It has also been successfully applied in Lithuania for recording an inventory of mature forests to obtain statistically reliable total volume estimates for forestry districts areas of about ha -  , but has failed at the forest compartment level . In total, thousands ha of damaged forest stands consisting of 3. Preliminary field assessments of the damage immediately after the storm produced no more than a rough estimate of the amount and spatial location of damaged stands. An aerial photography survey was also carried out towards the end of , but this rather costly project required the efforts of highly qualified personnel.
Finally, a field survey based on sampling methods carried out in produced reliable statistics on damaged and harvested timber, but this information became available far too late for operational planning purposes. Thus, the key question to be answered is: would satellite imagery provide opportunities for the timely assessment of wind storm damage and also meet the requirements of Lithuanian forestry authorities for estimating damage location and damaged stem volume?
Areas potentially affected by the wind storm were photographed in late September to meet the requirements of forest inventory orthophotographic mapping . Color infrared orthophotos with a resolution of 0. Forest compartments were split into smaller polygons if the continuous wind-damaged area exceeded 0. There were 12 polygons identified in damage class 1 or more with an average area of 0.
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The actual percentage value of the damage was also identified for each polygon. All forest compartments enclosed by the rectangle were selected from the geographical database of the Lithuanian State Forest cadaster for subsequent study. The total number of compartments in the minimum bounding rectangle was 71 , with an average area of 1. The satellite images were processed to the geocoded products using ground control points measured on topographic maps and in the field, stored in Lithuanian coordinate system LKS Relative radiometric calibration was applied to the later image using the multiple linear regression technique of Olsson  - eqn.
Only the pixels located within the forest in the study area were used to develop the regression models for relative image calibration. The final spatial extent of the study area was defined by the intersection of satellite images available for the study and the minimum bounding rectangle around the forest compartments damaged by the wind storm on 8 August Fig.
Next, six image difference grids corresponding to the TM bands were created by subtracting the calibrated pixel values of the later image from corresponding pixel values of the earlier image. Aggregate image difference values were obtained as follows  - eqn. Initially it was thought that, in addition to the X TM value, the interpreted change in the validation data set might have some influence on the interpretation of the results.
Thus, based on the vector data of the mapped wind damage extents, a series of raster grids was constructed to represent actual change due to wind damage. Polygons forest compartments or parts of forest compartments with percentages of stand volume damaged greater than 50, 60, 70, 80, 90 and , respectively, were converted to raster grids using the same grid properties as for the aggregated difference image.
Error matrices  were then created to assess the classification accuracy for all combinations of X TM and the percentage of stand volume that had been damaged. In addition to the spatial location of wind-damaged forest areas, the volume of damaged forest stands is considered to be equally important in Lithuanian forestry. Information on the total stand volume in the forest compartments is usually available from the stand register of the State Forest cadaster.
For each compartment, two methods of resolving the wind-damaged stand volume were used:. The k -NN method is a multi-dimensional version of the inverse distance-weighted technique, briefly described as follows  : the Euclidean distance d i,p is calculated between each first-phase observation unit p in this case, p is a forest compartment inside the test area in n -dimensional feature space of auxiliary information, and a second-phase observation unit i with a known value of wind-damaged stand volume here we used 9 randomly selected compartments from the wind damage assessment data set obtained from orthophotomap interpretation representing all tree species, age class, relative stocking level and wind damage class combinations in the test area.
Here, n refers to the total number of layers of auxiliary information-age class, volume per hectare, relative stocking index, area of the compartment originating from the stand-wise forest inventory, and the percentage of area that has been classified as changed, according to satellite images; and k represents the distances d i,p - d 1 ,p The value of wind-damaged volume per hectare for each compartment p of first-phase observation is given by eqn.
The list of layers of auxiliary information and the values of k and t were iteratively chosen using leave-one-out technique in order to minimize the prediction root mean square error. The lowest prediction root mean square errors were achieved with all layers of auxiliary information and a value of t equal to 1.
To obtain the wind-damaged volumes in all compartments of the test area to be used for validation, we simply multiplied the stand volume as accessed from the stand register by the damage percentage interpreted from the orthophotomaps. Increasing the threshold value to consider the value of a difference image caused an increase in the overall identification accuracy of wind-damaged areas Fig. The same trends were observed not shown in Fig.
However, only Therefore, the total volume of damaged forest in the study area estimated using the Solution 1 i. The total volume was underestimated by This might be explained as being a result of the dispersed nature of the distribution of wind-damaged trees throughout the whole area of the compartment, especially when the damage classes were low, such as 1 or 2.
Solution 1 : prediction using only difference of two satellite images; Solution 2 : prediction using k -NN and information from satellite images and stand-wise forest inventory; Validation : aerial photography-based inventory. For more details, see text. To summarize, the forest damage estimates arrived at by using Solution 1 lay outside the acceptable range for Lithuanian forestry. However, the introduction of auxiliary information other than satellite imagery, which is readily available from the State Forest cadastre Solution 2 , improved the total estimates of wind-damaged forest volume notably Tab.
The total volume of damaged trees was underestimated by just 2. The use of the k -NN method in forest inventories is usually connected to sampling-based inventories and for generating statistics, basically volume, for areas larger than a forest compartment. As mentioned above, attempts to analyze data at the forest compartment level have not always been successful, and studies of this topic are only modestly represented in the literature. Other research projects used potentially more accurate - therefore more costly - remote sensing data, or else considered somewhat elementary forest conditions.
Previously, the approach to obtain forest compartment-level estimates has mainly involved the use of aerial imagery and historical forest inventory records  ,  , aerial images together with, or independently of, data derived from laser scanning  ,  ,  ,  ,  ,  ,  , . More recently, similar research in Lithuania reached the conclusion that summary statistics only, such as the mean volume of growing stock per hectare for some areas, could be applicable operationally if the k -NN predictions were done at a forest compartment level .
However, it seems that the estimation of the stand volume lost due to natural disasters using a similar approach has not previously been studied. Similarly to most of the research mentioned above, the prediction unit in the present study was a forest compartment. The prediction accuracies expressed in terms of root mean square error are usually quite poor at the prediction unit level using non-parametric methods  and the bias is low. The root mean square error tends to drop further for larger aggregations. We did not report in details the prediction accuracies at a forest compartment level they were used to choose the settings for k -NN prediction , bearing in mind that most of the compartments did not exhibit any damage neither in the field nor in predictions.
Aggregated damage estimation for the whole study area is compatible with the major objective of such assessment, which is to provide general statistics for strategic planning related to the elimination of the consequences of a natural calamity. One of the major features of non-parametric methods is that we may expect no unrealistic predictions to occur if the reference material is appropriate. In fact, although all forest compartments in the study area were inventoried using color infrared orthophotos and provided with the forest damage characteristics, under operational conditions it may be quite difficult to construct such a set of second-phase observations.
The extents of natural calamities such as wind storm damage are first roughly audited in Lithuania by field visits by state forest officers, as has been done in this particular case.
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Data is usually collected for some parts of compartments that are accessible, known, concentrated on state-owned land, etc. Assuming that the total damage estimates may differ under other conditions, for rapid assessment of forest damage caused by wind storms we still consider the approach using satellite images and stand-wise forest inventory data described in this paper as possessing some potential.
The main reason for such optimism is the fact that both satellite images and forest inventory data are available free of charge.
Rapid changes in a forest may be assessed using a combination of satellite images and stand-wise forest inventory data. The proposed solution for predicting the percentage of damage in terms of wind-fallen or broken stem volume for each forest compartment inside the zone potentially affected by the wind storm using the non-parametric k -nearest neighbors technique and classified difference images originating from two satellite images captured on dates before and after the wind storm event, together with forest compartment characteristics accessed from the Lithuanian State Forest cadastre, resulted in acceptable accuracies at a study area level.
The overall underestimation was 2. Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data.
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