فهرست مطالب ehsan atazadeh

Diatoms are one of the biological indicators suitable for evaluating river water quality. Diatoms reflect the ecological conditions of aquatic ecosystems so that some diatoms choose a polluted environment for growth. Thus, diatoms can be used as an indicator of pollution. In this regard, the present study was conducted in order to determine the diatom flora of the Ahar-Chai River and to investigate the effect of environmental factors on diatom communities. In this study, 6 sampling stations were determined in the Ahar-Chai River. Sampling was done seasonally in 2019. In general, the number of 40 genera and 123 species were observed in this river, the most species diversity are Cymbella, Nitzschia, Navicula, and Gomphonema, all four genera belong to the Bacillariphyceae. A high biodiversity of diatoms was observed in spring and summer. The statistical analysis of this research showed that parameters such as temperature, pH, electrical conductivity (EC), turbidity, light, and amount of nutrients have a significant effect on the distribution of diatoms along the Ahar-Chai River.

Considering the importance of maize production and the impact of water deficit stress on reducing the yield of maize, estimating the genetic components and heritability of traits for determine the breeding method under water deficit stress is essential in breeding programs. The generations drived from a cross between two inbred lines of maize including B73 (maternal line) and MO17 (paternal line), SC704 (F1) as well as F2, BC1, BC2 and F3 generations in order to estimate the genetic effects and heritability of yield, yield components and morphological traits were studied. Seven maize generations using the generations mean analysis under the full irrigation, mild and severe water deficit conditions were evaluated. The experiment was conducted in the form of randomized complete block design with 20 replications per experimental unit during two cropping seasons (2018-2019) at the Agricultural Research Station of University of Tabriz. The results of two-year combined analysis of variance and mean comparisons under three different irrigation regimes showed that water deficit stress significantly reduced all of the studied traits (except root/shoot ratio). The generations mean analysis showed the high contribution of non-additive gene effects for the genetic control of grain yield, ear diameter, number of kernel row, ear weight (in full irrigation conditions), 100 grain weight, plant height, fresh shoot weight and biological yield traits. According to these results, selection in the advanced generations and the breeding method based on hybridization can be effective to improve these traits. Also, the significant contribution of additive gene effects in controlling the inheritance of ear length, ear weight (in both stress conditions) and root/shoot ratio traits indicated that selection in early segregating generations and inbred parents can be effective for breeding of these traits and taking advantage of additive variance. Hybrid SC704 and inbred MO17 compared with the inbred B73 showed the lowest variation percentage under the water deficit stress conditions, which indicated their high yield potential and stability in the stress conditions.

This study is the first attempt to investigate some diatom species in the western rivers of Lake Urmia. The study presents the details of Encyaonema, Craticula, and Cymatopleura at the lowest taxonomic level to provide information in terms of species taxonomy, morphology and ecology. In order to determine the morphological similarities in species level of the aforementioned genera, 14 sites were repeatedly sampled along the rivers from September 2021 to August 2022. The results showed a high diversity of diatoms including Encyonema caespitosum. Encyonema silesiacum, Encyonema prostratum, Craticula cuspidata, Craticula ambigua, Cymatopleura solea, Cymatopleura apiculata, Cymatopleura hibernica and Cymatopleura elliptica. The taxonomic and ecological study of the observed species from the western rivers of Lake Urmia indicates diatom assemblage closely integrated with the limnology and ecological status of the rivers. Indeed, the results infer that the rivers undergo a transition from freshwater to brackish, particularly where the rivers terminate at Lake Urmia.

The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems. In generally, global waters are classified into two types of water (the first case and the second case). In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However Case-2 waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter and Total suspended matter. Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.  Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves. Chlorophyll-a considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale. The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-a concentration.

In this study, Landsat 8, Sentinel 2 satellite time series data and field data collected from the study area were used. First, the satellite images used in ENVI 5.3.1 software were converted to Surface Reflectance and Top of Atmosphere Reflectance. Then, MATLAB 2018a software was used for image processing and coding. to estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results two statistical parameters R2 and RMSE were used.

Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere Reflectance Sentinel 2 and Landsat 8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat 8 and Sentinel 2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat 8 and Sentinel 2 Surface Reflectance data, respectively, while Landsat 8 and Sentinel 2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat 8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-a concentrations using the surface reflectance data (Sentinel 2) during the seasons estimated the chlorophyll concentration to be uniformly and downward. The reason for this poor performance in the Sentinel 2 is the lack of sufficient field data for calibration.

In this study, we tried to evaluate and compare the reflectance algorithms (Landsat 8 and Sentinel 2) in the OC2 algorithm. Preliminary results indicate that the type of satellite data used (surface reflectance and Top Atmosphere reflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data. In general, the results showed that 10 field data is enough to calibrate with Landsat 8 data, but for Sentinel 2 data, more than 10 numbers field data must be calibrated to obtain a good result.

The estuaries are one of the most important coastal natural resources. Chlorophyll-a (Chl-a) monitoring, which is the pigment of oceanic and coastal phytoplankton, can be measured and evaluated using new remote sensing technology. The presence of blue, green and red wavelengths in oceanic observation satellites has always paved the way for monitoring the color of the oceans. The aim of this study is used OC2 and OC3 bio-optical algorithms and Sentinel-2 MSI and Landsat-8 OLI satellite data in April 2019 to estimate chlorophyll-a concentration in the estuary Tiap area. Ground sampling data were carried out to correlate and evaluate the results. The results showed that the OC2 algorithm in Landsat-8 and Sentinel-2 satellites had the highest R Squared coefficient (R2) 0.91 and 0.64, respectively, and the Root mean square error (RMSe) of the satellite images were 0.13 and 0.33, respectively. These results indicate the high accuracy of the OC2 algorithm in the satellite images used and were selected as the most suitable algorithm for mapping chlorophyll-a concentration in the study area.