Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors

Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58 degrees 27 ' N, 13 degrees 39 ' E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE)

Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics

Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inventory applications based on RS data is that errors from subsequent predictions tend to be strongly correlated, which limits the efficiency of DA. One reason for such a correlation is that model-based predictions, using techniques such as parametric or non-parametric regression, are normally biased conditional on the actual ground conditions, although they are unbiased conditional on the RS predictor variables. A typical case is that predictions are shifted towards the mean, i.e., small true values are overestimated, and large true values are underestimated. In this study, we evaluated if the classical calibration of RS-based predictions could remove this type of bias and improve DA results. Through a simulation study, we mimicked growing stock volume predictions from two different sensors: one from a metric strongly correlated with growing stock volume, mimicking airborne laser scanning, and one from a metric slightly less correlated with growing stock volume, mimicking data obtained from 3D digital photogrammetry. Consistent with previous findings, in areas such as chemistry, we found that classical calibration made the predictions approximately unbiased. Further, in most cases, calibration improved the DA results, evaluated in terms of the root mean square error of predicted volumes, evaluated at the end of a series of ten RS-based predictions

Enriching a primary health care version of ICD-10 using SNOMED CT mapping

<p>Abstract</p> <p>Background</p> <p>In order to satisfy different needs, medical terminology systems must have richer structures. This study examines whether a Swedish primary health care version of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and SNOMED CT's structure. Manually-built mappings from KSH97-P's categories and chapters to SNOMED CT's concepts are used as a starting point.</p> <p>Results</p> <p>The mappings are manually evaluated using computer-produced information and a small number of mappings are updated. A new and poly-hierarchical chapter division of KSH97-P's categories has been created using the category and chapter mappings and SNOMED CT's generic structure. In the new chapter division, most categories are included in their original chapters. A considerable number of concepts are included in other chapters than their original chapters. Most of these inclusions can be explained by ICD-10's design. KSH97-P's categories are also extended with attributes using the category mappings and SNOMED CT's defining attribute relationships. About three-fourths of all concepts receive an attribute of type <it>Finding site </it>and about half of all concepts receive an attribute of type <it>Associated morphology</it>. Other types of attributes are less common.</p> <p>Conclusions</p> <p>It is possible to use mappings from KSH97-P to SNOMED CT and SNOMED CT's structure to enrich KSH97-P's mono-hierarchical structure with a poly-hierarchical chapter division and attributes of type <it>Finding site </it>and <it>Associated morphology</it>. The final mappings are available as additional files for this paper.</p

Book Review

reviewing Eyal Benvenisti & Moshe Hirsch eds., The Impact of International Law on International Cooperation: Theoretical Perspectives (2004

Boundary estimates for solutions to linear degenerate parabolic equations

Let Ω ⊂ R n be a bounded NTA-domain and let Ω T = Ω × (0, T ) for some T &gt; 0. We study the boundary behaviour of non-negative solutions to the equation We assume that A(x, t) = {a ij (x, t)} is measurable, real, symmetric and that for some constant β ≥ 1 and for some non-negative and real-valued function λ = λ(x) belonging to the Muckenhoupt class A 1+2/n (R n ). Our main results include the doubling property of the associated parabolic measure and the Hölder continuity up to the boundary of quotients of non-negative solutions which vanish continuously on a portion of the boundary. Our results generalize previous results of Fabes, Kenig, Jerison, Serapioni, se

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

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

Data assimilation

The purpose of this report is to describe a data assimilation prototype program(Appendix A) developed to estimate forest stand data. The program was developed and tested on data col-lected on the forest estate Remningstorp in southern Sweden. Data assimilation can be used to sequentially combine remote sensing based estimates of forest variables with predictions from growth models. The assimilation routine implemented was the extended Kalman Filter. The program supports two different ways to assimilate the forest data: (1) pixel-wise and (2)stand-wise. In the pixel-wise way, raster cells are used as assimilation unit and can beaggregated to a stand for evaluation. In the stand-wise way, the whole stand is assimilatedas one unit. The two methods has pros and cons. The pixel-wise way is simple to use as nostand-delineation is needed, but might be subject to boundary effects and noise due to geo-metric errors. Using the developed code, it has been shown in three case studies that thecombination of time series of remote sensing data and forest growth functions provides bet-ter estimates of forest variables than only using forecasting, or only using the latest remotesensing data. This opens up for a new way to keep forest stand registers up to date