In: Wagner W„ Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
COMPARISON OF GRID-BASED AND SEGMENT-BASED ESTIMATION OF FOREST 
ATTRIBUTES USING AIRBORNE LASER SCANNING AND DIGITAL AERIAL 
IMAGERY 
S. Tuominen a ’ *, R. Haapanen b 
a Finnish Forest Research Institute, Metsantutkimuslaitos, PL 18, 01301 Vantaa, Finland - sakari.tuominen@metla.fi 
b Haapanen Forest Consulting, Karjenkoskentie 38, 64810 Vanhakyla, Finland - reija.haapanen@haapanenforestconsulting.fi 
KEY WORDS: Forestry, LIDAR, Inventory, Photography, Segmentation 
ABSTRACT: 
Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on 
interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on systematic grid, where the 
grid elements serve as inventory units, for which the laser and aerial image data are extracted and, for which the forest variables are 
estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image 
segments are particularly useful as the basis for generating the silvicultural treatment and cutting units, since their borderlines should 
follow the actual stand borders, whereas the grid elements typically cover parts of several forest stands. In this study we carried out 
an automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating ecologically 
homogeneous micro-stands. Further, we extracted laser and aerial image features both for systematic grid elements and segments. 
For both units, the set of features used for estimating the forest attributes were selected using a genetic algorithm, which aims at 
minimizing the estimation error of the forest variables. The estimation accuracy produced by both approaches was assessed by 
comparing their estimation results. The preliminary results indicate that despite of the theoretical advantages of the image segments, 
the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in 
estimating forest attributes. 
1. INTRODUCTION 
In Finland, the forest inventory for the forest management 
planning has traditionally been based on visual inventory by 
stands. In this method, the forest stands that are delineated on 
the basis of aerial photographs and their growing stock and site- 
related characteristics are measured or estimated in field. The 
method is considered too labour-intensive, and it requires large 
amount of the fieldwork. Thus, the visual inventory method will 
be replaced by a new-generation forest inventory method, which 
is now at the pilot phase and which employs more remote 
sensing data and less fieldwork. 
The new generation forest inventory method will be based on 
interpretation of airborne laser scanning (ALS) data and digital 
aerial imagery using field sample plots as a reference data. 
Statistically the new generation forest inventory method is 
based on two-phase sampling with stratification, where 
inventory database is based on systematic grid of sample units 
(i.e. grid elements as sample units), and the size of grid 
elements should correspond to the size of field plots. Field 
measurements are allocated into strata that are derived on the 
basis of earlier stand inventory data. The typical remote sensing 
data sources that are used in the new generation forest inventory 
system are low density ALS-data (typically 1-2 laser pulses/m 2 ) 
and color-infrared (CIR) digital aerial imagery with spatial 
resolution of approx. 0.5 m. 
The inventory units in the new generation forest inventory 
method are typically defined by a systematic grid of sample 
units. The forest variables are estimated for each grid element 
that covers a square shaped area. As an alternative to the grid 
based approach, the use of automatic stand delineation has been 
studied for defining the inventory units. Automatically 
delineated stands (i.e. image segments) have an advantage 
compared to grid elements (e.g. Pekkarinen & Tuominen, 2003; 
Hyvonen et al., 2005). They can be delineated in such a way 
that they follow exactly the actual stand borders, whereas the 
grid elements are spatially "sparse" in relation to the actual 
borders of stands and other ecological units in forest, so they do 
not follow the borderlines accurately and they usually cover 
trees from more than one stand (e.g. Pekkarinen & Tuominen, 
2003). On the other hand, grid elements are unambiguously 
defined by their coordinates and, thus, the same units can be 
used in consecutive inventories. 
In delineating forest stands the primary input variables are the 
height of the trees and tree species composition (or dominancy). 
Stand density usually is a secondary parameter for stand 
delineation. The height of the trees can be derived on the basis 
the ALS-data but, on the other hand, ALS data with the applied 
pulse density does not serve well the purpose of the recognition 
of tree species. Thus, optical aerial imagery is needed for the 
estimation of the tree species composition. 
The objective of this study was to find a suitable combination of 
laser and aerial data for automatic stand delineation and to test 
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