جستجوی مقالات مرتبط با کلیدواژه « NDVI » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

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 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.

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.

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.

 Vegetation is one of the most important criteria affecting the ecological life of the environment, which is directly related to the moisture conditions of watersheds. Estimation of plant drought indices such as VCI, TCI, VHI is a effective technique to assess the effects of drought on the ecological life of the environment, which are obtained by calculating NDVI. In this regard, several studies have been conducted that explain how to calculate and use these indicators. According to the results of studies related to the importance of using plant and climatic drought indicators, the purpose of this study was to investigate the effect of drought on vegetation in Kashkan basin of Lorestan using landsat satellite images, rainfall data and satellite imagery.

Rainfall data of Kashkan basin stations during the statistical period of 1999 to 2019 were collected from the Meteorological Organization. Data related to satellite images of multi-time images Landsat for the years 1999 and 2019 from ETM and OLI sensors (L Needs 7 and 8) were obtained from the USGS database. Finally, using the SPI index, dry and wet years were distinguished during the statistical period and were selected as representatives of dry and wet years in 1999 and 2019. And calculated vegetation indices (NDVI, TCI, VCI and VHI) using the ENVI 5/3 software. Satellite data were obtained from Landsat multi-time images for the years 17/2/1999 and 15/2/2019 with ETM + and OLI sensors (Landsat 7 and 8) from the USGS database. Because the area is located at different passes 165 and 166, the Seamless Mosaic operator in ENVI 5.3 software was used to mosaic the images. Data with L1T level corrections (including systematic radiometric correction, geometric correction using ground control points, and topographic displacement error correction) were obtained from the USGS database. A 1: 25000 topographic map of the study area was used to control the geometric correction of satellite images. Atmospheric corrections of the images were performed by applying flash algorithm in the software and the images were prepared to extract the research indicators.

  After preparing the data, the classification map of NDVI, VCI, TCI and VHI indices in 1999 and 2019 was determined. Based on the obtained results, in 1999, the amount of poor vegetation was the highest with an area of ​​4869.7 square kilometers, equivalent to 52.49 percent of the area, and the highest vegetation belonged to the good vegetation class with an area of ​​3311.44 square kilometers, equivalent to 69 Percentage of the area. The results of the VCI index show that the vegetation situation was good in 1999 and 4749.1 square kilometers of the area have experienced drought-free conditions. In addition, 4034.9, 111.3 and 83.5 square kilometers of the region had mild drought, severe drought and very severe drought, respectively. In 2019, according to the VCI index, 7531.8 square kilometers of the basin, ie 81.2% of the area, have vegetation-free conditions in terms of vegetation. According to the TCI results in 1999, limited parts of the western and southwestern parts of the basin with an area of ​​742.1 and 278.3 square kilometers had severe and very severe drought conditions, respectively. In 2019, in terms of heat, in 3005 square kilometers of the area in the northern, central and southeastern parts of the basin, no drought conditions were observed. According to the VHI results, in 1999, about 102 and 1159 square kilometers of the area had severe and moderate drought conditions, respectively, and no severe drought occurred in the area. In 2019, there was no severe drought. In general, the situation has been very good for vegetation

 The results showed that drought significantly reduced the density of vegetation in basin, and the existing vegetation was affected by drought stress. According to the VHI index, which indicates the health status of plant coverings affected by drought, two VCI and TCI index. In 1999, the eastern and western regions and the southwest had moderate to severe droughts, and most of regions had a mild drought. In 2019, due to the health of vegetation, in most areas, vegetation was under drought-free conditions and did not suffer from drought stress, but some parts also suffered from mild drought.

Todays, with advances in satellite sensors, their role on obtaining information and study of the environmental events and phenomena have become increasingly important. One of these precious data which is the basis for many planning and decisions in rangelands, is the above ground net primary production. The traditional method of estimating ANPP is clipping and weighing. The high cost, time and difficulty in collecting are the limitations of the traditional method, which makes it extremely difficult and expensive to estimate the ANPP of large areas. Thus, the aim of this study was to estimate above ground net primary production (ANPP) using vegetation indices.

Sampling of vegetation was performed in rangelands of Sabalan elevations in Ardabil province and altitude ranges from 1500 to 3300 meters in 2016. Nine sites were selected in study area and in each site three 100 m transects was placed with 50 m interval. Along each transect 5 plots (1 square meter) with 20 m from each other was placed and in each plot, total and life forms including shrubs, grasses and forbs ANPP were measured. The initial net production samples were placed in an oven at 70 ° C for 24 hours and then weighed to determine their dry weight. Furthermore, veg/etation indices including NDVI, PVI3, RVI and SAVI were calculated using data of Landsat 8 Operational Land Imager (OLI) images for 2015. Due to the fact that the maximum growth of vegetation in the region is in June, the images were selected for June. Then using general additive model in software R, curve of relationship between ANPP and vegetation was analyzed in two way Individual and Combined data. Finally, ANPP was modeled using non-linear regression.

The application of generalized additive models for each of the vegetation indices with total ANPP and life forms separately shows that NDVI, PVI3 and RVI have a nonlinear relationship with total ANPP and life forms. However, the SAVI index has a linear relationship with the ANPP of total and grasses. Also, all vegetation indices have a significant relationship with ANPP. The ranking of vegetation indices affecting the ANPP based on the coefficient of determination shows that the most important and least important plant indices are SAVI and PVI3 for shrubs, and PVI3 and SAVI for grasses, forbs and total ANPP. In the present study, contrary to expectations, NDVI vegetation index, which has many applications in the vegetation studies and its increase indicates the presence of more vegetation in the region, in the generalized additive model and the combined study of vegetation index and ANPP show a nonsignificant relationship. Results showed that nonlinear regression significantly increased the accuracy of ANPP estimation using vegetation indices. The coefficient of determination for total ANPP (0.80) is more than shrubs (0.74), grasses (0.75), and forbs (0.56) and among the life forms, forbs have the lowest coefficient of determination.

Based on the results, suitable vegetation index for estimate the ANPP of life forms are different. Also, ANPP estimation using vegetative indices at the total level is more accurate than life forms. According to the results, the OLI image and the use of nonlinear regression models were able to adequately estimate the ANPP in the study area. Therefore, if similar results are obtained in other areas of Ardabil, it will be possible to generalize the results and estimate this important ecological indicator with less time and cost. This tool can also be used to provide information on the amount of forage production and thus determine the capacity and amount of livestock, as well as the amount of pasture degradation.

Snow is one of the effective fators on vegetation rate and function in mountainous areas. The aim of this study was to investigate the impact of snow cover area index and snow cover duration index on two declining and dominant plant species including Astragalus adscendens and Quercus brantii in Chaharmal and Bakhtiari Province from 2003 to 2016. For this purpose, Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite images and compared using Pearson analysis in forest decline regions (Barz, Seveh, Helen and control area) and in rangeland decline regions including Astragalus adscendens decline and control areas. Results showed that about 32% of the snow cover in the study area has been reduced, although a constant trend was not observed. The studied snow indices showed the highest relationships with rangeland and forest vegetation cover in March. The decline region of Astragalus adscendens had the maximum relationship with the snow cover area (R>0.70) and snow cover duration (R>0.71) in March. According to the findings, snow precipitation in late winter season and its duration is more effective on the rangeland Astragalus adscendens species than forest vegetation cover and this pecies can be more sensitive to decline in case of snow reduction.

Land use change is one of key factors in global environmental change, which affects a wide range of environmental characteristics and natural resources (processes and functions of ecosystems and climate systems, water quality, land and air resources). Excessive destruction and exploitation of forests and pastures, inadequate cultivation patterns, land degradation due to population growth and lack of proper management of natural environments have endangered nature and destructed habitats. Planning in this respect demands a clear understanding of how the land should be used and the type of activity done in each part of it in order to obtain accurate information about different communities and terrestrial phenomena. Also, it is difficult to access information to evaluate land degradation and land use change on both global and regional scale. One of the most important ways to determine land use change is satellite imagery. Because protected areas play an important role and are the core of biodiversity conservation, their management and conservation require further field research. Therefore, having information about land use and its changes over time is one of the important issues in planning and policy-making in this region. Therefore, the present study was conducted to predict and evaluate the rate of natural land degradation by emphasizing land use change in the Dinarkoh protected area.

Dinarkoh forest protected area is one of the mountainous areas of Ilam province. This study collected the required information about the study area and similar studies using library and field studies. Then, satellite images were extracted for data analysis and software methods were used to process them. In this way, after examining the geometric and atmospheric errors in the images used, using the maximum probability method, a map of land use classes related to the period 2001-2020 was created. Then, using two post-classification comparison methods, the difference in vegetation index (NDVI) was used to detect and study land use changes in the study period.

The results of evaluating the accuracy of the map obtained from the classification with the maximum probability algorithm on TM and OLI images showed that the accuracy of each of the TM and OLI classified images in the supervised classification method with the maximum probability method is high and acceptable. Considering the overall accuracy of 92.09% in TM image classification and 92.66% in OLI image, it can be said that these two images can prepare the desired area map at any time and can be used to identify changes. The results showed that factors such as similarity of the reflection of some lands which causes an inaccurate segregation of classes reduce the accuracy and are classified as a source of error. Therefore, in general, it can be said that according to the accuracy obtained from TM and OLI images, these two sensors can detect changes. The results showed that forest exploitation and a sharp increase in the population of villages and cities and their need for housing and economic factors have reduced the level of forest. Comparison of the classification maps obtained from the methods used in this research with the ground reality map showed that these methods have different accuracy in identifying areas of change and non-change. The results of satellite image processing showed that in 2001, compared to 2020, more than 6000 ha of forest lands in the region during the 19-year period have been converted into non-forest lands such as agriculture and rangeland, which indicates poor protection of the region.

The results showed that 8000 ha lands in the study area in terms of NDVI index had a decrease in vegetation cover, which indicates the trend of degradation in the region and the need for more serious conservation measures.

The main objective of the present study is to delineate all-natural resources of Iran with the priority of desert and semi-desert areas using indicators and criteria extracted from remote sensing data and new satellite image classification techniques. Accordingly, desert, semi-desert, and salinity areas of Iran in conjunction with other natural resources areas (such as forests, rangelands, water bodies, and farmlands) were studied using time-series MODIS satellite images and different indices and parameters such as Albedo, NDVI, and surface temperature during day and night along with the temperature difference between day and night. Here, unlike the classical classification methods, based on using one-single satellite image and features such as vegetation density or surface temperature, the behavior of different natural resources over time, extracted from satellite images, was analyzed. Accordingly, the temporal behavior of each of the mentioned natural resource areas during 2019 was studied and analyzed using the remote sensing criteria. The basic object classification method was used to classify the country using the mentioned indicators using the least distance technique based on fuzzy logic. Based on results, about 41.2%, 14.8%, and 3.9% of Iran (totally about 60% of Iran) are classified as desert, semi-desert, and salty areas, respectively. By considering the percentage of rocky mountainous areas (11.3%), about 71.2% of Iran has no biological conditions (unsuitable for agricultural activities). As expected, desert and semi-desert areas are concentrated in central, eastern, southeastern, and southern regions of Iran, and no signs of such areas are found in the northern, northwestern, and western of the country.

Spatial mapping of evapotranspiration (ET) rates can indirectly be very important for rangeland management and rehabilitation. The map of evapotranspiration can be an indicator of plant water requirement. Mapping of ET from remote sensing data is more appropriate than field methods, because the spatial and temporal coverage of this method is very extend and time and cost are optimized. Among the evapotranspiration mapping methods using remote sensing data, the FAO-56 model is one of the usual ones. Therefore, this study aimed to spatially estimate the amount of ET in semi-arid region of Isfahan province using the integration of FAO-56 and remote sensing techniques. To calculate ET, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were obtained from geometrically corrected TM 2009 image and the standard crop evapotranspiration (ET0) was calculated using FAO-56 equation and then the crop coefficient (Kc), crop transpiration (Kcb) and soil evaporation (Ke) were extracted from NDVI data. Results showed that agricultural lands with 6/977 mm/day and bare ground with 0.012 mm/day had the highest and lowest ET in the study area. High relationships of ET map with well-known remote sensing maps such a NDVI (>99%) and LST (>92%) indicated that ET mapping in the region has been achieved with a high accuracy, therefore, it is a valuable supplementary technique to field methods in determining plant water requirements for rehabilitation purposes.

Surface temperature is an important management parameter at the time of planning in various environmental sciences including natural resources. But the uneven distribution and small number of terrestrial data stations is the incentive of using remote sensing data. The purpose of the present study was to investigate the emission correction method of vegetation and soil cover using the NDVI index on OLI and TIRS Landsat 8 satellite imagery. Data were taken in September 2018 in Golestan province. The results showed that NDVI and LST were inversely correlated with -0/96, with vegetation values ​​of R2 = 0/87 which can be used to obtain ground surface temperature. Also, the difference in determination of the maximum temperature obtained from the image, or LST calculated, is 2/9 ° C higher than the maximum data recorded in the synoptic and climatological ground stations in the region.