1203 
INTERGRATION OF HIGH RESOLUTION AERIAL IMAGES AND AIRBORNE LIDAR 
DATA FOR FOREST DELINEATION 
Zuyuan Wang, Ruedi Boesch, Christian Ginzler 
Dept, of Land Resource Assessment, Swiss Federal Research Institute WSL 
zuyuan.wang, ruedi.boesch, Christian - ginzler@wsl.ch 
WG VII/6 
KEY WORDS: Aerial images, Airborne LIDAR, Image Segmentation, Texture, Forest Delineation, Remote Sensing 
ABSTRACT: 
Forest boundary is one of the important parameters in Swiss National Forest Inventory (NFI). It is mainly delineated by aerial photo 
interpretation according to the defined “Forest/Non-forest decision” rules at each sample plot. However, it is not suitable for local 
assessment of forest stands with such kind of coarse grid sampling design. This paper presents an approach intending to delineate 
forest boundary automatically by integrating use of aerial images and airborne LIDAR. Empirical tests show that the proposed 
method offers an automatic process of forest boundary detection for various aerial images in a promising way. However, it’s a 
challenge task to describe NFI forest /non-forest definition with automatic computer-based method and current study provides an 
encouraging basic for further development and testing. 
1. INTRODUCTION 
Forest is an indispensable foundation of human life. It fulfils 
multiple functions such as providing wood and foodstuff, 
protecting soil from erosion and stabilising the climate on a 
regional and global level etc. A third of Switzerland is covered 
in forest. While the plateau has only relatively little forest, by 
contrast the south side of the Alps is particularly rich in forest. 
In order to record in detail the current state and change within 
the Swiss forest in a representative and reproducible manner, 
the Swiss NFI has been carried out for three times by the 
Federal Council: between 1983 and 1985 (NFI 1), 1993 and 1995 
(NFI2) and from 2004 to 2007 (NFI3) (Brassel,2001, Brassel 
and Brandli,1999) . A forest inventory is the procedure used to 
obtain information on the quantity and quality of the forest 
resource and many of the characteristics of the land on which 
the trees are growing(Husch, et al.,1982) . The Swiss NFI 
covers all essential data on land use, giving the surface area, the 
growth and the condition of the forest. Apart from the above 
important forest management indicators, the NFI is designed to 
be a multi-purpose inventory which entails a high demand on 
the methods implementation, flexibility in respect to the 
contents, inventory perimeters as well as data analysis. 
The employment of aerial photograph is involved in Swiss NFI 
so that the cost of the ground survey could be reduced. An 
important application of aerial photograph lies on the 
classification of plot samples in forest and non-forest areas. 
Each aerial photo sample plot was classified according to the 
so-called “Forest/Non forest decision”. The Forest Boundary 
Line (FBL) determines the border line between normal forest or 
shrub forest and non forest. It allows the evaluation of the forest 
width and forest inter-space. These values are needed, in order 
to reach a forest/non-forest decision. The interpreted boundaries 
at each sample plot belong to a regular 500m grid with 25 raster 
pointes. However, local assessment of the forest area and forest 
parameters is not suitable due to such a coarse grid design. 
Furthermore, subjective interpretation results and production- 
oriented uncertainty can’t be avoided despite the careful 
training. 
During the last decade, NFI research has focused on utilizing 
remote sensing data in forest inventory. Remote sensing data 
can make significant contribution to regional and global forest 
cover assessment. It is cost-efficient information for forest 
inventory and monitoring purposes(Pekkarinen,2002a). Satellite 
images have been widely applied in different forest inventory 
and monitoring tasks, e.g. estimation of forest characteristics 
(Lu, et al.,2004, McRoberts, et al.,2007, Tuominen and 
Haakana,2005), change detection (Desclee, et al.,2006, Yen, et 
al.,2005), etc. Haara et al.,(2002) propose a tree species 
classification method using semi-automatic delineation of trees 
on CIR (color infrared) aerial images. LIDAR (Light Detection 
and Ranging data) is an optical remote sensing technology that 
measures properties of scattered light to find range and/or other 
information of a distant target. This technology has applications 
in archaeology, geography, geology, geomorphology, 
seismology, remote sensing and atmospheric physics. A number 
of studies reveal the successfully use of LIDAR-based 
techniques to estimate tree and stand attributes such as height, 
crown diameter, basal area and stem volume(Donoghue, et 
al.,2007, Morsdorf,2004, Naesset,1997). 
There are about 7000 aerial images as well as LIDAR data 
covering the whole Switzerland in NFI3 which can be used for 
extracting forest features. These data allows the implementation 
of modem image processing methods and NFI may be partially 
automatic analyzed. In addition, the advantage of achieving 
reproducible and consistent result will be offered by using the 
available remote sensing data. Improvements are expected in 
areas where it is possible to replace or supplement decisions 
based on expert opinions, subjective interpretations, or 
estimation with measurable quantities. 
This paper suggests an integration algorithm of high resolution 
aerial images and LIDAR data for the improved automatic 
forest boundary delineation. Image segmentation method is 
applied for obtaining homogeneous patterns while LIDAR data