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Landuse maps of Khalkhal County using Landsat and IRS imagery by considering geometric and radiometric corrections based on supervised classification with Maximum Likelihood algorithm for 1987, 2002 and 2008 were produced. The accuracy of the produced maps using overall accuracy and Kappa statistic were calculated and results of comparison for the maps of 1987 with 2002 show that, dry farming land has increased from 18.37 to 25.22% and irrigated farming has also increased from 5.77 to 7.30%. On the other hand, forest area has decreased from 2 to 0.38% and rangelands have also reduced from 38.44 to 31.61%. Moreover, the results of map comparison from 2002 and 2008 show that, rangelands and residential areas with 0.23 and 0.06% have increased respectively, and dry farming with 1.58% has the most decreased areas. Statistical analyses in the level of 1 and 5% showed that the rock on the 1988 landuse map were 89 and 91%, and meadow 62 and 65% as the lowest and highest significance. Results of significance for the landuse map of 2005 were 91 and 94% for dry farming, and 67 and 69% for forest as the lowest and highest and for the landuse map of 2008 significance were 86 and 89% for rock, and 67 and 69% for forest as the lowest and highest. By considering accuracy assessment and the significance of the results for the produced maps, the results were acceptable.

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Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. In order to provide the land use map of Hamadan- Bahar Watershed, digital data obtained from TM (1985), ETM(2000) and  OLI (2013) sensor of digital data of the Landsat 5 , 7 and 8 were used. To classify images, were used Maximum likelihood method, using samples of  ground truth. According to the acceptance Kappa coefficient classification by Landis and Koch kappa coefficient, acceptance Kappa coefficient in 1985 (93.11 %), 2000 (90.01%) and 2013 (85.06 %) was excellent. Comparing  NDVI maps with those  of maximum likelihood classification, It also was found that the produced NDVI maps match with Irrigation farming category, indicating the accuracy of maximum likelihood method in classifying images. Result showed that between the years 1992 to 2013 settlements and Irrigation farming have increased 139.93% and 12.29% respectively, while Dry farming and Rangeland have decrease 0.33% and 17.12% respectively.In addation, the results of the conversion of non-residential to residential Maps showed that between the years 1992 to 2013, 2000 to 2013 and 1992 to 2013 among agricultural and pastures, agricultural lands with an area 44. 67, 73. 35 and 94. 84 square kilometers the, respectively, will be allocated the greatest area converted from non-residential to residentia. Also, in order to assess land use changes due to its status in the past, land use map for 2030 was predicted using cellular automata model, Then land use changes trend was plotted during 1990 to 2030. The results indicate a growing trend in settlements and Irrigation farming in the future period. In the period Dry farming and Rangeland will decrease. 

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Introduction
One of the main issues in regional planning and development is land use change by human activities. It can be argued that human actions can lead to significant changes in current state of earth’s surface. Changes in surface cover (land cover change) may in turn lead to alternations in balance of energy, water, and geochemical fluctuations at local, regional or global levels. Land use mapping using remote-sensing data is one of the newest and most widely used methods for the provision of land use map, and making a comparison between the existent usages.Therefore, Considering the benefits and potentials of satellite data, this technology can be of great help in identifying and detecting these changes.
 
Materials and Methods
Processing satellite images and performing supervised classification helps to extract information from these images. This study was carried out for assess changes in  land-use from 1999 to 2019 in the Qazvin plan’s Aquifer. Qazvin plan’s Aquifer is located at the North West part of Iran and the Qazvin province. The land uses observed in visit the area included: 1-irrigated agricultural lands, 2-residential and industrial areas, 3-rangelands, 4-dry and abandoned lands and 5-salt-marsh and barren lands. In this study, ENVI 5.3 software was used for processing five selected imageries in this project (1999,2004,2009,2014 and 2019).
For this purpose, Landsat-5 Thematic Mapper (1999, 2004, and 2009) and Landsat-8 Operation Land Imager Sensor (2014, and 2019) satellite images were used for the land use change analysis with 30-m spatial resolution, which were taken from the United States Geological Survey (USGS; https://glovis.usgs.gov/), and after correcting geometric and radiometric in the pre-processing stage, Maximum Likelihood Classification (MLC) algorithm as a supervised classification method has been used to identify and detect land use changes. Also, The overall accuracy test used to determine the accuracy of produced maps.
 
Results and Discussion
Detection of land use change is one of the most important applications of remote sensing data. The ability to periodically repeat over time, this data can be used to identify and investigate variable and dynamic phenomena in the environment. Different land use classes had been recognized and used as the base map. The result showed that, the area of rangeland lands has decreased from 1999 to 2019 and other uses have increased. so that the area of irrigated agricultural lands, residential and industrial areas, dry and abandoned lands and salt-marsh and barren lands have increased by 14.24%, 38.8%, 25.37% and 8.37%, respectively, but rangelands decreased by 21.16%. Overall accuracy and Kappa statistics were extracted from the error matrix. supervised classification accuracy for the 5 different time frames (1999,2004,2009,2014 and 2019) found from accuracy assessment showed that the highest accuracy was found for 2019 supervised classification (96.28% accuracy). Kappa value is also used to check accuracy in classification and having a Kappa value (0.81–1.00) denotes almost perfect match between the classified and referenced data. The Kappa coefficient for land use in 1999, 2004, 2009, 2014 and 2019 were 87%, 86%, 91%, 89%, and 94%, respectively. Also,The results showed that the extraction of adequate samples from different classes of land use would increase the possibility of correct distinction of image pixels received from the satellite and accurate extraction of land use classes. Thus, obtaining accurate results from the classification of images via the maximum likelihood method is depending on adequate and appropriate training samples.
Conclusion
The present study confirm that remote sensing is an important technology for extracting land use maps and detecting land use changes. Changes detection is made possible by this technology in less time and with better accuracy. Land use changes is one of the most important factors of environmental changes. Such changes is often the result of human intervention, in addition to the negative effects on the environment, will increase the damage to natural disasters. The quantification of land use changes is very useful for environmental management groups, policy makers and for public to better understand the surrounding. Hence, the researchers emphasize the need for the planning to managing natural resources and monitoring environmental changes.