ndvi

Vegetation cover is affected by the interplay of precipitation and temperature, as the amount and timing of precipitation, along with temperature patterns, directly influence plant growth, distribution, and overall health. The Normalized Difference Vegetation Index (NDVI) serves as an indicator of surface vegetation condition and effectively reflects the impacts of environmental changes. Precipitation and temperature are key controlling factors in NDVI variations. This study aims to examine the effects of precipitation and temperature changes on NDVI values derived from MODIS data in Yazd Province over a 24-year period (2000 to 2023).

This study utilized NDVI data extracted from the MODIS sensor aboard the TERRA satellite. The NDVI index with a spatial resolution of 250 meters was generated every 16 days. These images, which include vegetation indices, are used for global vegetation monitoring, land cover representation, and its changes. The analysis of NDVI changes was performed in Google Earth Engine (GEE), and spatial analysis of the images was conducted using ArcGIS 10.8.3. Due to the lack of long-term precipitation and temperature data and the absence of suitably distributed meteorological stations in Yazd Province during the study period, ERA5-Land reanalysis data for the 24-year period (2000 to 2023) were utilized. The spatial resolution of ERA5-Land is 9 km, and its temporal resolution is hourly. Data processing related to the calculation of annual, monthly, and seasonal averages of NDVI, precipitation, and temperature was carried out, followed by trend analysis using the Mann-Kendall test. The correlation between NDVI and climatic variables (precipitation and temperature) was examined at the annual, monthly, and seasonal scales.

The time series of NDVI, precipitation, and temperature at the annual scale for the 23-year study period showed an insignificant upward trend. Correlation analysis between NDVI and precipitation and temperature revealed that at the annual scale, the correlation of NDVI with precipitation was 0.31 (r = 0.31, p-value = 0.05) and with temperature was 0.15 (non-significant). At the seasonal scale, the highest correlations between NDVI and temperature occurred in autumn (r = 0.14), while the highest correlation with precipitation occurred in spring (r = 0.75). At the monthly scale, the strongest positive correlation with temperature was observed in July (r = 0.31) and the weakest in February (r = 0.08), while the strongest positive correlation with precipitation occurred in March (r = 0.38) and the weakest in September (r = 0.04). Based on the average monthly correlation coefficients, the effect of precipitation on vegetation changes was found to be stronger than that of temperature. A differential map of NDVI between the start (2000) and end (2023) years indicated that areas with increasing and decreasing vegetation changes were scattered irregularly and did not follow any specific spatial pattern. Vegetation changes in different areas were observed with considerable variation in magnitude and form.

The use of advanced remote sensing techniques, such as time series analysis of satellite images, the integration of multi-source data, and machine learning algorithms, has become a focus for improving NDVI estimation and other climatic variables, including temperature, precipitation, and evapotranspiration. The results indicate that both precipitation and temperature have a positive effect on NDVI, with precipitation having a greater impact. However, the intensity of this effect varies depending on the time scale. At the annual scale, NDVI showed the highest positive correlation with both precipitation and temperature. In spring, the highest correlation between NDVI and precipitation was observed, while in summer, the highest correlation occurred between NDVI and temperature. The findings of this study show that vegetation changes, in addition to the direct impacts of climatic variables, are also indirectly influenced by human activities such as land-use change, water resource management, and land exploitation in Yazd Province. These findings contribute to a deeper understanding of the interactions between vegetation cover, climatic variables, and human activities.

As a natural disaster, drought may occur in any climate. In recent decades, widespread severe droughts have continuously affected Iran, imposing detrimental effects on the country’s various economic sectors, including agriculture, environment, and water resources. Today, vegetation indices obtained from remote sensing are widely used to identify and analyze meteorological droughts. Remote sensing technology enables near-real-time monitoring of drought conditions by analyzing high-resolution spectral data, allowing for pixel-level calculations over large geographic areas. The Iranian provinces of Qom, Isfahan, Chaharmahal Bakhtiari, and Markazi are among those regions whose drought conditions have frequently been warned about within the last few years. Therefore, as the study of drought in these four provinces bears special significance due to the sensitivity of the provinces and the large population they accommodate, the current research selected the provinces as its study areas.

this study set out to investigate the correlation between SPI, NDVI, and EVI that were obtained from MODIS images from 2011 to 2020, seeking to monitor drought in central regions of Iran. To this end, changes made over a period of 10 years were identified using the images of the Modis satellite sensor and the precipitation data collected from the synoptic stations located in the study area. In this regard, four months (April, May, June, and July) were selected as sample periods by reviewing the data collected from the existing stations using the standardized precipitation index (SPI) model. This study selected MODIS Terra MOD13A2 imagery from 2011 to 2020 due to its high temporal resolution, broad spectral coverage, ease of access, and the absence of atmospheric and geometric correction requirements. This dataset was chosen to ensure the capture of both wet and dry periods. Subsequently, the Standardized Precipitation Index (SPI) was compared with the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Moreover, the Pearson correlation coefficient was used to determine the correlation between the SPI meteorological drought index and remote sensing indices.

According to the study’s results, the correlation between SPI NDVI and EVI was found to be 0.832 and -0.149, respectively. In general, the results indicated that in areas with insufficient precipitation data and poor distribution of drought monitoring, remote sensing, and NDVI data can be used to monitor vegetation changes. Moreover, the results of drought monitoring revealed that during the ten-year study period, severe droughts occurred in some years. For instance, severe drought and extremely wet periods occurred in 2020 and 2011, respectively. On the other hand, the results of the correlation between SPI and remote sensing indices suggested that SPI had the highest correlation with NDVI at the level of 0.01. The results of this study can effectively contribute to the decisions made by decision-makers in monitoring, investigating, and resolving drought conditions.

Vegetation characteristics, the studied time period, soil characteristics, and the distribution and intensity of precipitation are important factors involved in the establishment of the highest correlation coefficient between the NDVI and the SPI during the delay period. Satellite indices show a remarkable correlation with each other in terms of detecting the magnitude of change, and the highest correlation between satellite indices and terrestrial indices is found in the NDVI-SPI pair. Therefore, the NDVI is used to monitor meteorological drought. Compared to point meteorological methods (precipitation recording stations), satellite images offer greater advantages, including the number of sampling points, wider coverage area, higher time resolution, and lower cost. Therefore, remote sensing knowledge is suggested for drought monitoring. Generally, remote sensing data and NDVI are suggested as appropriate indices to be used for monitoring vegetation changes in areas with insufficient rain gauge data and inappropriate distribution of drought monitoring.

Desertification is land degradation that occurs in arid, semi-arid, and dry sub-humid areas, where water is the most important limiting factor for land use in the ecosystem (19). Desertification (31) is a term used to describe the decrease in biological productivity in ecosystems of arid, semi-arid, and dry sub-humid regions caused by climate change and human activities. To provide management solutions and prevent desertification spread globally, it is necessary to evaluate the phenomenon and its changes over time. At the global level, numerous studies have been conducted to evaluate and monitor desertification using various methods and evaluate its changes over time. In recent years, there has been a lot of attention given to remote sensing in this regard. Lamchin et al. (2016) examined land cover changes and desertification in Mongolia using remote sensing indices such as NDVI, albedo, and TGSI. It was discovered that the study area has experienced an increase in desertification each year (16). Ontel et al. (2023) investigated the trend of land degradation and desertification in Romania using remote sensing indices. The research findings revealed that 60% of the limited study area (22) had been improved and restored. In Iran, the majority of the land is located in arid, semi-arid, and hyper-arid climates (4), and the duration of the warm season is increasing in 80 percent of the regions (10). These conditions will lead to the continued growth and expansion of desertification in Iran. Therefore, finding methods to assess this phenomenon, its causes, and predict its trends will become increasingly important. The management of vegetation cover and natural resources requires an assessment of the desertification process due to the semi-arid and desert climate of Kerman province. This study aims to examine the trend of desertification over time using remote sensing due to the issue's importance and the climatic conditions of Kerman province.

Kerman province, with an area of 183,000 square kilometers, accounts for approximately 11% of the total area of the country. Satellite images were used to calculate the intensity of desertification to achieve the set objectives. To calculate the intensity of desertification, the DDI index introduced by Pan and Li (2013) was utilized in this study. To evaluate desertification in Kerman province, the first step was to establish the target months by utilizing the monthly NDVI average. Estimates of NDVI were made on the Google Earth Engine platform from 2001 to 2022 using the MOD09A1 product layers of the MODIS sensor, and the monthly average for the study period was then determined. An average Albedo coefficient map was calculated for the period 2001 to 2022 after determining the target months and obtaining the average NDVI map from 2001 to 2022 in these months. The intensity of desertification for the target months was calculated by performing a linear regression between NDVI and Albedo coefficient in the next step. The highest intensity of desertification for every target month was determined by determining the year with the lowest DDI value at pixel level. After calculating the DDI index for the 22-year study period, to examine the trend of desertification changes, the trend of DDI index changes for the target months was calculated from 2001 to 2022 using the non-parametric Kendall test in the TerrSet software. The slope of changes in the DDI index for the target months in time units was calculated using TerrSet software for 22 years. To simulate the trend of changes, linear regression analysis can be utilized.

The highest NDVI values over the 22 years are in March, April, May, and June, which indicate the highest growth and greenness of vegetation during these months, as per the results obtained. The four months with the highest NDVI values were chosen to investigate the trend of desertification changes based on the results of this section. The most severe desertification in March was 23.93% in 2012, while the lowest was 23.0% in 2016. The highest and lowest severity of desertification in April were 22.23% and 35.0% in 2012 and 2006, respectively. The most severe desertification was recorded at 20.14% and 22.42% during May and June in 2001, while the least was 32.0% and 11.0% in 2017, respectively. The results indicate that the trendless class has allotted the largest area throughout the 22-year period, with values of 82.45%, 59.83%, 49.96%, and 51.79% in March, April, May, and June, respectively. The results indicate that desertification changes with high and very high intensity mostly occur in the southwest, south, and southeast regions of Kerman province. The northwest and northeast regions of the province are also a part of this class. The vegetation cover values in March, April, May, and June were the highest in the year, as shown by the average monthly NDVI results. Behrangmanesh et al. (2019) and Alamdarloo et al. (2018) reported that the vegetation cover in most regions of Iran is at its peak during these months. The results indicated that the intensity of desertification is increasing considerably in Kerman province in all four selected months, particularly in the southern regions. The assessment of desertification changes showed that the southern regions of Kerman province are classified as high and very high in all four selected months. The effectiveness of the desertification intensity assessment method proposed by Pan and Li (2013) can be demonstrated by analyzing the results of this study in Kerman province and comparing it to previous research in this area.

Soil erosion and sedimentation are among the major problems and environmental challenges in watersheds. The sediment load in rivers causes numerous issues, such as sedimentation in dam reservoirs, changes in river courses due to sedimentation in their beds, reduced water carrying capacity in waterways and water transfer facilities, and changes in water quality for drinking and agriculture. These problems are of great concern to researchers and water resource managers as they can investigate sedimentation, erosion, and potential effects on biological processes. Therefore, it is essential to investigate and evaluate suspended sediment concentrations to determine water quality and hydrological functions. In this regard, the use of accurate, extensive, and cost-effective techniques, such as remote sensing, is invaluable for improving sedimentation estimation and, consequently, water quality assessment. Understanding the spatial relationships between upstream vegetation and sedimentation is crucial for the effective control and optimal management of water resources and soil. The present study aimed to determine the relationship between vegetation cover and suspended sediment concentrations in the Doab Mereg and Gamasiab watersheds.

In this research, the Sentinel-2 satellite data were investigated in the first step. If there were cloudy conditions, dust particles, or other radiometric problems, they were removed from the calculations. Then, factors such as band type (visible and infrared bands), river width (higher than the image pixel - 40 meters at the Doab station and 80 meters at the Polchehr station), separation power, and sensor location (10 and 20 meters) were considered to select the appropriate image pixel for obtaining the spectral reflectance of water. The pixel corresponding to the hydrometric station and its surroundings was selected to extract the spectral reflectance. Then, the suspended sediment concentration statistics of the Doab Mereg station located in the Qarasu River and the Polchehr station located in the Gamasiab River in the five-year period (2016 to 2020) were used simultaneously to investigate the correlation between the spectral reflectance of the Sentinel-2 image bands and sediment concentration. Next, the Normalized Difference Vegetation Index (NDVI) for the two May and June seasons was extracted using Sentinel-2 images. The relationship between vegetation cover and suspended sediment concentration recorded at the station and extracted from the images was estimated separately.

The results of the correlation analysis of suspended sediment concentration showed that the best result for the Doab and Polchehr stations belonged to band 4 (R2 = 0.86) and band 5 (R2 = 0.83), respectively. The suspended sediment concentrations varied from 0.17 to 76.45 and 0.44 to 118.86 mg/liter in the Doab Mereg and Polchehr stations, respectively. In the Doab station, the depth of the power state had the highest correlation coefficient between the observational data (recorded in the station) and the data extracted from the images. In the Polchehr station, it had a high correlation coefficient in both polynomial and exponential modes. The best values of the coefficient of determination (R2) of the normalized vegetation cover difference index for the Doab and Polchehr stations were 0.98 and 0.64, respectively. This means that the amount of sediment decreases with an increase in vegetation cover. The average values of the vegetation index for the Mereg watershed (0.35) and the Gamasiab watershed (0.28) show the relatively sparse vegetation in the area. The lowest average values of vegetation cover in the studied season (spring) were respectively equal to 0.11 and 0.21 in the Mereg watershed in June and the Gamasiab watershed at the end of May. The results of the regression test showed a strong and significant relationship between the density of vegetation and the amount of suspended sediment concentration recorded in the hydrometric stations of the two basins.

The results showed that six models were extracted for the studied area, which had acceptable and suitable R2 and error values. Among the obtained models, better results were obtained in the single-band model than in the case of using the band ratio. The highest correlations belonged to bands B2, B3, B4, and B5 in the Doab Mereg station, and to B4 and B5 in the Pol Cheher station. The highest R2 values obtained for the two stations were 0.86 and 0.83, respectively, in the exponential model. The results of this study show that Sentinel-2 can be used as a suitable tool to estimate suspended sediment concentrations with acceptable accuracy in small-scale basins and flood conditions, which has been confirmed in a number of similar studies. Sediment rate refers to the positive effect of vegetation on soil protection and the reduction of sediment production and transport within watersheds. In general, the results demonstrate that vegetation has been effective in the quantity and quality of the spatial changes in the sedimentation rate of the basins. In fact, the NDVI vegetation index, as a representative of the vegetation, can be successfully used to create a statistical model of the changes in the sedimentation rate. The revitalization of vegetation should be included in development plans.

The distribution of organisms and ecosystems is affected by climate change, in fact, climate change has affected the distribution patterns of a large number of species and even led to displacement, extinction of certain species, destruction and loss of habitats. In this regard, remote sensing technology, due to having a spatial and integrated view of the region, as well as geographic information systems (GIS) due to the ability to analyze and analyze, prepare maps and model, can be efficient in the field of monitoring, planning and dynamic and sustainable management of wildlife habitats. The purpose of this research is to investigate the weather-climate changes in Khabar National Park located in the south of Kerman province using remote sensing technology and Landsat satellite thermal bands and geographic information systems (GIS). Therefore, first, Landsat satellite images and sensors (TM-ETM-TIRS) were prepared in the years 1972 to 2022, then pre-processed, processed images and maps of surface temperature changes (LST) in four seasons and for the years 1988-2000-2010-2014- 2020-2022 has been prepared. Landsat satellite and NDVI index have also been used to monitor vegetation changes. The results of the research showed an increase in the temperature of the earth's surface in the last 40 years and in the 4 investigated seasons. Also, the results of analysis and monitoring of vegetation cover (NDVI) showed a decrease in the average vegetation cover of the region in the studied years. Also, in order to investigate the relationship between vegetation percentage and LST changes, linear regression and SPSS software were used, and R=99% and R2=98% and R2=97% were calculated, which is acceptable and shows that with the reduction of vegetation area, the surface temperature has increased. According to the findings, what is evident is that the trend of climate change and the relative increase in the temperature of the earth's surface and the decrease in vegetation in the study area continues and requires strategic planning and systematic management of the Khabar wildlife habitat in order to maintain the indicators of habitat desirability and the balance and parallelism of ecological relations. And protecting the rich fauna and flora diversity of the region.

Extended AbstractIntroductionAccelerated soil erosion and severe sediment production disrupt the natural balance in watersheds and have off-site impacts on river channels and downstream reservoirs. There are different estimates of soil erosion and sediment yield in Iran. However, what the researchers agree on is that it is more than tolerable erosion. Rangelands cover more than half of Iran, and poorly covered lands play a large role in flood and sediment production. Therefore, the study of hydrology and soil erosion is necessary for sustainable use of pastures. The prerequisite for these studies is long-term monitoring of sediment, runoff, vegetation, soil, etc. A careful review of previous studies on rangeland hydrology shows that most studies have focused on the effect of grazing management at the plot scale or have used simulation approaches and experimental models, as well as in a short period of time. Therefore, it is very necessary to conduct scientific research using long-term monitoring data in experimental watersheds. In this regard, the present study was proposed with the aim of evaluating the sediment yield of small watersheds in pastures with grazing exclusion vs. overgrazing. The results of this research can provide useful information to researchers, promoters, planners, and ranchers.Materials and methodsSanganeh Soil Conservation Research Station (SSCRS) with an area of 30 ha was established about 25 years ago in Kalat County (Razavi Khorasan Province, Iran). In addition to measuring erosion in plots and monitoring vegetation, by building several ponds at the outlets of six small watersheds (SWs), their runoff and sediment yields have also been recorded since 2006. The present study was conducted with the aim of evaluating the erosion and sediment production of these SWs (1200 to 17000 m2). For this purpose, the runoff and sediment of 69 events were collected at the outlets of six SWs. Also, the time series of NDVIs were calculated for SSCRS, adjacent rural and nomadic (outside the village) rangelands on a seasonal scale. To determine the time series of NDVIs, all satellite images of the study area were downloaded and after pre-processing and corrections, the images were processed. Then, the soil erosion amounts of SWs were estimated in terms of the sediment delivery ratio of the study area (Noor, 2020) based on the average sediment yields (according to their long period of 15 years). In the next step, the amounts of soil erosion obtained were compared to the amounts of tolerable soil erosion for arid climate rangelands proposed by Skouti Oskouei and Arabkhedri (2018). Finally, the soil erosion of two similar watersheds, one in the overgrazing area (E6) and the other in the grazing exclusion area (E4), were compared.Results and DiscussionThe results showed that NDVI is influenced by livestock grazing intensity. The highest value of this index was observed in the grazing exclusion area (E1 to E5 SWs), then in the Sanganeh village rangeland (E6 SW), and the lowest value was observed in the nomadic rangelands. In terms of time scale, the biggest difference in NDVIs between overgrazing and grazing exclusion areas was observed in spring. The results of the investigation of sediment production indicated an inverse and non-linear relationship between the specific sediment yield and the area of SWs. This study showed that the amount of soil erosions in E2, E3 and E6 SWs are more than tolerable erosions which suggests the need for more conservation measures. Finally, the comparison of two similar SWs (E4 vs. E6) indicate a significant reduction in annual sediment yield (582%) due to grazing exclusion in the area. Also, the results showed that the sediment productions of E6 SW in the spring and autumn seasons are significantly higher than E4 at 1% and 5% levels, respectively. Furthermore, it is remarkably (but non significant) higher than than the E4 SW in the winter season.ConclusionGrazing exclusion in SSCRS rangeland led to a significant reduction in erosion and sediment production compared to overgrazing condition outside the station. However, in some SWs, the erosion amounts were still more than the tolerable values, which indicates the difficulty of restoration of destroyed rangelands on steep slopes with sensitive formations in arid climate. Implementation of management measures including scientific grazing (especially in spring) is necessary to reducing damage caused by floods and sediments.

Extended AbstractIntroductionMapping and assessment of surface water dynamics is essential for continuous monitoring of water resources as it has significant implications in engineering and scientific research for floodplain delineation, wetlands, disaster management, biodiversity, climate change, and water resource management (Huang et al., 2018; Jawak et al., 2015). Traditional surface water monitoring methods mainly rely on field surveys or on established measuring stations. Although the accuracy of the data obtained by this method is high, it is time-consuming and has low efficiency. In addition, many aquifers are very difficult to access because they are located in remote and rugged places, and only data from limited points in incomplete time series are obtained due to the limitations of economic and land factors (Ogilvie et al., 2018). With the expansion and development of remote sensing science and geographic information system, better ways have been provided to monitor water bodies in a long period such that the use of multispectral satellite images and different spectral indices for monitoring water bodies and floods is a fast and economical method in terms of time and cost. The high number of existing sensors and their differences in estimating the spectral and spatial characteristics of spectral indices have led to a good representation of the potential and limitations of satellite data. Therefore, in this research, to investigate the flood of 2019 in the southwest of Iran, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Diference Vegetation Index (NDVI) obtained from the Landsat 8 satellite images were used.Materials and MethodsThe study area is a part of the southwest of Iran, which includes the southern part of Ilam province and the northern, northwestern, western and southwestern parts of Khuzestan province. In this study, to investigate the flood of 2018-2019, Landsat 8 sensor images were used for three months of January, February and March in 2019. This section used QGis3.28, GIS10.8, Excel software and remote sensing data including satellite images related to Landsat 8 sensor. These multispectral data were obtained from the United States Geology website (earthexplorer.usgs.gov) and were prepared for preprocessing and necessary processing. To prepare a map of the flood area and vegetation, radiometric and atmospheric corrections were performed on the received images (Eskandari Damaneh et al., 2016). After applying the necessary pre-processing, the MNDWI and NDVI were used to prepare the map of changes in water bodies and vegetation for the years 2018 and 2019, respectively (Rugel et al., 2017; Abutaleb et al., 2015; Arekhi et al., 2019).ResultsAccording to the results, the highest values of MNDWI in 2018 corresponded to those on March 12, which includes the northern and central parts. In 2019, the highest value of this index was on the date of March 19, which included the southern to southwestern parts of the study area. Examining the changes in MNDWI classes showed that in 2019, on February 2nd, 19th and March 7th, classes ranging from 0.11 to 0.15 occupied the highest level of the studied range, which is more than 68.75 percent of the area. This range and its trend were increasing. On the other hand, on the mentioned dates, classes ranging from 0.15 to 0.2 covered an area of more than 24.32% of the region, and these classes were decreasing. Accordingly, in 2019, on March 8th and May 14th, the largest percentage of the area under study was still in classes ranging from 0.11 to 0.15, and the total of these areas was more than 76.13% of the region, which was decreasing. On these dates, classes ranging from 0.15 to 0.2 included more than 20.03 percent of the study area, and these classes had gone through an increasing trend.Examining the changes of NDVI classes showed that in 2019, on February 2nd, 19th and March 7th, the largest percentage of the study area was taken by classes ranging from 0.2 to 0.3, which totaled 71.81%, and was increasing. Also, on these dates, classes ranging from 0.3 to 0.5 included more than 11.46% of the study area and these classes were decreasing. Also, in 2019, on March 8th and May 14th, the largest percentage of the study area was still taken by classes ranging from 0.2 to 0.3, and the total of these areas was more than 82.54%, which was decreasing. Meanwhile, on these dates classes ranging from 0.3 to 0.5 included more than 11.64% of the study area, and these classes had been increasing.Discussion and conclusionThe trend of spatial and temporal changes of the MNDWI index in this period shows that in February and March of 2019, the highest value of this index was in the northern and central parts of the studied region. While the highest value of this index was in March and May of 2019, it has been seen in the southern and southwestern parts of Khuzestan province. While this precipitation is in the season when the vegetation in this area is in good condition, the classes above 0.3 NDVI vegetation index in March 2018 and March 2019 are more than 42 and 38% of the area, respectively. Because the southern parts of Khuzestan province have lower altitudes than the northern and northwestern regions, it is plain and flat. This has caused it to serve as the foothills of the upper elevations of Khuzestan and Lorestan provinces, which in turn causes the influx of upstream waters into this region. Even if the vegetation cover is suitable in the season, it has caused a large and unexpected influx of water in these areas, which itself causes flooding of the residential regions, facilities and agricultural lands. In general, it can be concluded that by using the MNDWI and NDVI indices obtained from the Landsat satellite images, it is possible to monitor the water bodies and waterlogged areas resulting from natural hazards such as floods, as well as the vegetation of different regions with high accuracy. The findings of this study can be used in studies and decision making with sufficient confidence.

Soil is one of the most important natural resources of any country, which plays a key role in food security, self-sufficiency in food production, national economy, and sustainable agriculture. Soil erosion is one of the most obvious factors of soil loss, and rain erosion is one of the most important forms of erosion. Therefore, the knowledge of the processes governing soil erosion and sediment transport is very important in water and soil resources management, as well as, the development of soil erosion models to achieve sustainable development is of great importance. Previous research has shown that rainfall patterns are one of the factors influencing rain erosion. Vegetation also reduces soil erosion by protecting the soil against the effects of raindrops and runoff. Rain erosion is especially important in arid and semi-arid areas due to the lack of vegetation and low initial soil moisture. This research was conducted, regarding the effect of rainfall patterns on rain erosion, by investigating the rainfall pattern and vegetation changes over 25 years in Ebrahim Abad and Royan watersheds situated in Semnan City. 

First, the physical characteristics of the watersheds were obtained; using ArcGIS software, and the precipitation information was extracted from the rain gauge sheets with an accuracy of 10 minutes. To compare the rainfall for different amounts of precipitation, the dimensionless cumulative rainfall curve of each event was obtained. The time of each rainfall was divided into 10 parts and the percentage of rainfall was determined for each part. The rainfall curve was divided into 4 quartiles (1st, 2nd, 3rd, and 4th quartiles) depending on the occurrence of the maximum precipitation. According to the information on the sediment layers in check dams located at the outlet of each watershed and the precipitation data, the storm-related to each sediment layer was determined and the effect of the storm pattern on the sediment pattern was investigated. To check the similarity of precipitation and sedimentation patterns in check dams, the average difference in precipitation and sedimentation in each time step and standard deviation changes were used. Considering the dynamic changes of vegetation compared to other characteristics of the watershed, remote sensing data were used to investigate the changes in vegetation and its area. Due to the effective performance and high accuracy of NDVI index and landsat satellite images in dry areas, Google Earth Engine system was used to estimate vegetation cover, manage and recall the satellite images. Then, the influence of watershed characteristics such as slope, area, soil type, shape factor, and vegetation cover on watershed sedimentation was investigated. 

The average similarities in precipitation and sediment pattern in Ebrahim Abad and Royan watersheds were 48.2 and 46.1%, respectively. Also, the percentage of coarse-grained sediments augments by increasing the precipitation quarter number, during each storm event, which shows the important role of the rainfall pattern on the sedimentation pattern in each watershed. The average monthly vegetation cover (obtained from Landsat images) in Ebrahim Abad and Royan watersheds during the mentioned period was 5.15 and 4.99%, respectively, which is less estimated than reported by previous research. In this research, a threshold limit of 0.1 has been used for the NDVI index, in which very weak vegetation has been omitted.

In both watersheds, in more than 51% of cases, by increasing vegetation cover in each storm event, the thickness of the corresponding sediment layer augments, which shows the effect of vegetation cover on the erosion and sedimentation of the watersheds.

The aim of this study is to detect and quantify the cultivated area of potato fields in Hamadan Province using remote sensing methods and a time series of satellite photos. As a result, Awifs time-series imaging was used to determine the potato cropping area. For this purpose, pictures were taken at three different times when the potato plant turned green and yellow. Processing such as preparation, atmospheric and geometric correction, vegetation index, and unsupervised classification were performed on the images using appropriate training sites for supervised classification. Following the integration of these two layers, the studied area under the cropping map was prepared using the phase classification method. Additionally, by using the vegetation indices NDVI and SAVI, the area under cropping for the three main crop yields is determined first using the threshold level technique and in three temporal intervals. The kapa coefficient for potato under cropping area determined by phase classification, NDVI, and SAVI was 90, 87, and 85%, respectively. In 1998, the potato cropping area was determined to be 38740, 36728, and 36614 acres, respectively. This study clearly shows that the phase classification method and Awif data time series can be used to recognize and estimate potato under cropping area with acceptable precision and that vegetation indices distinguish potato under cropping area faster.

The past decade has seen a major revolution in vegetation monitoring using satellite imagery, resulting in quantitative indicators of vegetation with a professional processor in a web-based interactive development environment. In this study, using MOD13A1 and MOD13Q1 products of Modis sensor, the trend of temporal and spatial changes of NDVI and EVI indices in Fars province in a period of 16 days from 2000 to 2020 was coded and processed monthly in Google Earth engine system. The results of this study showed that the average index of NDVI index is from minimum 0.11 to maximum 0.495 and the average index of EVI index is 0.1. According to the results obtained in this survey, in all the years from 2000 to 2020 in January, NDVI and EVI values had the highest values compared to other months, so that in January 2019 and January 2020, the highest EVI values averaged 0.22 and the NDVI values Was estimated to be 0.18. The lowest monthly average values of both indices occurred between 2000 and 2005, which indicates that the vegetation has been severely degraded during these years. The results of spatial changes using EVI index showed that the level of vegetation in Fars province in different months varied from 10,000 square kilometers to 22,000 square kilometers and from the perspective of NDVI index from 15,000 square kilometers to 30,000 square kilometers.

The purpose of this study was to investigate the oak forest dieback with respect to drought occurrence, soil moisture changes and dust occurrences factors in Ilam, Kermanshah, Lorestan and Chaharmahal and Bakhtiari provinces of Iran. The data used were field surveys collected through GPS, MODIS satellite imagery, GLDAS Soil Moisture, dust and precipitation data of the meteorological stations of the provinces during an 18-years period (2000-2017). The results of the study of greenness values of the forests in the study area showed that the first decline occurred in 2005 and repeated more severely with much wider spatial extent in 2008. Investigation of the relationship between drought events and its spatial and temporal variations with the changes in forests greenness of the study area showed that the reduction in precipitation amount is one of the main reasons for forest greenness reduction in the study area. The increased frequency of periods of rainfall shortage and drought duration, especially at 9 and 12-month time scales, showed a significant relationship between drought occurrences and forests greenness in the study area. The results indicated that by decreasing precipitation drought periods increased, soil moisture decreased, and dust storm occurrences increased. As a result, in most of the years, with decreasing soil moisture and increasing dust storms, the forests greenness of the study area has decreased and vis versa. Therefore, there is a direct relationship between soil moisture and forest greenness while an inverse relationship exists between dust and forest greenness.

As a complex phenomenon occurring due to a long period of poor precipitation, drought causes water scarcity in the soil and the hydrological system via hydrological, bringing about long-term consequences which may lead to severe economic, environmental, and social problems worldwide. Droughts are classified into four types: meteorological (rainfall), agricultural (soil’s moisture), hydrological (flow and groundwater), and socio-economic droughts. It should be noted that all types of droughts originate from meteorological ones, bringing about social and economic damages. on the other hand, Iran has long been known as a dry country with a low annual precipitation rate. Moreover, the development of spatial and satellite sciences, the application of remote sensing techniques, and GIS together with previous methods have led to more accurate results. Therefore, investigating the relationships between satellite-derived indicators and meteorological droughts could improve our understanding of the response of such indicators to climate change.

This study sought to investigate the relationship between remote sensing indices and the drought index (SPI). To this end, first, the daily precipitation statistics of 103 meteorological stations located in the study area were collected from the Iranian Meteorological Organization’s website for the 2000- 2019 period and their quality was controlled. Then, the SPI drought index was measured for all such data on monthly and annual scales using the McKay method, according to which the total annual precipitation was compared to the fit of different distributions. Then the probability of the observations’ experimental occurrence was calculated based on the relationship between the aforementioned equations. Moreover, after measuring the SPI index in the stations located in the study area, the MOD021KM outputs of the Terra sensor for the 2000-2019 period were obtained from the USGS website, to which necessary geometric, radiometric, and atmospheric corrections were applied. Finally, the NDVI, VCI, TCI, VHI, DDI, NDDI, EVI, NDWI, and SAVI indices were used to assess the drought conditions, and a monthly time series database was created for each of the indices over the past 20 years.

The study’s findings indicated the acceptable efficiency of remote sensing indices in extracting the data required for analyzing the drought in Central and South Zagros via the SPI index. The drought-related data and indicators of the study area were obtained and identified using the KMO and Bartlett index, whose range varies from zero to one. Accordingly, in cases where the index value is close to one, the collected data are suitable for factor analysis; otherwise, the results of factor analysis would be not appropriate for the intended data. In this regard, the results of Bartlett's statistics also suggested that the remote sensing indices affecting the drought in the study area were suitable for factor analysis, indicating that the confirmation of the opposite hypothesis, according to which there was a significant correlation between the variables.

This study sought to investigate nine remote sensing indices affecting the analysis of drought in Central and South Zagros. The results of factor analysis revealed that the drought in the study area was affected by three factors. In the first factor, SAVI, NDVI, VCI, and EVI indices showed the highest correlation. In the second factor, NDVI, DDI, TCI, and VHI were of great significance, and in the third factor, the NDDI index played the most important role. In the next phase, the relationship between the spatial pattern of remote sensing indices and drought’s spatial clusters was examined. Spatial relations analysis is a method to spatially analyze the randomness and non-randomness of the distribution of spatial variables. Furthermore, spatial autocorrelation is one of the most practical and important analytical tools for researching spatial relationships. In this regard, it was found that remote sensing indices and SPI drought index possessed a positive spatial autocorrelation in terms of spatial relationship in eastern, southern, and some parts of the central regions of the study area. Therefore, it could be argued that the areas were more affected by drought in terms of spatial autocorrelation index G *. On the other hand, out of the indices investigated in this study, SAVI and NDVI had the most similarity in terms of regions with positive cluster patterns, possessing the most positive spatial patterns. The results of the impact factor analysis performed on the correlation between the SPI and remote sensing coefficient confirmed that the spatial distribution of drought was of cluster type.

The purpose of this article is to study and analyze the land use dynamics and its impact on the heat islands of the city. This research was conducted for the city of Mashhad and a 10 km wide strip around it with the help of Landsat satellite images in the period 1988 to 2018. The process used includes supervised classification of images, investigation of land use changes during the mentioned period and investigation of changes in land surface temperature (LST) and vegetation density (NDVI) was in land uses and their relationship with land use. The results showed that by 2018, the area of ​​agricultural-garden use decreased by 9.38%, the area of ​​urban and residential areas increased by 12.19%, urban green space increased by 2.11% and other areas increased by 4.93%. The decrease in the level of agricultural-garden areas has been due to their conversion into urban and residential areas. It was also found that changes in man-made land use have led to changes in land surface temperature (LST). An increase in surface temperature and a decrease in the difference between the average temperature of urban areas and areas around the city was observed, which indicates the expansion of the urban heat island phenomenon in the city of Mashhad. The results of this study showed that the use of multi-temporal satellite data and the combination of common methods for studying thermal islands (study of temporal-spatial distribution changes of urban thermal islands compared to changes in urban development, the relationship of thermal islands with vegetation and land use and coverage Lands) can provide a comprehensive view of how urban thermal islands are formed and expanded.

Arid and semi-arid regions with highly variable rainfall and periods of drought or unpredictable floods have severely affected residents' water shortages and they are often living in insecure areas. Rainwater catchment systems are one of the available solutions to overcome water shortage and runoff management in these areas. Surface runoff is a potential source of water that, with proper management and rainwater extraction methods, can be used to meet demand in various sectors, including agriculture and drinking. This study was conducted with the aim of introducing suitable places for establishing rainwater catchment systems in Latyan Watershed. Essential maps of the watershed for this study included land cover coefficient, permeability of formations and soil, average long-term rainfall, slope, land use, and drainage network were prepared. The mentioned maps were each divided into five categories and by overlaying in ARC/GIS 10.7.2 environment, finally suitable places for runoff production of the watershed were obtained to create catchment levels. The results showed that slope percentage, type of land use, NDVI and climate in this watershed were effective factors for runoff production. In addition, the map of runoff production potential classes showed that the frequency percentages related to runoff production potential classes were very high, high, medium, and low, respectively 3.2, 22, 33.6 and 23.5%. For the construction of these systems, priority is given to areas that have the potential and frequency of upper class in terms of runoff production, which areas with medium runoff production potential have this feature. Introducing suitable places to establish rainwater catchment systems can maximize the probability of success of rainwater harvesting projects.

Due to the importance of vegetation cover in these areas, the aim of this study was to investigate the effect of drought, on vegetation of HablehRood watershed.Initially, NDVI index obtained from MODIS sensor was used to study vegetation cover and then SPI index based on rainfall data of two basins in two arid and semi-humid climates was used for drought assessment (2001-2018) using image processing methods. The results showed that during this 18-year period, 53% of the region had droughts on average. Also during the period 2001-2003, drought was more severe than other periods (2003-2018). In addition, the highest vegetation index occurred in 2005, indicating that vegetation was affected by rainfall fluctuations in the region. The correlation matrix between the three indices indicated that NDVI had the same correlation with SPI and annual rainfall. The results of this correlation in dry and semi-humid climates showed that the correlation was 0.38 and 0.25, respectively. These results indicate that this relationship is positive and robust in different climates of a region؟. On the other hand, drought class is mainly located in dry and semi-humid climates, with 55.55% and 50% in relatively normal drought class, respectively. Based on the above, it can be concluded that using remote sensing data can monitor the response of semi-humid and dry arid ecosystems to climate change. The study also showed that arid and semi-arid regions are highly susceptible to climate change and human anomalies. Therefore, the destruction of these lands will have many environmental and economic consequences.

Vegetation is one of the most important key components in arid regions for reducing of the effects of erosion and determining the severity of desertification. Decrease in vegetation leads to increase in surface albedo. Accessing and preparing desertification intensity map at the fastest possible time and at the lowest cost is one of the concerns of governments. In the present study, in order to identify the best vegetation index for preparing the desertification intensity map, MSIL-1C data of Sentinel 2 satellite in the arid region of Sistan has been used. For this purpose, the relationship between surface albedo and each of the different vegetation indices of the NDVI, RVI, DVI, PVI, SAVI and TSAVI were conducted. After determining the linear regression equation between the albedo and each of the mentioned indices, the relevant desertification intensity equation was calculated and the desertification intensity map of the studied area at 5 classes was prepared based on albedo and each of the mentioned indices. The results showed the strongest relationship in the study area was between albedo and NDVI, with a correlation coefficient of 0.63, and the lowest correlation of 0.37 was between the albedo and PVI indices. Based on the present study among the indices studied, the NDVI is the best for the preparation of maps of desertification intensity in the arid region of Sistan. Based on this index, 20.3% of the region was classified as severe and 32.9% of the region grouped into the moderate desertification class.

Plain ecosystem is highly vulnerable to environmental changes, and drought is the most famous ecosystem change driver that is difficult to identify after its occurrence. In this research, to study the slope of vegetation changes against drought, the NDVI index of MODIS images and the SPI index from 2001 to 2016 were used and the map of vegetation changes against drought with five drought stress classes included very low classes, Low, moderate, high and very high, so that a suitable assessment of the drought can be made at specified time scales. The results of slope pattern of spatial change of vegetation against drought showed that across the plain vegetation changes have declined, and from east to west of Qazvin plain, the slope of vegetation changes and land susceptibility to drought have been reduced. So that the most percentage of area in a one-month drought related to the drought class is very low, but in droughts of 3, 6, 9, 12, 24 and 48 months, the highest percent of the area belonged to moderate and high drought classes. The results of this study, the determination of the level of vegetation changes in against drought in the past years and prediction of these changes in the future years, can be used in the planning and optimal use of resources, control changes in the future.