Classification of forests in the Precarpathian region using QuickBird-2 high resolution satellite image

Based on the study of literature relating to the classification of forests using high resolution space images established that the main problem of classification is the separateness classes and close to the spectral brightness classes can not be identified with high accuracy. Classification using maximum likelihood algorithm, which generally gives better results compared with algorithms of spectral distance or Mahalanobis distance, does not lead to the definition of areas with a high probability. Therefore, the article examines approach of classification of forests using post-processing. Experimental studies were carried using an satellite image of the forested area of Precarpathian region obtained from QuickBird-2 (June 2010). Data collected during field research were used as Verification data to determine areas of different objects. The controlled classification has been performed using the method of the maximum likelihood, size of signatures for 8 classes were selected from 100 to 400 points. For these classes was calculated matrix of separation of classes, and was found a significant correlation between next classes: young conifer plantings and pine and mixed forest, and deciduous young plantings and deciduous forest. Post-processing significantly improves the reliability of determination of area, which consists in the assign to all pixel of the selected neighbourhood brightness of most points, although there is a dependency of reliability of determination of area from the size of the area. Accuracy of determination of areas are from 92 to 99%.