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

Department of Forest Resource Management, Swedish University of Agricultural Sciences, SLU Skogsmarksgränd, SE-90183 Umeå, Sweden
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 667;
Received: 23 March 2018 / Revised: 20 April 2018 / Accepted: 23 April 2018 / Published: 25 April 2018
(This article belongs to the Section Forest Remote Sensing)
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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. View Full-Text
Keywords: airborne LiDAR; Composite estimators; forest inventory; SPOT-5 HRG; TanDEM-X airborne LiDAR; Composite estimators; forest inventory; SPOT-5 HRG; TanDEM-X

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ehlers, S.; Saarela, S.; Lindgren, N.; Lindberg, E.; Nyström, M.; Persson, H.J.; Olsson, H.; Ståhl, G. Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation. Remote Sens. 2018, 10, 667.

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