changes detection

Today, global warming and climate change are considered to be the greatest challenge of human life that has overshadowed other environmental challenges. In this study, it has been attempted to detect climate change phenomena in Yazd by monitoring the trend of heat wave changes and its relation to temperature anomaly. For this purpose, daily temperature data for the time period (1960– 2017) were used. Finally, the characteristics of heat waves such as the number of occurrences of heat waves, frequency of days with heat waves, heat wave continuity, magnitude and intensity of heat waves and average heat wave (hottest day of heat wave) using ClimPACT software in programming environment R 2.10 were calculated. Heat waves are the result of incremental temperature changes. Therefore, temperature anomaly of Yazd station was extracted and compared with the temperature anomaly of the Land's surface. The relationship between heat wave indices and temperature anomalies was investigated. The results showed that the highest number of heat wave events occurred in 2010 and 2016. The number of days associated with the hot wave has increased significantly, with 47 and 42 days in 2010 and 2016, respectively. An increasing trend in the continuity and intensity of heat waves was also observed. The longest continuation for 2013 was estimated to be 13 days. The findings also show that in the last two decades the magnitude of the heat wave has reached more than 31 ° C. Investigation of the relationship between all indices related to heat wave and Yazd temperature anomaly showed good correlation and coefficient of explanation of temperature anomaly and frequency of days with heat wave was higher than other indices and its R2 was approximately 0.6108. In general, the results indicate the intensification, continuity and intensity of heat waves in Yazd city, which can indicate the occurrence of climate change, especially in the last two decades in this region of the country as a representative of the arid regions. Due to the increasing temperature in the coming years on the frequency, intensity and continuity of these waves will increase, knowing how heat waves are processed can help managers and planners make risk management decisions about energy efficiency.

Indeed, protected areas, national parks and biosphere reserves in general, are the natural heritage of each country. Therefore, knowledge of their changes plays an essential role in management of these areas. Remote sensing is one of the most advanced and effective technology for monitoring environmental changes and resource management. The purpose of this research is to detect the land use /cover changes in Bojagh National Park in Guilan province during 2000-2017. For this purpose, the images of ETM+ sensor from the landsat 7 were taken in the year 2000 and the images of OLI sensor from the landsat 8 were taken in the year 2017. After applying the necessary preprocessing on the images, the training points were selected for each user class in sufficient number and with appropriate processing then, the land use / cover map was produced using the supervised classification method with maximum likelihood algorithm. Using the Overall accuracy test and Kappa coefficients, accuracy of the produced maps was determined. The results of the study indicated that the areas of the sea, grassland and the areas of the waterbody parts has decreased and the areas of the agricultural, marshland, man-made, woody and bare lands users show an increase during the study period.

In recent decades, rapid changes of land use and land cover in the suburbs of major cities in Iran, especially Tehran as the capital, has been growing with important consequences such as the destruction of natural resources, environmental pollution and poor growth of cities. Land use changes and investigation of their causes and factors in a certain time period can be very vital for planners and managers. Purpose of this research is investigation of first grade Markov chain model efficiency for estimating land use changes and land cover for Tehran city and its suburbs during the period of 30 years. In this research multi-temporal LANDSAT satellite images of 1975, 1990 and 2005 were used. Initially, these images were classified in six classes and to the maximum likelihood method. Then, using land cover maps obtained for each period of 15 years, the transformation matrix for land cover classes were calculated between each both time periods. Finally, using the transformation matrix of the first period, the results of land use changes for 2005 was predicted by Markov chain model. Accuracy of the predict with Markov chain model were checked with the help of existing land use maps for 2005. The results showed that the highest percentage of difference is related to arable lands class (0/64622 percent) and the lowest percentage of difference is seen in green space class (0/00551 percent). It is worth noting that in all cases the magnitude of these differences was generally less than 7%, that this number is justified the useful and usable Markov model and its accuracy to predict changes well.