Earth as the human habit, has been affected by natural events, such as tornado and flood of thunder and drought. In addition, some human activities such as urban development and deforestation have made the changes in many ways. However, these changes are unintentional they constantly threaten out the environment. So, predicting these changes are really important in order to face the consequences. Remotely sensed images, due to wide coverage, high resolution and low cost for providing data from the earth, play an important role in environment monitoring. One of the most important applications of remote sensing is change detection. Change detection is a process which measures the differences between objects in the same place at different times. The change detection is an essential tool for monitoring and managing of resources at the local and global scales. The most important criteria in chage detection are the real-time and accurate detection of land cover changes. Hyperspectral sensors operate at continuous wavelengths with a bandwidth of approximately 10 nanometers. Carrying out change detection procedures on hyperspectral images some problems appear that affect the results, such as the presence of noise in the images, and different atmospheric conditions, all of which lead to more computational complexity and an increase in execution time. This paper presents a new unsupervised change detectin method for land use monitoring by utilizing multi-temporal hyperspectral images. By incorporating similarity/distance based and Otsu algorithm in hierarchically manner, this method can detect any changes. The proposed method implements in two main phases: (1) the corrected data by using distance and similarity-based criteria that converted data to new computing space called similarity space. At this space, the changed areas can be a highlight from the no-change areas. (2) The second phase is to make a decision about the nature of pixels by a hierarchical process using Otsu algorithm that result of this phase is a binary change map. The main advantage of the proposed method is being unsupervised with simple usage, low computing burden, and high accuracy. The efficiency of the presented method has been evaluated by using Hyperion multi-temporal hyperspectral imagery. The first dataset is a farmland near the city of Yuncheng, Jiangsu Province, China. The data were acquired on May 3^rd, 2006, and April 23^rd, 2007, respectively. This scene is mainly a combination of soil, river, tree, building, road and agricultural field. The second study area covers an irrigated agricultural field in Hermiston City, Umatilla County, Oregon, USA. These data were acquired on May 1^st, 2004, and May 8^th, 2007. The land cover types are soil, irrigated fields, river, building, type of cultivated land and grassland. The results of two real datasets show high efficiency and accuracy with low false alarms rate by using proposed method compare to common change detection methods with overall accuracy of 98.48%, kappa coefficient of 0.965 and false alarms rate is 1.5% for China dataset as well as overall accuracy of 95.12%, kappa coefficient of 0.87 and false alarms rate is 4.8% for USA dataset.