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

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.

Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China

“…By studying the correlation between various remote-sensing vegetation indices and measured forestry data, scholars have evaluated the feasibility of using remote-sensing data to study forest changes, as well as the applicability of various remote-sensing indices [38][39][40]. Macedo et al [41] used forest inventory data (24 plots) and forest indices (NDVI, EVI, SR, and SAVI) derived from high-spatial-resolution satellite images, to estimate and map the aboveground biomass of Mediterranean Quercus rotundifolia in Southern Portugal.…”

Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data

“…In that study, DP point clouds from drones were used in combination with pre-harvest ALS data with promising results. Data assimilation is another approach to combine predictions (Ehlers et al 2013;Nyström et al 2015;Lindgren et al 2017;Ehlers et al 2018). In the data assimilation approach, using for example an extended Kalman filter (Kalman 1960;Kalman and Bucy 1961), the combination of different data sets is split into two parts: (1) update old data through forecast models, (2) combination of forecasted data with new data through weighting inversely proportional to their respective uncertainties.…”

“…Focusing on the forest where no harvest has occurred, several recent studies have used the framework of data assimilation to combine new and old data using forecast models to update earlier predictions (Ehlers et al 2013;Nyström et al 2015;Lindgren et al 2017;Ehlers et al 2018). These early data assimilation studies used the extended Kalman filter (Kalman and Bucy 1961).…”

Using Topographic Attributes to Predict the Understorey Structure of a Wet Eucalypt Forest

“…The canopy at the Warra silviculture system trials (far north-east of landscape) had canopy height around 40 m on poorly drained soils. Returns below 2 m height were discarded from the analysis, thereby avoiding small shrubs and coarse woody debris (Ehlers et al 2018;Packalén and Maltamo 2006;Wilkes et al 2016). A large proportion of forest understorey lies between 2 m and 10 m above ground (Musk 2017).…”