Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. In Iran, forest inventory information has been essential with respect to land management because 10% of Iran is composed of forests. Therefore, accurate forest information such as tree counts, height, DBH, and volume are critical for forest management. While such data traditionally have required labor intensive and time consuming field measurement, new technologies such as remote sensing have supplemented and supplanted some of these field measurements. Although different types of sensors have been used to extract individual trees information, WorldView-2 (WV-2) has been used recently to extract surface information because WV-2 have high spatial and spectral resolution. In this study, object base classifiers (with KNN way) were used to classify WV-2 satellite and do assessment accuracy with UAV image in study sites. The study indicate that the classification accuracy of Object-based algorithm was best for extraction of dry trees. This study is conducted to evaluate the possibility of WV-2 data to extract forest characteristics from identifying and measuring individual trees. Our results demonstrate that WV-2 data, NDVI with object based classification can be used to detect tree mortality resulting from numerous causes and in several forest cover types.