Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes

Recent advances in developing new airborne instruments and space-borne missions and in SAR technology, especially in interferometry and coherence estimation, have roused questions: can such new SAR data be utilized in operational forest inventory? What is the accuracy of different satellite data for forest inventory? This paper verifies the explanatory power and information contents of several remote sensing data sources on the retrieval of stem volume, basal area, and mean height, utilizing the following data: Landsat TM, Spot PAN and XS, ERS-1/2 PRI and SLC (coherence estimation), airborne data from imaging spectrometer AISA, radar-derived forest canopy profiles (obtained with HUTSCAT), and aerial photographs. Ground truth data included three different sets ranging from conventional forest inventory data to intensive field checking where one man-day was spent for assessing one stand. Multivariate and neural network methods were applied in data analysis. The results suggested that (1) radar-derived stand profiles obtained with 100 m spacing was the most accurate data source in this comparison and was of equivalent accuracy with conventional forest inventory for mean height and stem volume estimation, (2) aerial photographs (scale 1 : 20,000) gave comparable results with the imaging spectrometer AISA, (3) the satellite images used for the estimation in the decreasing explanation power were Spot XS, Spot PAN, Landsat TM, ERS SAR coherence, JERS SAR intensity images (PRI); and ERS SAR intensity images (PRI). It appears that optical images still include more information for forest inventory than radar images, (4) from all satellite radar methods, the coherence technique seemed to be superior to other methods.