In this study the vegetation of gherh Aghaj in Pakdasht with the longitude of 51- 45 to 51- 57 and the latitude of 35-25 to 35-35 located on the south west survyed relation to the physionomy of vegetation. Sampling was conducted in releves. The study area separated in to plant unit using areal photographs. Broun- Blandqve table method was used for each plant unit, with regards tomp scale, twenty seven plant communities were Recognizes region. The physionomic studies were made according to kuchelers and one physiognomic formula was made to each plant community.

This study was carried out to create and compare vegetation maps based on geomorphology and physiography units methods. An area of about 16230 hectares comprised of four sub-watersheds of Arton, Kash, Zidasht and Kalanak were selected in Taleghan region. In Geomorphology method different maps of hipsometry, slope, aspect, geomorphologyical facies and lithology were prepared. Created maps were integrated to obtain a uniform terrain mapping unit. Each unit in this map was takenas a base of management and measurement for vegetation parameters. In Physiography method, map of primary vegetation type was obtained using 1:20000 areal photographs. This map was modified, based on field observations, to produce actual vegetation map. Then vegetation parameter assessment was conducted within each vegetation type. Arcinfo, Idrisi and Arcview softwares were used for integrating and digitalizing maps. Vegetation maps obtained from either of Geomorphological and physiographical methods were compared. According to the results, in spite of expectations, in some terrain units there were more than one vegetation type or in some vegetation types more than one terrain unit was found. In a general sense, there did not exist a strong relationship between vegetation types and terrain mapping units in the study area.

To better managing of rangeland the vegetation map is one of major factors, because plant communities is planning units of rangeland management and vegetation map shows the current status of plant communities. This research was conducted to produce vegetation type's map using Landsat 8 image classification in Behbahan, Khuzestan province. Rangelands of the study region is warm semi steppe and winter grazing. Geometric correction of satellite image was performed by ground control points with an error of less than one pixel. Atmospheric correction of existing data using the dark object subtraction was done. Field visits for vegetation type's border controlling and sampling training area was conducted. Eight supervised classification algorithms included Parallelepiped (PP), Minimum Distance to mean (MD), Mahalanobis distance (MAH), Maximum Likelihood (ML), Neural Net (NN) and Support Vector Machine (SVM) was performed. The results showed that ML algorithm has the highest overall accuracy (87. 5 percent) and kappa (0. 867) and PP algorithm has the lowest overall accuracy (67. 1 percent) and kappa (0. 571). It is suggested that, along with digital methods of classification of satellite images, visual interpretation should be used to clarify the boundary of the obtained vegetation types map.

Using satellite imagery is a reasonable option to overcome the field visits problems and limitations to evaluate the vegetation cover over the years. The present research has conducted to specify the percentage of vegetation cover of rangelands using Geographic Information System (GIS) and vegetation indices. The study area is located in Qazvin plain rangelands, Iran. In this study, the MODIS satellite images was used to extract the vegetation canopy percentage map in June 2017. To make a correlation between vegetation canopy percentage and satellite data, 160 plots (10*10 m2) were marked on the study area with an area of five thousand square kilometers so that each 10 plots was located on the perimeter of a hypothetical circle with a radius of 150 meters having 100 meters interval. Vegetation indices were extracted from the satellite images. The correlation between vegetation indices and field data were calculated by analyzing simple linear regression. Then, a vegetation cover model obtained for each of the vegetation canopy percentage. The study results showed that the correlation coefficient of the NDVI and EVI indices were 0. 63% and 58% respectively and the NDVI index was selected to prepare vegetation cover map because it had the highest correlation coefficient. Using the NDVI index model, the vegetation canopy for five percentage classes namely 10%>, 10-20%, 20-30%, 30-50% and more than 50% were prepared. The study results showed that 10 to 20% vegetation canopy class is prevailed. The study results also showed that MODIS and NDVI indices are suitable tools to create vegetation canopy map in the rangelands dominated by shrubs and grasses.

The analysis of the relationship between spatial distribution of environmental factors and vegetation types is crucial for understanding mountainous ecosystems. In this research a GIS based approach was used to produce a vegetation map for Sabzkouh protected area in the Chaharmahal- Va-Bakhtiari province. To identify environmental parameters affecting the vegetation cover, 6 primary and secondary environmental parameters including hypsometric, slope steepness, slope direction, annual precipitation, temperature and sun radiation maps were derived from the study area DEM. To investigate the relationship between these factors and the spatial distribution of vegetation cover, quantitative analyses using statistical techniques like Principal Components Analysis(PCA) were undertaken. Then, the spatial distribution of vegetation types was predicted using a multi-logistic regression. Results showed that topographic variables derived from the OEM were very useful for indicating habitats of range and forest types. Although lack of information on the anthropogenic effects led to some uncertainties in the interpretation of spatial pattern of vegetation types, the topographic and climatic variables, derived from the OEM, were considerably effective in modelling the spatial distribution of vegetation types.

The Mirabad protected area (S. Azarbaijan, Iran) has a variety of ecological nurseries due to elevation of the sea, physiographic factors, micro-climates and soil types, and has high vegetation diversity. Mirabad protected area in the Piranshahr-Sardasht axis is between the latitudes of 36° 23' and 36° 31' north, and the lengths 45° 15' and 45° 25', with an area of 11435 ha, in the elevation range 1177– 2068 m above sea level. The average rainfall and annual temperature over the age of 19 years are 696 mm and 12. 3 ° C, respectively. The present study, examines the vegetation structure of the region in the year 2017 where two types of classification and GIS were used. Thus, with the help of ArcGIS software (ArcGIS 9. 3), maps, slope, aspect, and elevation were prepared. From the integration of these maps, in the map of the units of work, 224 study units were homogeneous and identified and then field studies were done in these units. To sample vegetation by random distribution method, Whittaker multi-scale unit parts in plant types, 447 plant species were identified from 81 families and 301 genera. In the next step, plant types were based on biological diversity at a similar level of 75% in four distinct clusters, with the highest genetic distance between the Astragaleto-Ferulaetum plant type and the Querceto-Amygdaletum plant type. The highest and lowest similarity index was observed between Gulke 57% Gulke and Soeystan 12%, respectively. Biological diversity was higher in Quercetum, Querceto-Amygdaletum and Querceto-Pistaciaetum species than in other types.

ABSTRACT Today, climate change and its obvious negative effects on ecosystems have caused concern. This research seeks to test whether vegetation changes are sensitive to climate shocks and also how the ecosystem recovery process is through this index. In this regard, by using the GEE platform, Java coding, GIS and statistical analysis, vegetation and Palmer indices were calculated and based on time series climate data, vegetation and climate changes were presented. The results of Palmer's drought index show that during the statistical period (1985-2020) the study area is facing drought or is moving towards drought. Also, the results indicate the longest period of drought in the region from 2013 to 2020. Totaly from 420 evaluated months, the NDVI index is below the change threshold in 70 months. Among these, 31 months of the study period is below the acceptable threshold in green and non-reservoir seasons, which is ecologically worrying. The distribution of the vegetation index based on hexagons in 1985 and 2005 had a normal and almost normal distribution; But in 2020, the graph deviated from the normal state and skewed towards the vegetation cover index under stress or even thin covers. According to the analysis of the indicators, it is predicted that the Gorgan region is on the border of such ecological developments and the historical ecosystem of the region is moving towards new ecosystems or being in a new equilibrium state with climatic conditions and human disturbances Extended Abstract Introduction Today, climate change and its obvious negative effects on terrestrial ecosystems have caused great concern to humans. These changes are effective on vegetation performance, plant distribution patterns, and have economic and environmental consequences. Therefore, it is important to know the behavioral pattern of vegetation changes against climate changes. Reviewing the studies of scientists in the world shows many researchers have used the NDVI index to study temporal and spatial changes in vegetation and its relationship with the climatic index of precipitation in different parts of the world. Studies have shown that NDVI follows precipitation with different time scales. Surveys showed that there are very few studies on determining the threshold of changes in the vegetation cover index in the face of climate shocks. Determining these thresholds can provide a suitable solution for evaluating the state of the ecosystem, the consequences of climate shocks and the reversibility or disturbance in the ecosystem. This study was conducted with the aim of improving our understanding of the dynamics of vegetation in the forest city of Gorgan during 1985-2020 against climatic stresses.   Methodology The current research is a comparative and monitoring research and seeks to test whether changes in vegetation cover are sensitive to climate shocks and also how the ecosystem recovery process is through this index. To achieve the gole, first, NDVI index was selected among the optimal vegetation indices and its calculation process was done as a time series in the GEE system. In parallel with those climate shocks, the main elements including temperature, precipitation and storm were calculated during the historical process of 35 years and the average and standard deviation statistical indicators were calculated for them and the trend of changes in the thresholds was determined. The results of climate plots and climate changes show that in the years before 1985, 2005 and 2020, drastic changes have occurred in climatic elements and climatic factors. Therefore, these years can be considered as the periods when the climate shock happened.. Next, the region was divided into 436 hexagons and the NDVI index for each of the hexagons was calculated and modeled for the years 1985, 2005 and 2020 as selected years affected by climate shocks. In conclusion, to analyze the trend of changes in the time series of the vegetation index and compare the behavior of its changes with climatic indices, the Palmer index was calculated.   Results and discussion The results of climate change monitoring based on the Palmer index showed that during the statistical period the study area is facing drought in most years. The most severe climatic fluctuations and drought in the region were recorded in 2018 and in the months of October to December. The longest period of drought has also prevailed in the region from 2013 to 2020. During this period, rainfall, temperature and storm fluctuations have the most changes. The results of drought monitoring show that in 270 months, the region is facing climatic drought stress, 57 months of the study period, the region is facing severe and very severe drought stress. The results of the time series of the NDVI vegetation index showed that, out of the 420 evaluated months, 70 months of the year the NDVI index is below the change threshold, 31 of which are in the green and non-accumulating seasons, the seasons when the vegetation is expected to be at its maximum. Placing below the acceptable range means crossing the ecological thresholds and challenges the recovery and restoration of the ecosystem, also the ecological performance will be affected at this point. Based on the assessment of the Palmer index, from 2014 to 2019, the situation of the Palmer index is in the extreme drought range. Also, since 2015, i.e. with a one-year time delay, NDVI index has experienced the lower limit of the equilibrium threshold of vegetation cover. These conditions are also valid for the years 2008, 2009, 2002 and 1997. In general, it can be said that the vegetation cover index is dependent on climatic changes and fluctuations and shows high sensitivity to changes. The important point in this section is that in the years when the NDVI index changes are at the lower limit of the threshold, we witness the most climate shocks and temperature changes, the occurrence of severe storms and precipitation fluctuations. The distribution of the vegetation index based on hexagons in 1985 and 2005 have a normal distribution; but in 2020, the graph has deviated from the normal state and skewed towards the vegetation cover index under stress or even thin covers. The visual interpretation done on the vegetation cover index in 1985 confirms the condition of the vegetation cover in the southern and western limits of the region in a state with suitable dense and pasture vegetation and forest cover on the edges. However, in 2005 and 2020, this cover has been changed and mainly turned into agricultural land and poor rangeland. In such a way that in 2020, the situation of the region has revealed the critical state of vegetation. The vegetation cover index in the central areas of the city has also reached from a relatively favorable situation in 1985 to a critical situation with almost no dense and stress-free vegetation cover in 2020. The results of the present studies are consistent with the studies of Visentr Serrano et al. in 2013 and confirm the relationship between NDVI vegetation and climate change. In addition, the results of the studies are consistent with the studies of Alwesabi 2012, Xiai & Moody, 2005 and Yan et al. 2001. In such a way that the present study and the aforementioned studies all confirm the influence of the vegetation index on climate fluctuations and precipitation with a one-year time difference.       Conclusion In general, the threshold is defined as a border with different conditions. After crossing the thresholds, the stability and positioning of the NDVI in the equilibrium range is often difficult, and the ecosystem is constantly spending energy to restore itself or to position itself in a new stability state. The result of the mentioned disorders is the reduction of resilience and resistance in the region, which leads the ecosystem to alternative states or crossing the threshold or being in a new equilibrium state. The results showed that the areas where green vegetation is concentrated and denser are less affected by climatic stresses and show more resilience. However, the areas that have become spots and islands due to destruction in the urban areas are more affected by climatic stress and destruction and show less tolerance against the destruction factors. The results help managers to focus their management plans for the preservation and maintenance of urban green spaces as well as forest and pasture ecotones on the edge of the city by knowing the thresholds.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Currently, Remote sensing is a useful technology that can be used to extract different layers such as soil, rainfall, vegetation and so on. The ultimate goal in most remote sensing analysis operations that are used to investigate different factors of vegetation cover is that the data of various spectral bands that can represent data such as percentage of vegetation cover, biomass and leaf area index into a single value in each pixel. Reduce. Over time, environmental and human factors have caused positive and negative changes in the quantity and quality of vegetation; This situation will continue in the future. Temporal changes in vegetation may be in the form of increasing or decreasing trends. Recognizing these changes and determining their trends in the past and future can open the way for decision-making for the image of the land. One of the ways to study vegetation changes as the most important indicator of land degradation is remote sensing. Based on this, in this research, using the NDVI normalized vegetation difference index in HDF format and MODIS sensor with a pixel size of 250 meters in a 16-day period, monitoring the long-term changes in the vegetation cover of Fars province during a 20-year period from 2000 to 2020 and was investigated using spatial statistics functions in order to prepare a road map in the year 1400. For this purpose, using spatial statistics functions to prepare a road map. For this purpose, MADIS data has been used for a period of twenty years. Then the data were analyzed by classical statistics and spatial statistics. The results show the increasing trend of the vegetation level in a period of 20 years, and the distribution of the vegetation over time was clustered. At the end, a roadmap for long-term vegetation monitoring in Fars province was proposed.

In this research, 23 soil samples with specific geographical characteristics were collected to investigate and monitor salinity changes in the region. Using the Sentinel-2 and Landsat-8 sensors, seven vegetation cover indices and five salinity indices were examined and evaluated in the GEE environment, resulting in a total of 240 outputs from the two sensors. To assess the modeled values, several statistical indices including root mean square error (RMSE), coefficient of determination (R2), normalized root mean square error (NRMSE), and percent bias (PBIAS) were utilized. The results indicated that the SI-2 index exhibited the highest correlation with the measured salinity values in the region, with an R2 value of 0.91, demonstrating its accuracy in estimating salinity levels. In the next step, a multiple regression model was employed to investigate the mean values of measured ECe (electrical conductivity of the saturation extract) and the vegetation indices GDVI (Green Difference Vegetation Index) and CRSI (Crop Salt Stress Index) obtained from the Sentinel-2 sensor, which showed the highest correlation with the salinity data. The results demonstrated that the two-variable regression model achieved a satisfactory accuracy with an R2 value of 0.84 and a PBIAS value of 0.01 in producing a salinity map of the area. Therefore, this model can be utilized as a cost-effective approach for salinity mapping in the region with minimal ground-based data. Furthermore, the investigation of the impact of constructing a barrier drain in the area revealed that the construction of a barrier drain within a distance of 250 meters had a significant effect of approximately 40 percent in controlling salinity. It was able to prevent a substantial increase in salinity levels in the region. Therefore, if a barrier drain is not constructed in the area, salinity progression in the upstream agricultural lands could significantly escalate.