---

Abstract
Climate change is an important environmental issue because the melting processes of glaciers and snow density are sensitive to climate change. Today, a variety of satellite sensors such as AVHRR, MODIS, GEOS, MERIS are available for snow monitoring and are widely used to investigate and investigate the fluctuations and changes in snow cover globally. Modis sensor has been considered more because of its global spatial coverage with suitable spatial accuracy and frequent temporal coverage on different scales , Therefore, in the present study, snow products of this sensor were used. In this study, after collecting statistics and data on snow-related days during the statistical period (1989-2018) in three provinces of Kermanshah, Ilam and Lorestan, they were processed using Modis snow cover data in middle Zagros as well as remote sensing techniques, Finally, the snow cover changes in the study area were studied in detail. NDSI index was used in MODIS sensor products to detect snow cover. Consequently, in order to differentiate pixels and identify different phenomena, the received images were processed in GIS environment. .  Investigation of snow cover changes in different seasons using Modis sensor images shows that most of the studied area has a significant decreasing trend, especially in the elevated areas of the study area And only in the western and southwestern regions of the study area, there is no specific decreasing trend. Also, the study of snow covered days during the study period indicates a decrease in middle Zagros snow cover and these changes have been intensified in recent years, especially in snow-covered areas of the region. Also, changes in winter and snow-capped and elevated areas were more and more severe than other seasons and other regions in the study area.           

---

The occurrence of torrential rains in April 2017 in Lorestan province was a clear example of heavy rains that left very heavy damage to agricultural, urban, transportation and communications infrastructure. The purpose of this study is to investigate and reveal the relationship between the physical structure of clouds producing two waves of heavy rainfall in April 2017 in the Doroud catchment area of ​​Boroujerd. In this regard, the statistical characteristics of two precipitation waves on March 25 and April 1, 2019 were analyzed. The supernatural properties of the clouds producing these two heavy rainfall waves were investigated using the Madis superconductor product, MOD06. 4 Microphysical factors of generating clouds These two waves of heavy rainfall in the Doroud-Borujerd basin, including cloud peak temperature (CTT), cloud peak pressure (CTO), optical cloud thickness (COT) and cloud cover ratio (CF) were analyzed. Statistics of these two waves of heavy rainfall showed that in the first wave of heavy rainfall, ie the wave of March 25, 2019, (5 April 1398) 15% of the total annual rainfall and in the second wave, the wave of April 1, 2019 (April 12, 1398) 20% of the total The total average annual rainfall of the region was recorded in these two days. The results of analyzing the microphysical structure of the generating clouds of these two precipitation waves using the MODSI cloud sensor product data showed that the four microphysical factors of the cloud showed a significant spatial correlation with the recorded precipitation values ​​of these two heavy precipitation waves. The two factors of temperature and pressure of cloud peak, which show a vertical expansion of clouds in the area, showed a significant inverse relationship with the amount of precipitation in the basin, while the two factors of cloud ratio and cloud optical thickness have a direct and significant spatial correlation with values. Recorded rainfall showed. The results of this study showed that in these two events of heavy rainfall, a significant and strong relationship was established between the microphysical structure of the cloud and the amount of rainfall recorded in the region.
 

---

Widespread and frequent droughts in recent decades in Khuzestan province have become one of the most important challenges of this province. The use of remote sensing products in temporal and spatial monitoring of drought can play a key role in managing this risk and reducing and adjusting its destructive effects. The main goal of this research is to provide a remote sensing index for temporal and spatial monitoring of drought in Khuzestan province and its validation using station meteorological drought indices. In this research, by using the products of vegetation (MOD13C2) and land surface temperature (MOD11C3) of MODIS sensor, a drought index based on vegetation called VHI plant health index was produced. SPI Meteorological Drought Index, which was based on station rainfall data during the statistical period of 2000-2012, was used to evaluate and quantify this index. The comparison of VHI drought index with three-month SPI meteorological drought index values showed a significant correlation between 0.68 and 0.75. By identifying 4 years with widespread and relatively severe drought in Khuzestan province (based on both VHI and SPI indices), which included the years 2000, 2005, 2012, 2015, the spatial distribution pattern of meteorological drought and VHI plant drought to In general, it indicated that the northern parts of the province were generally involved in mild to moderate droughts and the southern parts were generally involved in moderate to severe droughts. The spatial correlation matrix based on the number of 2500 pixels with dimensions of 5x5 km, which included VHI and SPI values of selected drought years, indicated the existence of a significant spatial correlation between the two mentioned indicators. In the widespread drought of 2000, at the level of Khuzestan province, two drought indices VHI and SPI, the correlation was equal to 0.47, and in 2005, equal to 0.35, and

---

 

---

The main goal of this research is to analyze the time series trend of fire events in natural areas and reveal the relationship between these fire events and vegetation levels in Lorestan province. In this regard, the data of the fire product of the Madis sensor (MOD14A1) and the vegetation product (MOD13A3) of the Madis sensor were used during the statistical period of 2000-2020. The monthly and annual spatial distribution of fires in Lorestan province was investigated. Cross-information matrix analysis and spatial correlation matrix were used to reveal the relationship between fire occurrences and vegetation. The results showed that more than 70% of the total frequency of fire occurrences in natural resources fields (fires with code 2) in Lorestan province is related to June and then July. In terms of the long-term trend, the 21-year trend of the frequency of fire incidents in the province showed that the frequency of incidents in the natural resources areas of the province has generally increased with an annual slope of 3 incidents. The results of the correlation analysis between the monthly vegetation cover and the annual frequency of fire occurrences showed that the fire occurrences in the province showed a significant correlation with the vegetation cover changes in 4 months of the growing period, i.e. from May to August. Cross-matrix analysis between the spatial distribution of fire occurrence foci and NDVI index, both of which were products of MODIS measurement, indicated that, in general, the highest frequency of fire occurrences in Lorestan province in the period from May to August corresponds to Greenness range was 0.15 to 0.22. This range of vegetation generally corresponded to rainfed lands, weak pastures and low-density forest patches

---

Relative humidity is considered as one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. In this regard, the products (MOD05 and MOD07) were used to for estimating the total perceptible water, air temperature and sea- level pressure. Also the product (MOD35) was used for cloud testing, which by performing this test, 2190 cloudless images with 95% confidence for processing was identified. To evaluate the results, radio sound data of Bandar Abbas and synoptic stations in all over the Hormozgan were used. The results showed high accuracy of the used algorithms and experimental model so that R2 and RMSE values of the recorded layers of the sensor and ground data were acceptable. They are in good agreement with ground station measurements. The results showed that the climate of the province is semi-desert with a long warm season and a short cool one. With a closer look, it was found that sea-level pressure and total perceptible water (TPW) in this province are highly correlated with the topography of the region, so that, maximum total perceptible water and sea level pressure were recorded in coastal lowland areas and minimum in the highlands of the province. According to zoning maps, Hormozgan province can be divided into four parts due to relative humidity: from very dry climate with less than 20% relative humidity which is recorded at the highlands to humid areas with more than 65% relative humidity at the coastal area.