Risk Assessment of Vegetation Degradation Using Geographic Information System: A Case Study of Qareh Aghaj Basin,Iran     

Masoud MASOUDI  Parviz JOKAR 《资源与生态学报(英文版)》2018,9(5):477-483

The entire land of Southern Iran faces problems arising out of various types of land degradation of which vegetation degradation forms one of the major types. The Qareh Aghaj basin (1 265 000 ha), which covers the upper reaches of Mond River, has been chosen for a test risk assessment of this type. The different kinds of data for indicators of vegetation degradation were gathered from the records and published reports of the governmental offices of Iran. A new model has been developed for assessing the risk of vegetation degradation. Taking into consideration nine indicators of vegetation degradation the model identifies areas with “Potential Risk” (risky zones) and areas of “Actual Risk” as well as projects the probability of the worse degradation in future. The preparation of risk maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. By fixing the thresholds of severity classes of the nine indicators a hazard map for each indicator was first prepared in GIS. The risk classes were defined on the basis of risk scores arrived at by assigning the appropriate attributes to the indicators and the risk map was prepared by overlaying nine hazard maps in the GIS. Areas under actual risk have been found to be widespread (78%) in the basin and when the risk map classified into subclasses of potential risk with different probability levels the model projects a statistical picture of the risk of vegetation degradation.  相似文献   

4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station,case study: northwest of Iran     

Zohre Adavi  Masoud Mashhadi-Hossainali 《Meteorology and Atmospheric Physics》2014,126(3-4):193-205

Iran enjoys a variety of climatological conditions. Moreover, numerical weather prediction (NWP) models are not assimilated with the meteorological data in Iran, the country suffering from poor spatial and temporal resolution of radiosonde measurements. These facts make modeling of troposphere impossible using the measurements and NWP. On the other hand, the global positioning system (GPS) has been emerged as a valuable tool for modeling and remote sensing of Earth’s atmosphere. This research is the first attempt to address the tropospheric wet refractivity modeling by GPS measurements in Iran. Changes of topography in the study area are taken into account. As a leading work, virtual reference stations (VRS) are used to fix the rank deficiency of the problem. The model space resolution matrix is used to achieve the optimum spatial resolution of the tomographic model and the optimum number of VRS stations. The accuracy of the developed model (KNTU1) is investigated by deploying radiosonde measurements.  相似文献   

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects     

Masoud Monjezi  Hasan Ali Mohamadi  Bahare Barati  Manoj Khandelwal 《Arabian Journal of Geosciences》2014,7(2):505-511

In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.  相似文献   

Thresholds of land cover to control runoff and soil loss     

Masoud Eshghizadeh  Ali Talebi  MohammadTaghi Dastorani 《水文科学杂志》2018,63(9):1424-1434

This research focused on the determination of land cover thresholds that have a significant impact on runoff generation and soil loss at the pedon scale. For this purpose, six erosion micro-plots were set up on grassland and shrubland types of rangeland in the northeast of Iran, and the amounts of vegetation cover, litter, runoff and soil loss on them were measured. A factorial statistical analysis was carried out on the completely randomized design using land cover and rainfall factors. The results show that the effect of rainfall on soil loss and runoff was greater than that of land cover. Also, the effect of land cover on soil loss was greater than that on runoff generation. Furthermore, two specific thresholds were identified: the first was from 10 to 30% of landcover and the second from 50 to 70%.  相似文献   

Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production     

Seyyedattar  Masoud  Zendehboudi  Sohrab  Butt  Stephen 《Natural Resources Research》2020,29(3):2147-2189

Natural Resources Research - Offshore oil and gas reservoirs comprise a significant portion of the world’s reserve base, and their development is expected to help close a potential gap in the...  相似文献   

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine     

Bayat  Parichehr  Monjezi  Masoud  Rezakhah  Mojtaba  Armaghani  Danial Jahed 《Natural Resources Research》2020,29(6):4121-4132

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...  相似文献   

Discriminating Weathering Degree by Integrating Optical Sensor and SAR Satellite Images for Potential Mapping of Groundwater Resources in Basement Aquifers of Semiarid Regions     

Magaia  Luís André  Koike  Katsuaki  Goto  Tada-nori  Masoud  Alaa Ahmed 《Natural Resources Research》2019,28(3):1197-1215

Natural Resources Research - Unlike in coastal and sedimentary basins, regional-scale exploration of groundwater resources using only geophysical methods is costlier in consolidated rocks such as...  相似文献   

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms   总被引：2，自引：0，他引：2  

Alireza ARABAMERI  Biswajeet PRADHAN  Khalil REZAE  Masoud SOHRABI  Zahra KALANTARI 《山地科学学报》2019,(3)

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.  相似文献   

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada     

Fariba Mohammadimanesh  Bahram Salehi  Masoud Mahdianpari  Jerry English  Joseph Chamberland  Pierre-Jean Alasset 《地理信息系统科学与遥感》2019,56(4):485-510

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.  相似文献