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Human interventions in natural areas, such as changes in land use, have led to a series of anomalies and environmental hazards. These significant and cumulative changes in land cover and land use have resulted in various anomalies, including excessive runoff, soil erosion, desertification, and soil salinization. The primary objective of this study is to examine the temperature variations caused by the land cover structure in Lorestan province and analyze the impact of land use changes on the province's temperature structure. To accomplish this, data from the MCD12Q2 composite product, which provides information on land cover classes, and ground temperature data from the MOD11A2 product of the MODIS sensor were utilized. Additionally, the cross -analysis matrix (CTM) technique was employed to identify temperature variations for each land cover class during both hot and cold seasons. The findings indicated that Lorestan province had five distinct land cover classes: forest lands, pastures, agricultural lands, constructed lands, and barren lands. The cross -matrix analysis revealed that forest cover (IGBP code 5) exhibited the highest temperature, reaching 48°C, while urban and residential land cover (IGBP code 13) had a temperature of 16°C, making it the warmest land use. Furthermore, it was observed that the thermal variations of land cover were minimal during the warm season, with no significant difference between the temperature structures of land cover classes. However, during the cold season, the thermal variations became more pronounced. The analysis of variance test demonstrated that, unlike the warm season, different land cover classes significantly (Sig = 0.026) influenced the thermal variations in the province during the cold season. Scheffe's post hoc analysis indicated that this difference was particularly noticeable between rangeland cover classes and built-up cover. [ABSTRACT FROM AUTHOR]

Land cover and land use change occurs during the time due to human activities. Identification and detection of these changes can help planners and managers to recognize the effective factors which cause the changes, and use them at different levels of planning. Studying the rate of resources changes and destruction, and prediction and feasibility study of these changes in future years may play a key role in planning and optimal use of resources and controlling undesirable changes in the future. There are various methods for predicting land use and land cover changes, among them the Markov Chain model can be pointed out. In this study, by using the Landsat satellite data of TM (1995) and ETM+ (2000 and 2007) sensors, complementary data of the study area and Markov Chain model the changes occurred in land use and land cover over the past 22 years in Doviraj region of Ilam province are investigated. After performing required corrections on the satellite images and field visits, different land use classes were defined, the training samples were selected and the accuracy of obtained results was evaluated using Kappa coefficient. The results indicate the decreasing trend of forest cover and average rangeland areas of the region (i.e. 2.9 and 17.07 percent , respectively) and increasing trend of poor rangelands, agricultural lands, residential areas and barren land uses (9.17, 2.96, 0.4 and 7.44 percent, correspondingly) which represent total destruction in the region and replacement of weaker land uses. Finally, using Markov Chain model, the status of land use classes in the next 14 years (2021) has been predicted. The results from changes prediction matrix based on the maps of 2000 and 2007 showed that in the time period of 2007-2021 it is probable that 62% of forest lands, 38% of average rangelands, 64% of poor rangelands, 72% of agricultural lands, 69% of residential areas and 76% of barren lands remain unchanged. The average rangelands are of the highest stability and on the other hand poor rangelands have the lowest stability. The calibrated land use maps by Markov Chain model can be employed as an alarming system about the effects and consequences of future land use changes.

Land cover is defined as a physical and biological cover of the earth’s surface that contains water, vegetation and bare soil. Limiting factors in arid areas such as water shortage and soil salinity are important factors to study the occurred changes in land cover. Due to the large expanse of land, change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital. The overall aim of this study was to assess changes in land cover in arid lands of Marvast plain, located in south of Yazd. In this study, ASTER (Terra) data for summer month of July for years of 2003 and 2010 as well as ETM+ data (Landsat) for July 2002 and 2010 was used. In the first step, after pre-processing operations such as geometric correction, land cover maps of the study area were classified into 5 classes (agricultural land, rangeland, bare land, desert and wetland) using Maximum Likelihood method in both satellite types. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results of ETM+ classification show that the areas of agricultural land and wetland were increased and decreased, respectively in time span of 2002-2010. The same changes were also observed in change classification maps of ASTER for the period of 2003-2010. In this time period, the area of wetland is decreased and changed into desert area. In addition, rangeland area has reduced and changed into agricultural land. The results show that produced maps of both sensors have accuracy of above 85% which reflects high performance of both images in land cover change analysis. In spite of the fact that ASTER data is a little more accurate, results of using Landsat data which has more accessibility and lower cost, for change detection analysis is also acceptable and valuable.