mohammadjavad tajadod

PirBazar River is one of the most important rivers leading to Anzali Wetland. It is considered as one of the most polluted rivers in Iran due to the entry of a huge volume of industrial and urban sewage and leachate. Accordingly, about 50% of the pollution and sediment load leading to the Anzali Wetland comes from this river. The aim of this article is to investigate the trend of land use changes in the PirBazar watershed and its effects on the levels of pollution in the rivers of this area (Zardjob and Gohroud). In this study, the process of changes from 1990 to 2021 (30 years) was investigated using Landsat series satellite images (Landsat 5 and 8). 5 categories including urban, industrial, forest, agricultural and water land use were classified through vector machine algorithm and neural network. Also, the changes of each land use type in the area were examined through the LCM model. Results of the changes showed that the urban land use has been increasing in the study period. Moreover, the water quality index (NSFWQI) was directly related to the increasing trend of the residential land use and decreasing trend of the agricultural land use. According to the study results, the control of wastewater from residential areas can be an effective solution to reduce the pollution load of PirBazar River.

Technical limitations in classifying heterogeneous wetland environments, characterized by diverse vegetation cover, land use, and species diversity, often lead to interference in classification results and reduced accuracy in differentiating vegetation classes within wetland ecosystems. There is limited research available to improve classification methods in wetland environments. The main objective of this study is to investigate the combination of multi-spectral and radar data in improving the classification methods of wetland environments and to provide a method for fine separation of different plant covers in these biodiversity environments. In order to better examine the changes of the spectral index during a year, the open-source system of Google Earth Engine is used so that the spectral behavior of the phenomena during the year can be accurately studied.

In this study, a combination of Sentinel-1 and Sentinel-2 data was used as the first data series, and a combination of Sentinel-2 data with spectral indices such as NDVI, SAVI, and mNDWI was used as the second data series. The best image for each season (summer, autumn, winter, and spring) from 2016 to 2022 was selected to create classification maps and examine detailed changes in the wetland. For image classification, training areas were selected based on field sampling, combining satellite imagery and Google Earth images. Classification was performed using three supervised algorithms: Support Vector Machine, Artificial Neural Network, and Maximum Likelihood. Also, the index map was prepared in the Google Earth Engine system and the indices were calculated using the ready-made products available in this system and were reviewed monthly for one year. To ensure the classification and to evaluate the classification accuracy, the most common accuracy estimation parameters, overall accuracy, producer accuracy, user accuracy and Kappa coefficient were used.

The results indicated that the combination of Sentinel-1 and Sentinel-2 data yielded better results compared to the combination of Sentinel-2 data with spectral indices. The overall accuracy and Kappa coefficient for the four periods were 92.99%, 87.43%, 83.80%, and 97.90% (in 2016, 2017, January 2022, and July 2022, respectively) when using the combination of Sentinel-1 and Sentinel-2 data, which were significantly higher than the results obtained with the combination of Sentinel-2 data and spectral indices. Furthermore, the combination of Sentinel-1 and Sentinel-2 data resulted in better detection of water bodies and lotus habitats within the wetland. NDVI, SAVI and mNDWI have a high correlation in examining the changes, so that an increasing trend was observed in the first six months of the year and a decreasing trend in the second six months, and the trend of vegetation and water changes is the same.

Due to the complexity of wetland spatial structures and existing threats, identifying land cover types is challenging. This study demonstrates the use of multi-temporal Sentinel-1 and Sentinel-2 data to comprehensively assess wetland characteristics. The accuracy assessment for the four study periods from 2016 to 2022 using three classification algorithms, Support Vector Machine, Maximum Likelihood, and Artificial Neural Network, showed that the combination of Sentinel-2 and Sentinel-1 data outperformed the combination of Sentinel-2 data with spectral indices in terms of overall accuracy and Kappa coefficient. Among the three algorithms used, the Maximum Likelihood algorithm consistently achieved the highest overall accuracy and Kappa coefficient compared to the other two algorithms.

Sustainable development is the most basic human attitude developed today, for the balanced management of the world in the future, which is based on the priority of human beings over the whole world, and puts the management of biological resources at the forefront of development plans without affecting the resources of future generations. to give The Global Index of Sustainable Development Goals 2019 provides a comprehensive assessment and study of the process of achieving the Sustainable Development Goals. Due to the limitations of data and information in 2019, this index was used for 162 countries using 85 indicators. In this research, due to the fact that the extracted data and variables had different units for measurement, the first step of preparing the indicators is necessary to resolve the difference in the scale of the data and variables; Then the data were graded between zero (worst performance) and 100 (best performance). After calculating the values of each of the country's indicators, the total score of each goal has been calculated based on national and international information sources, and based on this, the first goal has the best performance with 96.8 points and then Of that Arman 4, 955/5 has been ranked second. In 2019, the worst performance was related to Arman Nahm with 39.8 points, which was at a significant distance compared to the first performance. Also, Arman Pahm has taken the second place from the bottom of this ranking with 6.42. The scores of other ideals of our country are higher than the average, but there is still a long way to go before reaching the ideal situation in Iran.