
done. 
Several other Nordic tests and projects that used remote 
sensing for forestry purposes have been studied. A method 
for stand delineation has been developed in the program 
named SKOGIS [Hagner 1990 and 1991], which could be 
used as a method to establish objects. The inductive 
approach using cluster analysis to explore the basic 
information in the satellite image is outlined in [Strand 
1989]. The effect from varying reflection depending on 
topographical and background variations is handled in 
[Tharaldsen, Angeloff 1992]. A summary of some Nordic 
projects and an implementation of SKOGIS for a small- 
542 
-New inventory classes have to be established, 
based on what kind of forestry information at the 
stand level that could be separated in the image 
and what kind of information that is needed for 
forest management planning? 
-What kind of ancillary data to be used has to be 
selected. Both to establish objects and to get 
information about the topography and other 
relevant stand information. 
-An interpretation/classification method has to be 
established. Several methods for improving 
classifications have been tested in research 
projects during the last few years and some main 
areas are discussed in [Richards 1993]. When 
working with forest registration the stands should 
be a better classification unit than pixels and 
[Lócherbach 1992] summarized several 
advantages with object-based interpretation 
compared to pixel-based interpretation. 
The first two parts are only roughly outlined in this paper 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996