Abstract
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.
Highlights
Today, remote sensing (RS) data from different sensors and platforms have become increasingly available for estimating forest characteristics at the scale of plots, stands, landscapes, and entire countries or regions, e.g., [1]
In the following we demonstrate the effects of correlated residuals in composite estimation (CE), assumed to be carried out at plot level using several sources of RS data
In a series of 10 RS-based estimates within a short period of time we show the consequences in terms of estimated and true standard deviation of the CE, and the weight assigned to each new estimate, when the same RS data type was used for all 10 estimates assuming: 1. uncorrelated estimates 2. a correlation of 0.4 between the residuals
Summary
Remote sensing (RS) data from different sensors and platforms have become increasingly available for estimating forest characteristics at the scale of plots, stands, landscapes, and entire countries or regions, e.g., [1]. For practitioners this development is welcome, but it poses several challenges with regard to the selection of RS data source for applications. In case estimates are correlated, this must be taken into account in the calculation of weights and in estimating the variance of the CE. CEs are sometimes applied in national forest inventories, e.g., [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
