Land use change is one of the most pervasive forms of ecosystem alteration and is commonly linked to other forms of environmental degradation including runoff increase, erosion, habitat and biodiversity loss, biogeochemical cycle alteration, ground water depletion, invasion of invasive species, the concentration increase of Co₂ and desertification. Studies have shown that landscapes with their natural condition are very little on the earth. Land surfaces have changed significantly due to human activities and the effects of these activities can be easily seen in land use/cover changes. Accurate change detection of earth's surface features is extremely important for understanding relationships and interactions between human and environment. In fact, it provides the basic components of common strategies for natural resource management and also monitoring environmental changes. Generally, the acquisition of the changes in time for sustainable management of natural resources is essential. The process of change detection is the application of multitemporal data for analyzing and quantifying landscape change over time. Remote sensing data due to reputability, broad vision and digital format suitable for computer analysis can provide suitable information for  specifying land use/cover changes. Therefore, the aim of this study is to monitor land use changes in Kale Shore basin, North Khorasan province over 36 years. For this purpose, Landsat satellite images of four periods including1973, 1987, 2002 and 2009 were selected and processed. First the pre-processing of imagery, geometric and radiometric corrections was applied. Then, using different image analysis methods such as NDVI vegetation index, supervised classification and fuzzy classification land use maps of the study area were produced. For change detection purposes, the post-classification technique based on fuzzy classification was used. In the final stage, land use status for the year 2025 was forecast using modeling techniques. The results of change detection showed that in the period of the study (36-year), irrigated agriculture has changed the most among extracted land uses in the region and its area has increased from 4,269 hectares to 15,586 hectares. Another important change is residential area (Esfarayen city) which has increased from 57 hectares to 664 hectares. Also, the area of industrial land use which has been constructed in recent years showed an increase about 22 hectares. Population increase and migration are two main reasons for these dramatic changes. In contrast to above land uses, rangeland and orchard classes, showed a significant reduction due to the development of industrial areas and water resources. An environmental positive change which has been occurred in the study area is that range woodland has increased about 10 percent in 36 years due to the rehabilitation of 12 thousand hectares of rangeland and bare lands. The results of modeling in the region showed a reduction about 300 hectares in irrigated agricultural in 2025. Furthermore, the model has predicted that the rangeland class will be decreased but the poor rangeland class will be increased in 16 years (2009-2025). Another result of CA Markov Model was that due to the development of Esfarayen city, its area will increase from 664 hectares (in 2009) to 1,114 hectares (in 2025). Overall, the results demonstrate that remote sensing technology can be used as an effective land management tool for  monitoring and assessing land use/cover in the region.