Floods play an important role in the flow of rivers, so their investigation and analysis are importance. Studying the dynamics of floods and the water discharged into the sea (plume) is very important in the fields of fisheries, sedimentation, transportation and environment. Babolrud-River originates from the south of Babol city and flows into the Caspian-Sea after traveling about 67 kilometers. In this research, the expansion of the plume entering from the Babolrud-River to the Caspian Sea during the spring flood of 2018 has been investigated. For this purpose, firstly the images of Sentinel-2 satellite were taken, then the required pre-processing including geometric and radiometric correction was applied. According to the spectral behavior of muddy and clear waters, in the spectral range of wavelengths of 0.4 to 0.78 micrometers, this phenomenon can be distinguished. As a result, by using this feature and the optimal index factor (OIF), the best color combination with the largest information was detected. The combination bands of 3, 4 and 8, with the OIF of 0.19, was defined as the best band combination. In the next step, NDVI, NDFI, and MNDWI were applied, and thresholds were applied to the defined indices for better separation of muddy and clear waters. These thresholds were identified by drawing spectral profiles at the plume of river and checking their histograms. Finally, by building the decision tree with all these indicators and applying the thresholds, the amount of muddy water from the flood entering the Caspian Sea from the Babolrud-River was revealed.

Accurate monitoring of surface water is one of the important and necessary applications in the use of remote sensing systems. Meeting the needs raised in the use of remote sensing data collected from the earth's surface in many applications, using only one product and classification algorithm is not sufficient and possible, and for a more accurate understanding, data fusion can be a better option. In this system, various approaches such as water extraction indices or classification algorithms are used to identify water areas. In this research, an fusion approach of Landsat-8 and Sentinel-2 optical sensor images was used. Firstly, the spatial resolution of these sensors was enhanced from 30 to 10 meters by Pansharpening them and preserving spectral information. Then, water extraction indices such as NDWI, MNDWI, AWEI_sh, AWEI_nsh, and WI were applied to the integrated images. Subsequently, using classification algorithms such as SVM, Maximum Likelihood, Minimum Distance, Neural Network, and Random Forest, the study area was classified into two categories of water and non-water areas. Finally, the results obtained from all classification algorithms for pre and post-flood images of Mazandaran province in the 2019 flood event were merged using the majority voting method, which is considered an integration approach at the decision-making level. Random forest classification algorithm with overall accuracy of 97.76 and 94.12 and Kappa coefficient 94.49 and 91.41 for images before and after flood had the best classification performance among the algorithms used in this research. The fusion of classification algorithms showed an improvement in the separation performance of water and non-water areas with an increase in the overall accuracy of separation to 98.41 and 95.24 and Kappa coefficient 96.12 and 92.81 for the images before and after the flood.

Background and objectives: Leaf area index (LAI) is a vital biophysical characteristic to assess the condition, describe forest structure and function of forest ecosystems. LAI is a key input in modeling global climate change, carbon fluxes, water cycle, photosynthesis, and interception processes. The estimation of LAI in forests through remote sensing data, using machine learning models, has gained widespread attention, particularly for large-scale LAI mapping. This method is favored for its efficiency, involving minimal time investment, cost-effectiveness, and a non-destructive approach. This study aimed to investigate the potential of Sentinel-2 data for estimating the LAI of northern Zagros forests, employing the Gaussian Process Regression (GPR) method.Methodology: LAI field data were collected in June and July 2023 from a coppice forest in the Marivan and Sarvabad counties of Kurdistan province, Iran. A total of 93 square plots, each measuring 20×20 square meters, were randomly selected. The location of each plot was recorded using a DGPS device. The LAI within each plot was measured using the hemispherical photography method. Five photos were captured within each sample using a Coolpix4500+FC-E8 camera equipped with a fisheye lens. The LAI was then calculated for each hemispherical photo and averaged for each sample plot using the “hemispheR” package in the R programming language. A cloud-free Sentinel-2B image with L1C correction level was acquired on July 2, 2023. After verifying the radiometric and geometric quality of the image, the Sen2Cor processor was used to apply atmospheric correction. Different input data, including spectral bands and spectral indices (Vegetation Indices, Tasseled Cap Transformation, and Principal Component Analysis) were generated from the Sentinel-2 image. These datasets, i.e., the spectral bands, spectral indices, and a combination of spectral bands and spectral indices, were used to estimate LAI. The modeling process was carried out using the GPR algorithm based on 65 sample plots (70% of the dataset). The performance of the models was finally evaluated using 28 plots (30% of the dataset) with different metrics such as the coefficient of determination (R2), root mean square error (RMSE), relative root mean square error (rRMSE), and Akaike Information Criterion (AIC).Results: The descriptive statistics for the measured LAI showed that the minimum, maximum, average, and standard deviation values of the leaf area index over the study area were 0.33, 3.88, 2.129, and 0.627 m2.m-2, respectively. The Pearson correlation analysis between forest LAI and spectral variables (including original bands and spectral indices) indicated a stronger correlation between LAI and spectral indices (i.e., GNDVI, SAVI, and TCTV) than the original bands. Thirty percent of field sample plots were randomly selected and used to evaluate the forest LAI model generated using the GPR machine learning algorithm based on three datasets: original bands, spectral indices, and a combination of original bands and spectral indices, all derived from Sentinel-2 imagery. The evaluation outcomes revealed that the model derived from the main bands of the Sentinel-2 satellite achieved R2 = 0.81, RMSE = 0.21 m2.m-2, rRMSE = 9.14%, and AIC = 103.65. This performance was deemed satisfactory when compared to the performance of models built using the other two datasets (i.e., spectral indices, and a combination of original bands and spectral indices) to estimate LAI. Using the best-performing model, a comprehensive LAI map of the study area was generated using data derived from the main bands of Sentinel-2 imagery.Conclusion: This study provides preliminary evidence of the potential of Sentinel-2 satellite data in evaluating the leaf area index in the North Zagros coppice forests. However, the integration of ground data of leaf area index and Sentinel-2 data from various growing seasons could potentially enhance the robustness of the results and mitigate uncertainties, thereby paving the way for future research endeavors. This approach could lead to more accurate and reliable assessments of forest health and productivity.

Flood is one of the most widespread and common natural hazards in the world. Depending on the severity of the rains and other factors affecting the flood, it can cause severe personal and financial damage to human. In this paper, Sentinel-1 Synthetic Aperture Radar images and their application in flood spreading mapping along the Kashan River, Poledekhtar County, have been investigated. The purpose of this research is to produce a map that is extracted from the SAR images (sentinel 1), showing the extent of Kaskan river flooding in beginning of year 1398. The main data of the paper are Sentinel 1 (SAR), satellite images before (2019/03/28) and after (2019/04/03), the flood as well as Sentinel 2 satellite images. For this purpose, pre-processing and correction of different images were performed first. The Kashkan River Area was extracted using the NDWI Water Index from sentinel 2 image (2019/05/05). This image was used as a reference image. Then before and after the flood images were analyzed using Utso thresholding method and finally the flood area was extracted through image classification. SNAP and ArcGIS software were used for this purpose. Results show that in the study area, 7. 99 km2 of the river banks has been submerged due to the April 1389 flooding. Also Radar image data can be used for flood surveillance because of imaging in all weather conditions, and the areas involved in flooding can be extracted from these images in the shortest possible time. It can also achieve detailed observations of 10m resolution for water extent using Sentinel-1 SAR imagery at an interval of several days (depending on latitude with a 5 or 6-day revisit and 12-day same orbit revisit time at the equator) to be used in future planning.

Background & Objective: The Lack of timely, documentary and scientific information from the current status (level and distribution) of poplar plantation of Tehran province is one of the main problems facing the managers of the wood production sector in the planning and management of wood supply in the province and the country. Preparing a map and determining the areas of poplar plantation and their distribution in Tehran province are the objectives of this study to monitor and evaluate changes of poplar plantation area in short-term periods. Material and Methodology: The present study was conducted from April 2018 to March 2020 for 2 years in the whole of Tehran province. In this study, multi-temporal data, from the beginning to the end of the poplar growing season (second half of March to December 2018), at least 6 time periods of 30 to 40 days were used from Sentinel-2 satellite image. Then, 355 poplar plantation fields with uniformly distribution in the province were taken as a training sample for use in the SVM classifier. Post-test and calibration of SVM model based on the phenology of poplar genus and harvested field samples, poplar plantation distribution map of province was extracted. Findings: The results showed that the total area of poplar plantation of Tehran province is 511. 1 ha which covers 0. 04% of the total area of province. One percent of the total poplar plantation fields were randomly selected for field control and after that, the overall mapping error obtained was calculated. In this study, the exact location and area of current poplar plantations were estimated with acceptable accuracy (96. 7%). The highest level of poplar plantations was obtained in Damavand (196. 8 ha), and the lowest in Varamin (0. 22 ha). Discussion and Conclusions: Using the resulting information (distribution map and mapping poplar plantation of province), can be initiated in studies on cultivation planning and development of wood farming for the present and future of the province.

Floods are one of the most important natural hazards threatening human societies. Flood issues are diverse and complex in nature. The onslaught of floods destroys facilities and causes human and financial losses and disrupts transportation and communications. Estimating flood area in flooded areas allows us to obtain flood damage and determine the extent to which we can identify a plan to reduce the damage and high-risk areas and reduce the risk to some extent. In this regard, remote sensing and GIS techniques are very suitable methods for data collection, fast, accurate and cost-effective decision making. For this study, Sentinel 1 and 2A satellite images for January 2020 were used. Also, the object-oriented method of satellite images and the capability of the Google Earth engine system were used to model and extract the flood area. Based on the results of accuracy evaluation, kappa coefficient and overall accuracy of object-oriented classification algorithms showed the best result compared to other processes. Also, validation results showed that object-oriented classification algorithm has an overall accuracy of 0. 94 and kappa coefficient of 0. 88 and the processes performed in the Google Earth engine system have an overall accuracy of 0. 91 and a kappa coefficient of 0. 87. These results indicate that object-oriented algorithms and the Google Earth engine system are useful tools for identifying flooded areas and can assist planners in managing natural hazards in the study area.

With a length of 950 km, Karun River is the longest river in Iran. In this study, we aimed at application of Sentinel-3B satellite altimetry data as well as Sentinel-1 and Sentinel-2 satellite imagery for the estimation of Karun River discharge and validation with the in-situ data. Knowing that Level-2 altimetry data are not reliable for rivers and shallow waters, we opted to re-track the waveforms of Level-1B Sentinel-3B mission data and to test several re-tracking techniques for this purpose. The results showed that the threshold algorithm, with threshold of 90%, improves the accuracy of the time series of water level by 7. 05% and increases the correlation with the in-situ gauge data by 12. 7% as compared with those obtained via Level-2 data based on OCOG that was identified as the optimum re-tracker in this case. Next, from the estimated time series of the river’ s water level, the time series of Karun River discharge were evaluated in order to constitute our discharge estimation based on Sentinel-3B satellite altimetry data, which further to be compared with the discharge that we calculated using satellite imagery of Sentinel-1 and Sentinel-2, while taking the in-situ data as the benchmark. The river’ s discharge time series obtained from the altimetry data resulted in RMSE value of 852. 31 m 3 /s, NSE coefficient of 0. 19 m 3 /s, and correlation of 62. 40% with the in-situ river discharge time series. On the other hand, the river discharge time series obtained from satellite imagery of Sentinel-1 mission resulted in RMSE value of 165. 06 m 3 /s, NSE coefficient of 0. 94 m 3 /s, and correlation of 97. 12%, and Sentinel-2 mission the RMSE value 264. 23 m 3 /s, NSE coefficient of 0. 81 m 3 /s, and the correlation of 97. 32% with in-situ data. The overall results of this study indicates that various Copernicus satellites missions have good potentiality for Karun River discharge monitoring.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

Purpose: The study purpose was to determine and compare the Recall and Precision of Bing and Google Image search engines for content based image retrieval. Methodology: The research used webometrics and comparative methods. Population includes images stored in the databases of Bing and Google search engines, and research sample includes 15 selective images searched in any of search engines. All the retrieved sources through the images by image content based image search were gathered, results’ Recall and Precision measures were calculated by relevance formula and their average percentage were obtained. Research hypotheses were tested by U Mann-Whitney test as well. Findings: Findings showed that the Google search engine functionality was higher with recall measure of % 88. 73 than recall rate (%20. 86) for Bing. But Bing search engine had higher precision (% 99. 86) than Google (%94. 80). Results: Hypotheses tests on recall and precision in two search engines’ image retrieval showed a significant difference for recall in favor of Google, indicating its better functionality than Bing but there was no significant difference between them concerning precision since both showed fair precision however Bing was relatively useful.