Iranian Journal of Remote Sensing & GIS
Year:2019 | Volume:11 | Issue:3|Papers Count:7

Plants are one of the most important components of the ecosystem which are affected by natural and human factors. Therefore, the study of net primary production (NPP) is one of the main subjects in ecology. The main purpose of this research was to model spatial and temporal distributions of NPP and also to determine the degradation of vegetation types using Carnegie Ames Stanford Approach (CASA), Rain Use Efficiency (RUE) and Light Use Efficiency (LUE) models in semi-steppe rangelands of Isfahan Province. For this purpose, the 16-day MODIS NDVI images, metrological data, land cover maps and field study were applied in the study area. The results showed that the NPP rate increased from March (11. 44gC/m2/mo) to May (41. 07gC/m2/mo) while demonstrating a decreasing trend from the onset of June (2. 2 g C/m2/mo) due to soil dryness. Climate, vegetation type and rangeland conditions had important roles in annual plant NPP and therefore the highest and lowest NPP were observed in Astragalus-Daphnae (38. 85 gC/m2 y-1) and Artemisia sieberi-Scariola (4 g C/m2 y-1) vegetation types with maximum (0. 13 g C (MJ)-1) and minimum (0. 005 g C (MJ)-1) LUE, respectively. The amount of RUE decreased in degraded rangelands. Moreover, the correlation between field measurements and the CASA model decreased in semiarid warm climate and degraded rangelands. Therefore, rangeland conditions, vegetation type and climate condition must be taken into consideration in NPP monitoring and rangelands management.

Generally, the photos captured by drones and satellites include both natural scenes and man-made objects. Having these two categories classified, we will be able to extract important information from aerial scenes such as the shapes and the alignments of the structures and then, create labeled aerial images accordingly. Obtaining such information is of great interest in, for example, military, urban, and environmental protection applications. However, due to a huge amount of data that is collected in form of images, it seems that manually processing of such data is impossible. Therefore, employing automatic techniques based on artificial intelligence has become more on demand. There are numerous researches on this topic from which detection of buildings, vehicles, roads, and vegetation are of more interest. In this paper, we aim to introduce a method to detect man-made objects in aerial images based on a new set of color statistical features, which can be easily extracted, together with a learning model. Experimental results on a publicly available dataset, Massachusetts dataset, have shown promising results in terms of both accuracy and processing time; the accuracy and the average processing time are 90. 07% and 0. 96 seconds, respectively.

Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological surveys. The main issue of this research is to assess the ability of the logistic regression model to predict the dispersal of ancient sites in the Harsin-Bisotun plain. Predictor variables for this study include the environmental variables of slope, height, distance to river, vegetation, distance to modern cities, density of modern villages and distance to main roads, and dependent variable is the most turbulent of area in terms of existence prehistoric Archaeological sites. For modeling, using logistic regression, GIS and IDRISI softwares was used. By analyzing the results of the logistic regression model, results showed that, the logistic regression model was successful in prediction the dispersion of ancient sites in the Harsin-Bisotun plain. As well, the introduction of densly populated areas due to the presence of ancient sites as a modle-dependent variable in the plain-bound areas is more important than the mere introduction of GPS points of the ancient sites as a dependent variable. and, Accordingly, the cultural variability of village density in the Calcolithic Age, village density, distance to cites and the distance to main roads in the Bronze Age and distance to cites, distance to main roads in Iron Age have had the greatest impact in prediction the dispersion of ancient sites.

Finding the optimum location of wind turbines for the proper use of wind energy, as one of the sources of renewable energy, is very important. Determining the location of wind turbines has a great influence on the efficiency of their equipment. So far, research in the Khorasan Razavi area has limited to the statistical analysis of wind speed and directional data while the spatial criteria affecting finding the optimum location of wind turbines was rarely considered. This research seeks to find optimum location for the wind power plant using environmental (including slope, altitude, distance from the fault, distance from the river, distance from protected areas), technical (including average wind speed, wind congestion and wind density), and economic (including distance from the city, the village, the airport, the natural resources and from the road) criteria and using ANP, ANP-DEMATEL and ANP-OWA multi-criteria decision-making methods. The aim is to evaluate the results of each method and produce a land suitability map for the construction of wind power plants in different decision scenarios. Based on this, using the analysis tools of the geospatial information system, the land suitability map is produced based on the aggregation of the mentioned criteria and the results of utilizing the mentioned decision-making methods are compared. By considering land suitability maps, it has been determined that in different scenarios of decision making, the southeastern province has the most potential for wind power plant construction. The results of this research indicate that the proposed method provides a good tool for choosing the right place to build a wind power plant.

Nowadays remote sensing science is widely used in the earth sciences. One of these branches is the identification, investigation and interpretation of the surface structures of the earth. The study of the fracture systems, the recognition of the geometry and spatial distribution of them, could helped to development of the oil fields. In this research, using Landsat 8 satellite imagery data, STA algorithm and remote sensing processing techniques, geometric analysis of the surface structures in the Siyahmakan oil field has been investigated. Then, the ground surface temperature (LST), calculated by Landsat project science office model for the oil field, and finally examined the relationship between the distribution of surface fractures and the surface temperature distribution pattern of the study area. The results show that the parallel axial (SA) and shear fractures(SO1), that have the N-S and NW-SE trend ordinary, have the highest frequency. According to the density map, the density of the lineaments in the mid-zone to the southeast of the field is the highest. Since underground resources are located along the tectonic lineaments near the surface, these structures have seen cooler and darker in the satellite images of the surrounding areas. Structural lineaments have shown conformity with surface temperature map so that lineaments often located in the regions with low to medium and medium – level Temperatures. Therefore low temperatures correspond to the highly fractured areas.

The capacity of passenger and goods shipment in a railway network is affected by various parameters. Considering the high cost of railway construction, optimum utilization of capacity in a railway network can help to improve the efficiency of network. Therefore, the purpose of this study is spatial calculating the capacity of railway network of Iran based on the effective parameters. In this paper using the transportation data of trains, a spatial software is developed for calculating the capacity of the railway network. Then, the outputs are compared and evaluated with the daily and real time data of the freight trains performance. In the next step, the amount of capacity utilization of each route and the amount of capacity remaining in each route and block is determined. Based on the analysis, the capacities of the selected passenger double-line route from Semnan to Shahrud, freight single-line route from Yazd to Bafgh and combination single-line route from Arak to Dorood were calculated 2. 6 for Semnan to Shahrud path and 2. 9 for return path, 13. 6 and 12. 6 (trains pair/day) respectively. Considering the common calculations, the online calculation with ability connection to related databases and the possibility of exchanging spatial web-based services with the different software, can improve the speed and accuracy of the railway network capacity calculation. On the other hand, in the equation of calculating the capacity (Scott equation) used in Iran, it is common that the adjustment coefficient of passenger train is determined by experts of transportation, while in this paper, since the data of each path is accessible, the aforementioned coefficient is calculated by the ratio of passenger trains number to their maximum number during the considered period of time. The outcome coefficient compared with the coefficient determined by experts, and the result was acceptable. Finally, with access to outputs in the GIS environment, the management solutions were proposed for optimum using the remaining capacity, enhancing the capacity in some parts of the network as well as eliminating the bottlenecks of the railway network.

In this study, processing and interpretation methods in remote sensing such as visual and spectral analysis have been performed on the EO-1, ASTER and ETM+ data from Meshkinshahr North area, and as a result, the alteration zones in the area have been identified. Then result Aeromagnetic data, using geological information, alteration and mineralization from the area. Development of advanced tools in remote sensing and geophysical exploration during recent decades indicates the necessity and importance of these tools in industry. For this purpose, a variety of image processing methods are used Aeromagnetic methods have an important role for exploration of metallic ore deposits. To achieve good results from these methods. In order to identify alteration zones, image processing methods such as PCA (principal component analysis), SAM (spectral angle mapping) and MTMF (Matched Filtering MF) using ENVI software were applied on the Hyperion EO-1, ASTER and ETM+ images from the study area. After removal of the noise from observed magnetic data, processing steps were considered, including IGRF subtraction for the proper years, reduction to pole, Signal Analytic, Tilt (TDR), THDR, and upward continuation 1000 meters. Identification of alteration zones in the study area using remote sensing and image processing methods, and interpretation of the geophysical Aeromagnetic results using geological and Mineralization and Hot Springs and Faults information in the area have been led to the identification of Alteration zone. Many Anomaly and Alterations Kaolinite and silica located in the Meshkinshahr north area (northwest Sabalan) and the other many situated in the northwest Sarab. For credibility of results, samples were taken and analyzed by XRD methods. Confirmed the results of remote sensing and aeromagnetic processes. Conclusions of this research revealed that applying concurrency both the remote sensing and aeromagnetic data could be led to improve the precision of the results.