Researchers: Jukka Heikkonen, Jouni Juujärvi, Sami Brandt, and Jouko Lampinen
This project is financed by Finnish Technology development center (TEKES) and is carried out with Finnish Forest Research Institute (METLA).
The need for information concerning different natural resources is increasing rapidly. Demands are being made for information which is more accurate, up to date and economical to collect for use in management planning of natural resources, controlling the sustainable use of resources and for monitoring the development of the resources. To be able to carry out these tasks in forestry, it is necessary to extract data from large areas on a regular basis and large amounts of information need to be processed. Traditionally, forest information have been gathered by field sampling methods. In the measurement work, analogue devices such as calipers (for measuring tree diameters) and height meters (for measuring tree heights) are used on sample trees. There are problems with the use of these analogue measurements: they are expensive and the amount of accurate data recorded compared to the time taken to collect the data is not always satisfactory.
In this study, new systems for replacing part of the analogue field measurements are developed by utilizing digital photographs of the sample trees. The methodology presented includes the field proof sample tree photographing system. Automatic pattern recognition methodology is developed for 1) describing the three dimensional space from digital photographs, 2) separating tree stems from the photographs. Methodology uses width measurement and height measurement as a priori information for correct segmentation. Stems growing direction is estimated by using neural network which locates stems quite accurately but cannot be used to locate precise stem/background place. Estimated stem edge locations are used to estimate stem width for whole stem by using Lappi's stem form model. In future also other kind of information (stem's straightness,number of branches, number of needles ...) can be estimated from images.
|Figure 3: Sample image|