Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures

Data assimilation of forest variables predicted from remote sensing data

Forest information for management planning is today gathered through a combination of field inventories and remote sensing, but the available flow of remote sensing data over time is not yet utilized for continuously improving predictions of forest variables. In the thesis, the utility of data assimilation, in particular the Extended Kalman filter, for forest variable prediction is investigated. This is an iterative algorithm, where data are repeatedly merged and forecasted. The test site was a forest estate in southern Sweden (Lat. 58°N Long. 13°E). Data assimilation of remote sensing predictions of canopy surface models from digital aerial photogrammetry in paper I and predictions based on interferometric synthetic aperture radar in paper II provided a marginally improved accuracy. This gain was, however, far from the theoretical potential of data assimilation. The reason for this was suggested to be correlation of errors of subsequent predictions across time, i.e. residuals from different predictions over a certain forest area had a similar size and sign. In paper III these error correlations were quantified, and an example of the importance of considering them was given. In paper IV, it was shown that classical calibration could be applied to counteract regression toward the mean, and thus reduce the error correlations. In paper V, it was shown that data assimilation applied to a time series of data from various remote sensing sensors could be used to, over time, improve initial predictions based on aerial laser scanning data. It was also shown how the combination of classical calibration and a suggested modified version of the extended Kalman filter, that accounted for error correlations, contributed to these promising results