In this study, the replacement of different range sites and their relationship with soil properties was investigated. For this purpose, according to teh elevation and aspect three sites were selected including SHRUBLAND, grass-SHRUBLAND and grassLAND. The measured soil factors were calcium, magnesium, Phosphors, Potassium, lime, ammonium, nitrate, organic matter, PH, EC, bulk density, moisture and soil texture. Also, the vegetation cover in each site in the key area was measured. Detrened correspondence analysis (DCA) was used to achieve the relationship between soil and vegetation. The result showed that some changes which have in the performance of range ecosystems are the result of decrease in nutrirional elements; this issue has resulted in the distinction of the sites of grassLAND, SHRUBLAND and grass-SHRUBLAND. In fertile grassLANDs with reduction of nutrients, first, grasses will replace the forbs and in the next stage, the grasses are replaced by SHRUBs. This trend is accompanied by decease of organic substance, humidity and potassium, and ultimately the pH and sand rate will increase in the SHRUBLAND.

In order to evaluate the capability of ETM+ remotely- sensed data to provide "Forest- SHRUB LAND- RangeLAND" cover type map in areas near the timberline of northern forests of Iran, the data was analyzed in a portion of nearly 790 ha located in Neka- Zalemroud region. First, ortho-rectification process was implemented to correct the geometric errors of the image, which yielded 0/68 and 0/69 pixels of RMS error toward X and Y axis, respectively. The original multi-spectral bands were fused to the panchromatic band using PANSHARP Statistical module. The ground truth map was prepared using 1 ha field plots in a systematic- random sampling grid. Vegetative form of trees, SHRUBs and rangeLANDs was recorded as a criterion to allocate the plots. A set of channels including original bands, NDVI and IR/R indices, and first components of PCA was used for classification procedure. Automatic band selection command was used to select the appropriate channel set. Classification was carried out using ML classifier on both original and fused data sets. It showed 67% of overall accuracy and 0/43 of Kappa coefficient in original data set. Due to the results present presented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of 3 form- based classes, in the studied area. Furthermore, applying complementary methods to minimize the background spectral effect is proposed for future studies.

Background and Objectives: Given the significance of investigating and monitoring riparian ecosystems, this project was designed to identify and map the LAND cover, including tree and SHRUB species classes, around the Zarineh Rood River in West Azerbaijan province, Iran. Recognizing that the separation of LANDs with high spectral similarity using single-time images is not precise, this study utilized a time series of satellite images, capitalizing on the phenological differences of plant species.Methodology: The research separated the LAND cover classes into two stages. In the first stage, the time series data from Sentinel 1 and 2 were used to map different classes of tree cover (natural, wood farming, orchard), SHRUB cover (natural, orchard), grass or pasture, agriculture, residential LANDs, soil, and water bodies. Given that seasonal changes in the images can provide valuable information about LAND cover classes, a one-year (2021) time series of Sentinel 2 optical images and Sentinel 1 radar polarizations for 2021, in the form of median in each season, were processed on the Google Earth Engine platform. The data were classified using four composites of input features and four classifiers. In the second stage, to separate the vegetation classes into Tamarix, willows, orchard, and poplar plantation, the trend of one-year changes of normalized difference vegetation index (NDVI), normalized green red difference index (NGRD), normalized difference red edge index (NDREI), and green normalized difference vegetation index (GNDVI) combined with HV polarization of Sentinel 1 radar in the form of median in seasons, was used as an input feature. The LAND cover map produced contained Tamarix, willows, orchard, poplar plantation, grass or pasture, agriculture, residential LANDs, soil, and water bodies.Results: In the first stage of classification, the input feature of NDVI (Monthly)_ Radar (Seasonal)_ Sentinel 2 (Seasonal) and the random forest classifier were the best feature and the most accurate classification algorithm, separating the classes from each other with an overall accuracy and Kappa coefficient of 88% and 0.85, respectively. In the second stage of classification, the NDVI index between the months of April and November enabled the separation of all four tree and SHRUB covers. GNDVI between December and April was the best indicator for separating willows. Also, between May to November, it effectively separated Tamarix. NGRDI was suitable between May and November for separating Tamarix and also separated the poplar plantations between April and November. The GNDVI index between April and September effectively separated the two categories of orchards and poplar plantations from Tamarix and willows. The map was generated using the mentioned input feature and random forest algorithm. The overall accuracy and Kappa coefficient obtained from the validation relying on ground samples and Google Earth images were 80% and 0.77, respectively. The main diagonal of the error matrix shows the highest separation between water, soil, and urban LAND classes. Among the vegetation classes, willows and agricultural LANDs exhibited the best distinction.Conclusion: The variation in a plant’s phenology, encompassing leafing, blossoming, fruiting, fall, and sleep cycle, leads to changes in the values of vegetation indicators during the seasons, which can be utilized in mapping vegetation to enhance separability. Consequently, if tree and SHRUB stands are pure and exhibit a different phenological behavior from their neighbors, they can be distinguished with higher accuracy using time series of satellite images.

In this study was purposed to investigate the absorption of zinc by Acacia victoriae. The one year old SHRUBs were randomly selected in 3 replications and exposed by Zinc-sulfate in four concentration (0, 50, 250, 500 mg/l). Some of the morphological and physiological properties such as plant height, collar diameter, leaf number and area, green and dry weight of plant, tolerance index, grade of growth inhibition and chlorophyll content as well as bioaccumulations of Zn in different plant tissues were evaluated. Results showed that the properties were significantly affected by Zn. However, the effect of high concentration of Zn slight had as well as tree on effects series any have didn't that, properties the in decreasethe signs of toxicity with zinc, addition In. observed not was the amounts of accumulation of Zn in the roots were higher than aboveground tissues, so that about 80% of Zn (4794. 5 mg/kg), more than 13% of Zn (797. 9 mg/kg), and about 7% of Zn (488. 35 mg/kg) were accumulated in the root tissues, the stem tissues, and the leaves, respectively. The bioaccumulation and transfer factors were also measured to higher one and lower one, respectively. Generally, the result demonstrated that the Acacia victoriae could be affected as a phytoremediation in reduction of soil pollution to Zn.

Understanding the climatic potentials of the regions is very important for the diversity and talent of agricultural products. Barberry, one of the products of Iran, suffers from climate change and anomalies. In the present study, the phonological stages of barberry tree without any basis of field observations in Ghaen synoptic meteorological station were determined. To measure the accumulation of cooling needs based on the cold clock model and the Utah unit, the statistics of 18 valid meteorological stations from 1987 to 2017 on an hourly and daily time scale were used. The results showed that barberry needs six phonological stages to complete the growth period from early April to late November. The highest temperature requirement occurs in the ripening stage until fruit development. The cooling requirement of barberry tree in different stations varies from 1050 to 1960 hours depending on climatic conditions. Field observations showed that seedless barberry does not take on a full and commercial color if it does not meet the need for sufficient cooling. The study area was zoned according to the models of the cold clock and the Utah unit, based on which Ghaen and Zahedan stations have the highest cooling needs. Based on the validation indices of different models estimating the need for cooling, the root mean square criterion was used and the results show that the cold hour (CH) model has a higher performance due to the fact that the root mean square (RMSE) is less than the other model.

The aim of this study is to evaluate the performance of two precipitation datasets, CHIRPS and ERA5-LAND, in detecting droughts using the Standardized Precipitation Index (SPI) over Iran. To achieve this, the raster data of the two datasets were evaluated using the standardized precipitation index in 11 time steps, along with precipitation data from 110 meteorological stations and correlation analysis. The study results revealed that both CHIRPS and ERA5-LAND datasets exhibited a high degree of correlation with station data in time steps ranging from 1 to 12 months across Iran, except for the northern region (RCHIRPS = 0.56, RERA5 = 0.76). Furthermore, the ERA5-LAND dataset demonstrated an acceptable level of performance in higher time steps (24-72 months) compared to CHIRPS. Additionally, the study revealed that the areas affected by drought were relatively similar between both datasets for time steps up to 12 months, although the reliability of CHIRPS was questionable at higher time steps. Moreover, based on the results, it was found that in general, the accuracy and capability of databases in high time steps, with a statistical period length of 30-40 years, have been significantly reduced, and for longer time steps, a longer statistical period should be used.

Temperature is a major variable in the Earth's climate system, which plays an important role in energy exchange interactions between the Earth's surface and the atmosphere. There are various sources for temperature estimation, including ground stations, satellite products, reanalysis datasets, and multi-source weighted-ensemble datasets. Reanalysis datasets are generated by combining different types of observational data for a certain time in numerical weather prediction models and using ground and satellite observations. The purpose of this research is to investigate the performance of the ERA5-LAND and AgERA5 reanalysis datasets as well as the MSWX multi-source dataset to determine reliable datasets for estimating temperature in Iran. First, we evaluated the temporal variations of the three datasets against the station data. We used the air temperature from 98 stations for 30 years from 1991 to 2020. Three metrics including Root Mean Square Error, Bias, and Index of agreement were used to evaluate ERA5-LAND, AgERA5, and MSWX datasets.Then, considering the seven main climate zones of Iran, the spatiotemporal quality of the annual mean temperature was evaluated in different climate zones. The results showed that all three datasets have less error and bias in the estimation of the minimum temperature of Iran. However, AgERA5 and MSWX significantly showed less error in the estimation of the maximum temperature with RMSE of 1.74℃ and 1.42℃, respectively. On the other hand, the ERA5-LAND dataset shows overestimation (5.05℃) and high error values (5.07℃) over the country-averaged. The results showed that the MSWX dataset has a better performance in estimating Iran's temperature with an average bias of 1℃. The interannual variations and decreasing and increasing trends of temperature in three datasets with correlation above 0.86 in all climate zones of Iran show a high consistency with observational data.The RMSE in all three datasets reaches its maximum in the winter season in mountainous climate zones of the country. This may be caused by snow-albedo feedback in mountainous climate zones. The findings showed that performing bias correction and downscaling methods as they have been done in MSWX and AgERA5, significantly improved the reanalysis dataset compared to the direct model output. Nevertheless, in the southwest of the Caspian Sea, the bias-corrected MSWX and AgERA5 show more errors than ERA5-LAND. In general, the values of bias and RMSE in all three datasets are affected by the physical schemes of the model, parameterization, and data assimilation system, or the downscaling and bias correction methods. However, sources of bias can be different in different seasons of the year. The monthly spatial distribution of temperature in Iran shows that the minimum temperatures are located in the middle of Alborz and the northwest mountains of the country, and the coldest month of the year is January with a temperature of -10.8℃. The maximum temperatures in Iran are located in the southwest of the country and the southern coasts, and the hottest month of the year is July with an average temperature of 42.38℃.

In order to ecologically investigate Rhus coriraria L. species and recognize its existing sites in west of Iran, boundaries of these species were located on the topographic map and 8 plots with areas of 100 square meters were implemented. Soil samples were collected from 0-10 and 10-20cm depth. Using analysis Kolmogorov-Smirnov, Levene and T-test were also used to compare that with and without Sumac covering with regard to biodiversity indices. The results showed Organic matter and Potassium in depth of 0-10 cm at Sumac covered area was greater than those at adjacent (without Sumac covering) area. Sumac covered area had greater values of Organic matter, nitrogen, calcium and clay percent in depth of 10-20 cm in comparison to adjacent area. In addition, the comparison of soil ingredients in different depths of the two areas showed that Electric conductance in depth of 0-10 cm and Nitrogen content in depth of 10-20 cm of sumac covering area was greater than those of adjacent area. Based on the results of this study, it can be concluded that Rhus coriraria L species in regions with soil types of high amounts of N, C-O, K, Ca and Clay and located in steep LANDs with north directions. Therefore, in order to plant this species and enrichment of this species in its natural habitat and other dry and semi-dry regions requirements of this species must be considered.

Lycium depressum, a prickly SHRUB from Solanaceae family, is a native species from Iran with high salinity and drought tolerance ability. In this study, the establishments of an efficient micropropagation method for L. desressum was investigated. In the first experiment, the effects of benzylaminoporin (BAP) and thiadiazuron (TDZ) in combination with naphthalene acetic acid (NAA) were investigated. In the second experiment, the effect of various organic and inorganic compounds including myo-inositol, casein hydrolysate, proline, FeEDDHA, as well as calcium chloride on the improvement of proliferation and quality of in vitro regenerated shoots were evaluated. According to the results of these experiments, BAP at low concentration (0. 5 mg / L) was regarded as the best treatment for in vitro proliferation of L. depressum. Moreover, the maximum leaf number was achieved in this treatment. In addition, increasing the concentration of myo-inositol to 200 mg/l in MS medium, enhanced shoot proliferation, leaf size and quality of regenerated shoots. The highest root number and length was obtained on full strength MS medium supplemented with 0. 3 mg/l IBA. Following rooting, the developed plantlets were hardened and established successfully in culture room with 95% survival rate. Overall, this experiment resulted in an efficient micropropagation protocol for L. depressum. The findings of this study can facilitate the path to more extensive research program on various aspects of domestication of this valuable medicinal and LANDscape species.