Data Assimilation in Forest Inventory: First Empirical Results

Abstract

Data assimilation techniques were used to estimate forest stand data in 2011 by sequentially combining remote sensing based estimates of forest variables with predictions from growth models. Estimates of stand data, based on canopy height models obtained from image matching of digital aerial images at six different time-points between 2003 and 2011, served as input to the data assimilation. The assimilation routines were built on the extended Kalman filter. The study was conducted in hemi-boreal forest at the Remningstorp test site in southern Sweden (lat. 13˝37 1 N; long. 58˝28 1 E). The assimilation results were compared with two other methods used in practice for estimation of forest variables: the first was to use only the most recent estimate obtained from remotely sensed data (2011) and the second was to forecast the first estimate (2003) to the endpoint (2011). All three approaches were validated using nine 40 m radius validation plots, which were carefully measured in the field. The results showed that the data assimilation approach provided better results than the two alternative methods. Data assimilation of remote sensing time series has been used previously for calibrating forest ecosystem models, but, to our knowledge, this is the first study with real data where data assimilation has been used for estimating forest inventory data. The study constitutes a starting point for the development of a framework useful for sequentially utilizing all types of remote sensing data in order to provide precise and up-to-date estimates of forest stand parameters.

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

“…This is to be expected, since the posterior error variance in a Bayesian estimator is typically smaller than in the prior estimator [6], and the variance estimators presented in [36] correspond to prior estimators since they assume additive variance accumulation in both stages of the estimation process which ignores the fact that both models have been calibrated based on the same field plot set. But if the field plot set were not a probabilistic sample of the forest area, then LOO variance estimates could be much too low.…”

“…If this is the case, then asymptotic unbiasedness is tested by cross-validation methods, such as Leave-One-Out (LOO). Some recent Bayesian studies of forest inventory that have adopted this approach include [5][6][7].…”

Potential of using data assimilation to support forest planning

“…In essence, DA is a process that can merge data from different sources into a single usable source. One feature of this process is the ability to combine the estimates of uncertainty from each data source to provide updated estimates of the uncertainty for the information (Ehlers et al 2013;Nyström et al 2015). In a forestry context, a typical setup could be to keep the information up to date by integrating growth models in the DA process (Nyström et al 2015) and to use remote sensing to obtain new estimates of the target forest information at regular intervals at low costs (McRoberts et al 2010).…”

“…The results suggest that the assimilation process improves the estimates of forest information over either only forecasted estimates or the most recent estimates from the remotely sensed data. For a more detailed description of the applications, readers are directed to Nyström et al (2015).…”

“…One concrete example of applying DA to improve forestry inventory estimations is the work of Nyström et al (2015). The DA process applied the extended Kalman filter (Welch and Bishop 2006) with univariate models and a few simplifying assumptions.…”