Preprint Technical Note Version 1 This version is not peer-reviewed

Assessing Error Correlations in Remote Sensing-Based Predictions of Forest Attributes for Improved Data Assimilation

Version 1 : Received: 15 October 2017 / Approved: 16 October 2017 / Online: 16 October 2017 (04:30:26 CEST)

How to cite: Ehlers, S.; Saarela, S.; Lindgren, N.; Lindberg, E.; Nyström, M.; Grafström, A.; Persson, H.; Olsson, H.; Ståhl, G. Assessing Error Correlations in Remote Sensing-Based Predictions of Forest Attributes for Improved Data Assimilation. Preprints 2017, 2017100098 (doi: 10.20944/preprints201710.0098.v1). Ehlers, S.; Saarela, S.; Lindgren, N.; Lindberg, E.; Nyström, M.; Grafström, A.; Persson, H.; Olsson, H.; Ståhl, G. Assessing Error Correlations in Remote Sensing-Based Predictions of Forest Attributes for Improved Data Assimilation. Preprints 2017, 2017100098 (doi: 10.20944/preprints201710.0098.v1).

Abstract

Data assimilation (DA) has recently been introduced for stand level forest inventories. The motivation is that numerous new sources of remotely sensed (RS) data are available and practitioners wish to make use of these data in a cost-efficient manner. With DA, new predictions are utilized to the extent motivated by their precision compared to existing predictions. A standard procedure is to develop weighted predictors where weights are assigned inversely proportional to the variance of existing and new predictions. In case the prediction errors are correlated, the correlations should be considered in assigning weights. We assessed the correlation of plot level prediction errors, between predictions of forest characteristics from different RS datasets of the same type as well as across predictions using different sensor types. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for the attributes 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 correlations between predictions using the same type of RS data were positive and strong whereas the correlations between predictions 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 DA are demonstrated.

Subject Areas

Airborne LiDAR; composite estimators; forest inventory; SPOT-5 HRG; TanDEM-X

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