Iranian Journal of Remote Sensing & GIS
Year:2018 | Volume:10 | Issue:1|Papers Count:6

The issue of the mapping of rock units in an ever-expanding area has now reached a point where the detection and classification of rock units is carried out through the aid of hyperspectral image. In this research, Hyperion images are used in the light of the work of previous researchers and the application of the SAM supervised classification algorithm for the detection and separation of rock units in Khorramabad region, Lorestan province. After performing the necessary preprocesses including atmospheric correction performed by the FLAASH method, linear MNF transformation was used to determine the dimension of main image, to separate the noise from other information and reduce the processing level in the next stages, and the PPI algorithm to find the pixels that More purity is used in multispectral images. From the overlapping of pure pixels with rock units and based on ground data from the study area, the average range was extracted for each member. Then, these pure members were used as inputs for the above-mentioned algorithms and image categorization was used. Finally, the mapped classification of this method was compared with existing maps and land data and their accuracy was checked. The accuracy of the SAM method was verified by verifying the accuracy of the algorithm by calculating the error matrix with the highest of 68. 83% and kappa coefficient of 0. 49%, which indicates the importance of hyperspectral images and the SAM method in separating the rock units.

Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical models including: 3D Affine Model, First Order Rational Function Model with unequal denominator, SDLT, DLT, Rational Function Model with equal denominator, with the emphasis on the effect of linear and point features as control information to geometrically correct the high spatial resolution images. In addition, a new form of Pushbroom-Projective function is introduced, as a new idea for geometric correction of satellite images. The satellite images used in this research are GeoEye-1 from Urmia and Ikonos from Hamedan. Based on the results obtained, in the case of GeoEye-1 satellite image, First Order Rational Function Model with unequal denominator when using point features as control and +XY term of Rational Function Model with equal denominator when applying linear features as control reached the highest accuracy of 0. 75 pixel and 2. 03 pixel respectively. In the case of Ikonos satellite image, the +XY term of Rational Function Model with equal denominator when using control point features and First Order Rational Function Model with unequal denominator when using linear control features reached the accuracy of 0. 68 pixel and 1. 5 pixel respectively at the best. It is worth mentioning that the remaining systematic errors in the case of using linear features as control are always more than those obtained using point control features.

Flood is one of the most common and destructive natural events in the world. Conventional methods which aim to prevent floods, mostly are based on resistance approaches. Considering uncertainties about time and location of flood occurrence and design variables such as discharge, structure resistance and physical characteristics of a basin, resistance methods are not suitable solutions. However, resistance methods for preventing from the flood are effective on lower discharges, they may become less effective or ineffective in extreme-case emergencies. As an example, levees are only effective when flood waters remain below their design capacity. Therefore, in order to manage and reduce human casualties and financial losses, more suitable solutions based on resilience are introduced. Flood resilience is interpreted as the capacity to tolerate flooding to avoid disaster when undergoing-not preventing-flooding, or when physical damage and socioeconomic disruption still occur, the capacity to reorganize and recover quickly. Recovery is defined as assisting of communities affected by flood waters to achieve a proper and effective level of functioning. Resilience approach in water resources management plays an important role in flood risk management. In this study, several strategies of flood risk management with an emphasis on the concept of resilience have been evaluated. A case study was carried out on the Ghezel Ozan river, located in the Mahneshan basin. In order to model the flood, the data related to the topographic conditions of the river are adapted using the HEC GeoRAS extension in the ArcGIS. Then, the flood characteristics in the 25, 50, and 100-year return periods are estimated by the HEC RAS model. Flood flow modeling has been carried out based on eight different management strategies including resistance and resilience strategies based on structural and non-structural approaches. The comparison of these strategies is based on the values of resilient indicators including the amplitude, graduality and recovery rate. Indicators for the amplitude are the expected annual damage (EAD) and the expected annual number of casualties (EANC). The graduality is measured by comparing the relative increase of discharge in a river by the corresponding relative increase of damage. Recovery rate is a function of social, economic and physical condition. In this research, in order to quantify the recovery rate, it is presented as a function of evaporation, transpiration and water penetration into the soil. Finally, after calculating the resilience indicators for each scenario, in order to prioritize the scenarios, the entropy method is used for weighting and TOPSIS is utilized to prioritize the scenarios. According to the results, it has been observed that resilience based methods are preferred to resistance methods and dry farming with flood warning and flood insurance has been determined as the best strategy.

Tehran is subject to high-rate subsidence because of extravagant water extraction. Groundwater extraction in Tehran plain due to agricultural or industrial activities has made it always be at risk and probable incoming damages. Large spatial baselines and temporal de-correlation have always limits the use of the conventional SAR interferometry for the purpose of subsidence monitoring in regions with high deformations velocity. Therefore, in this research, the InSAR technique based on persistent scatterer (PS) is carried out to analyze Tehran subsidence. The main objective of this paper is to determine the average annual subsidence rate of some urban regions in Tehran using a time series of Sentinel-1A (S-1A) and ENVISAT-ASAR data. PS pixels remain coherent in long spatio-temporal intervals and thus less affected by the lack of radar images correlation. However, inappropriate temporal distribution of data in this technique makes it difficult to derive the absolute value of the phase due to an integer ambiguity. Therefore, the use of S-1A dataset with short temporal baselines would help identify the phase ambiguity. Results prove considerable subsidence in southern part of the case study area for all-time series analysis which further proves the arrival of subsidence to urban parts. Results are cross-validated using the different image tracks and besides, absolute validation are employed on subsidence velocity maps based on precise leveling and GPS observations.

Identification of dominant landforms is important in a landscape because they applicable in various types of urban planning, tourism planning, spatial planning, etc. In this study, the landforms of Yazd-Ardakan basin were identified by two visual and automatic methods and then were compared. In automatic method, were used by Multiresolution and Contrast Split image segmentation in the object based concepts for identification of geomorphological landforms. The results showed using “ Multiresolution Segmentation” due to consider the shape parameter is appropriate in the recognition of the landforms structure and their natural boundary such as alluvial fan but using the “ contrast split image segmentation” is appropriate for micro-landform recognition such as braided river at the surface alluvial fans. The results of the comparison of visual and automated landforms maps showed that the visual approach was only useful for macro-landforms such as mountain masses, types of pediments, Ardakan playa and were barely detectable dunes, But the object based automatic approach not only mentioned landforms but also smaller landforms were identified such as the transverse dunes, alluvial fans, badlands, inselbergs. To evaluate the accuracy of automatic landforms identification models were used both qualitative and quantitative methods; in the qualitative evaluation were used the overlay technique to the visual investigation of matching the map of the model with Google Earth images. The quantitative evaluation was used the confusion matrix. The results of the evaluations showed that Overall Accuracy and Kappa coefficient for the multiresolution algorithm in landforms recognition are 97. 46% and 96. 53% respectively. Also, Commission and Omission errors showed that the minimum identification errors are related to soft surfaces such as Plain, but the maximum of the identification errors are related to rough surfaces like mountainous.

Ecotourism is a part of the tourism industry that has attracted the attention of many officials and people in recent years and it is one of the levers of economic and social development of many developed and developing countries. Since the non-systematic activity of the ecotourism can negatively affect the environment, evaluating the ecotourism activities using valid scientific frameworks and methods, such as DPSIR, can be effective and useful in the managers’ planning of this industry. The main purpose of this research was to investigate the ecotourism status in Rudbar-e Qasran and Lavasanat Zone using the DPSIR framework. Each of the five sections of this evaluation model was analyzed and the findings were presented in the form of a table. According to the results from the classification of images in 2004 and 2016, the constructed spaces have increased from 3625 square meters to 8744 square meters. One of the reasons for this can be the increase in the population, proximity to the capital, the ease of commuting, the expansion of second homes, and increasing the construction of tourist-related service sites. The conducted evaluations and the obtained results of this research can be used as a decision support structure for managers and planners in this area to adopt appropriate strategies for implementing sustainable ecotourism.