جستجوی مقالات مرتبط با کلیدواژه "radar images" در نشریات گروه "کشاورزی"

Flood damage assessment is often necessary for early flood management. To this end, this paper provides a framework of rapid estimation of flood damage and identification the flooded areas in March 2020 using Sentinel-1 satellite data. To this end, in the present study, after applying the necessary pre-processing in SNAP6 software, the backscattering coefficient, or sigma naught for two images related to before and after the flood occurrence was extracted. The backscattering coefficient histogram was used to separate the image into two classes including water and non-water and the threshold of 0.01 was obtained based on it. Then, by applying mathematical operations on both backscattering images, the binary image of water and non-water was prepared and the flooded areas were determined based on the difference between the two images. After detecting the flooded areas, Sentinel images were classified into three classes including waterbody before flood, flooded area and other lands using supervised classification algorithms. The results indicated the high accuracy of the Random Forest algorithm with kappa of 0.92 compared to other algorithms. By overlaying the land use and flooded areas maps, the inundation percentage for each land use was determined. According to the results, bare lands with 27.9 percent, residential land with 16 percent and rangelands with 12 percent had the highest inundation percentage, respectively.

Anzali Wetland in Iran as one of the most valuable wetlands registered in the Ramsar Convention is being destroyed by environmental factors and human activities. In the last two decades, among various satellite images, radar images have played a special role in wetland monitoring. Radar is an all-weather sensor and it is sensitive to surface roughness and moisture, they serve as a valuable source for quick and accurate monitoring of wetlands. However, similarities in backscattering coefficients of different wetland classes and relatively difficult processing – in comparison to optical images- are the most important factors that limit their application. In this study, the capabilities of SAR images in the classification of Anzali wetland and the three main land use classes around the wetland (i.e. agricultural lands, reeds, and built-up areas) were evaluated. Two radar images; Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) and Sentinel 1 captured in 2018 were used. The texture parameters of the two images have been extracted. The images and their extracted texture layers have been fused by the feature-level method and further classified by the random forest method. The overall accuracy of feature-level fusion is equal to 75% and the kappa coefficient is equal to 0.62. The evaluation results related to producer and user accuracy are 100% and 83.33%, respectively, show the high capability of radar images in the classification and detection of wetlands. However, some errors have been observed in the separation of agricultural lands, reeds, and built-up areas.

Minab plain in Hormozgan province has experienced population growth with agricultural development in recent decades. Low rainfall in recent years, successive droughts, and limited surface water resources, irregular irrigation practices in the agricultural sector in the study area, along with inappropriate cultivation patterns, have caused the extraction of water wells in the region. The extraction of these wells and groundwater resources has led to an imbalance in the aquifer in the area and a decline in groundwater levels. The consequences of this trend have caused the creation and expansion of subsidence in the region. This research, by investigating radar, satellite images and using differential radial interferometry, has been used to detect and determine the amount of subsidence in order to investigate the extent of this phenomenon in the study area. In this study, data from the Sentinel-1 refer to the dates 2014 and 2018 were used. The results showed it had 13-centimeter subsidence in the study period. After verifying the results by control points (in September 2018), in order to find the relationship of subsidence with changes in groundwater level, elevation, and slope, a spatial analysis was performed and the correlation of each of these factors with subsidence event density was calculated. Spatial autocorrelation analysis and Moran's index showed that climatic event due to water level changes in the study area was 0.925. The values ​​of 1.89 and 0.06 for standard normal distribution (z) and the p_value respectively, confirm a strong autocorrelation between the studied factors.

Recently, land subsidence due to natural and human factors changed to catastrophic destruction for the residential, agricultural and industrial areas. In this study, the high potential subsidence areas of Ardabil plain were identified to control and manage this phenomenon. Thus, the seven effective parameters on the subsidence were rated and weighted and the subsidence potential index (SPI) was calculated for study area. The SPI value for the Ardabil Plain was obtained between 80 and 154. Then the results of this method were verified by occurred subsidence which is obtained through INSAR image analysis for the study area. Although the results are acceptable but to increase the framework efficiency and accuracy of results, the fuzzy model adopted to optimize this framework. In this way, seven effective parameters on the SPI were used as the input of the FL model and the value of the corrected SPI by obtained observed subsidence from INSAR images was defined as the output of the model. The results of this method showed that the southern and southeastern parts of the plain have the high subsidence potential.

Differential synthetic aperture radar interferometry (D-InSAR) has become a useful technique for monitoring ground movement. The technique enables the analysis of very small ground movements in continuous and large areas and has the advantages of high accuracy, high resolution, all-weather adaptability, low cost and inaccessible area coverage. Thus, D-InSAR has been widely used for investigation of geologic hazards, such as subsidence, landslide, earthquake, and volcanic activity. In this research, D-InSAR technique was applied for detection of unstable slopes and determining moving displacement rate. For this purpose, eight SAR images of PALSAR sensor of ALOS satellite were selected for processing based on D-InSAR approach. Obtained results were validated by field observations prates. This paper is only represented results related to image pair processing of 5th July to 5th October 2007 with 92 days interval. Garm Chay watershed with 940 km2 area is located in North Eastern and 40 km far from Miyaneh in Eastern Azerbyjan, Iran. This region with 380 landslides is considered as one of the unstable landslide proven area in Eastern Azerbyjan. Occurrence of these slides caused heavy damages to rural properties and arable lands. As a result, Sovin village in north western part of this watershed was moved to other safesite. The results clearly showed that some old stabilized landslides were still active. Because of their proximity to local stream networks (check the possibility for stream displacement), they can be considered as important sources of sediment yield. Before mentioned period, the maximum displacement was calculated 5.8 cm in landslide surface near to Avin, Atajan and Benavaran villages. The results from this method should be validated by geodetic methods and field obsevations.