RADIOMETRIC CORRECTION OF MULTITEMPORAL LANDSAT TM DATA FOR DETECTING RAPID CHANGES 
IN MINERAL SOIL FOREST LAND 
Jari Varjo, Researcher, National Forest Inventory, Finnish Forest Research Institute, Unioninkatu 40A, 00170 Helsinki, Finland 
ISPRS Commission VII, Working Group 1 
KEY WORDS: forest change detection, multitemporal difference images, radiometric calibration 
ABSTRACT 
Possibilities to produce generic training data for forest change detection were studied applying linear radiometric calibration 
combined with studentization. The standwise approach with Landsat TM image pairs was applied and the spectral features used, 
were the difference of stand mean and standard deviation. The timing of several forest treatments within a three-year image 
interpretation interval did not affect to the spectral separability of the treatments. After the regression calibration and 
studentization all the treatment classes were significally separable at least on one difference channel. The possibilities of 
composing generic training data where training observations need not come be from the image pair analysed was demonstrated. 
After regression calibration and studentization the spectral responses were quite similar on all image pairs except on TM channel 
four. 
1. INTRODUCTION 
Detecting changes in forest canopy is important from 
strategic to operational management planning of nature 
resources. For operational purposes, forest change detection is 
necessary for controlling the quality of the. information used 
in forest management because of possible updating errors in 
continuously updated data bases, and for detecting forest 
damages large enough to affect management decisions. For 
smaller scale, updating is needed for detecting trends such as 
defoliation, for controlling forest activities over large areas 
such as the European Union and for detecting natural disasters 
in remote areas. Satellite remote sensing provides an 
economical and repetitive source of information for change 
detection purposes if specific changes can be detected based 
on their spectral response. 
Before detecting changes different pre-processing methods 
for producing multitemporal satellite images have been 
proposed. Normally the satellite acquisitions are first 
registered together and rectified to some map coordinate 
system. After these different radiometric calibrations have 
been proposed to make image acquisitions radiometrically 
comparable (e.g. Singh 1989). However, only few methods 
have been accurate enough for detecting changes such as 
silvicultural activities or forest damages in the Boreal Forest 
conditions. When absolute and relative calibration methods 
have been compared the absolute calibration has not been 
accurate enough, and relative methods have been proposed 
(Olsson 1994). Selection of the actual radiometric calibration 
method depends on the ground truth available. If there is no 
ground truth available, methods such as histogram matching 
have been proposed, but if for example forest/nonforest 
delineation or forest stand delineation is available, good 
results have been obtained by linear regression calibration 
(Olsson 1993, 1994 Varjo 1996). In the case of a single image 
pair the need for radiometric calibration has not been very 
clear. Häme (1991) has shown better forest change detection 
728 
results without calibration but Varjo and Folving (1996) have 
found that calibration improves change clustering results 
notably. In the case of several image pairs, such as in 
operational updating or control of forest management 
information, radiometric calibration seems necessary. If 
supervised methods are used it cannot be expected that 
training data for change detection is collected separately for 
every image pair (Varjo 1996). For producing generic training 
data good results have been obtained by combining linear 
regression calibration and studentization (Olsson 1994). 
In this work, relative radiometric calibration methods are 
studied for producing multitemporal data for detecting rapid 
changes in forest canopy at a scale of 1:20 000. Forest stands 
originating from base line field inventory are used as 
observation units (Varjo 1996). The combination of relative 
radiometric calibration and studentization methods (Olsson 
1994) for detecting changes as small as possible from 
Landsat a TM difference images is proposed for supervised 
change detection methods. The use of relative calibration is 
studied for producing generic radiometrically comparable 
training data for several image pairs covering the same 
geographic location and for image pairs covering different 
geographic locations of a similar forest ecosystem type. The 
interval within Landsat TM image pairs vary from one to 
three years. There are two test sites available for the boreal 
forest zone in Finland. The spectral response due to different 
forest management treatments is analysed. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
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