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International Journal of Remote Sensing Applications
   [8 followers]  Follow    
  This is an Open Access Journal Open Access journal
     ISSN (Print) 2226-4361 - ISSN (Online) 2226-4353
     Published by Science and Engineering Publishing Company Homepage  [46 journals]
  • Cellular Automata vs. Object-Automata in Traffic Simulation

    • Abstract: Cellular Automata vs. Object-Automata in Traffic Simulation
      Pages 61-69
      Author Saeed BehzadiAli.A. Alesheik
      Cellular Automata (CA) recently has been used in variety of fields related to continuous areas such as Geospatial Information System (GIS), traffic, air pollution, and so on. Having been used only in continuous raster-based area is the most weakness of CA, so defining the shape of the cellular and their adjacency for the study area is always a big challenge for researchers. Despite CA, agent-based modeling is complex and progressive type of intelligent object. Agent-based model is used in variety fields. Having efficiency such as moving makes it extremely distinctive from the concept of static intelligent entity. In this paper, at first, an intermediate type of object named Object-Automata (OA) was defined based on the simplicity of CA and complexity of agent which is suitable for both continues and discontinues area. Secondly, to asses and investigate the proposed OA, the simulation of traffic was implemented by OA and the model was compared with CA. As roads are defined as discrete entities, OA is much more suitable for such problem. At the end of this paper, the rules about the problem were described.
      PubDate: 2014-03
       
  • Spectral Unmixing Evaluation for Oil Spill Characterization

    • Abstract: Spectral Unmixing Evaluation for Oil Spill Characterization
      Pages 1-17
      Author Vassilia Karathanass
      Hyperspectral remote sensing exploits the optical properties of materials and provides detailed information about them. From a theoretical point of view, in case of oil spills, it cannot only detect and delineate them, but also provide information about the oil type and oil thickness, significantly contributing at the remediation stage of clean-up. In practice, many factors, either associated with the inherent characteristics of oil spills (oil type, quantity, weathering stage, etc.), or with environmental factors (sea bottom cover and depth, waves, etc.) affect the spectral signature of the oil, set constraints on the effectiveness of hyperspectral methods. In this study, the key factors that enable an airborne hyperspectral campaign to implement effective surveys for oil spill detection and characterization are investigated. Additionally, the study focuses on the assessment of environmental and slick parameters for which spectral unmixing-based methods successfully address the problem of oil spill detection and oil type and thickness estimation. For this purpose, study of the spectral behavior of the oil through laboratory measurements and measurements in the complex marine environment was a prerequisite and has initially been carried out. The results showed that almost all the measured spectral signatures as well as their variations can be extracted as endmembers from synthetic images using the unmixing theory. Consequently, laboratory spectral libraries could enable the labeling procedure during the spectral unmixing application on hyperspectral imagery. Unfortunately, oil spectral measurements implemented in marine environment were significantly different because they were affected either by sea bottom contributions (case of light oil and petroleum products) or by sea state conditions which cause high dispersion of oil and spatial variation in oil spill thickness (case of heavy oil and petroleum products). When airborne hyperspectral imagery is processed, it has been found that transparent clouds significantly affect the efficiency of unmixing methods for thin oil spill detection. Their removal, as well as atmospheric correction is strongly recommended. Applying spectral unmixing-based methods on hyperspectral imagery, oil spill detection is effective even in the marginal case of sheens. The results showed that all types and thicknesses of oils can be detected independently of seawater depth through the differences that their spectral signatures present in the wavelengths between 720 μm and 1000 μm. For oil sheens, a single endmember is usually extracted, which leads to relative thickness estimation. For thicker oil spills, many endmembers are extracted each one corresponding to a different thickness and/or emulsion. Further research based on an extended spectral library of measurements in marine environment should be performed in order to enable spectral unmixing-based methods to accurately estimate the oil type, the oil to water ratio of an emulsion as well the oil thickness.
      PubDate: 2014-03
       
  • Monitoring Water Pollution of Lake Maryout on the Mediterranean Coast of
           Egypt

    • Abstract: Monitoring Water Pollution of Lake Maryout on the Mediterranean Coast of Egypt
      Pages 36-40
      Author Mona F. KaiserSamah Ahme
      The present study assessed serious eutrophication and pollution levels in Lake Mayout, Egypt. Chlorophyll spatial distribution and corresponding water surface temperatures in polluted water were extracted from 1986-2012 satellite images. Results showed a significant positive correlation coefficient (R2 = 0.99) between thermal water and chlorophyll abundance. Five classes were differentiated, including cultivated land, algal blooms, urban areas, and sea water from the two dimensional scatterplot between chlorophyll abundance and surface temperatures. Markedly high temperatures and very high NDVI values characterized cultivated land, while clean water exhibited low temperatures, and low NDVI. High surface temperatures and low NDVI were observed in planned urban areas. Finally, high surface temperatures and NDVI values characterized algal bloom polluted waters. Results showed surface areas exhibiting algal bloom were 29.2 km2 and water surface temperatures were 22ᵒC in 1986, and respectively increased to 32.5 km2 and 24ᵒC in 2012.
      PubDate: 2014-03
       
  • Spatial and Multi-Temporal Change Analysis of the Niger Delta Coastline
           Using Remote Sensing and Geographic Information System (GIS)

    • Abstract: Spatial and Multi-Temporal Change Analysis of the Niger Delta Coastline Using Remote Sensing and Geographic Information System (GIS)
      Pages 41-47
      Author Chituru D. ObowuTamuoene K. S. Aba
      The Niger Delta is one of the most dynamic deltas in the world. It is experiencing relatively strong environmental changes resulting from the complex interaction of natural and human-induced processes that operate upon it. The research focuses on the Niger Delta coastline change detection and vulnerability assessment to coastline changes using remote sensing and geographical information system. The change detection involves, processing of multi-temporal images (1972-2008), followed by image differencing, post classification image overlaying, image fusion, image visual interpretation and on-screen digitising. The result shows that the image differencing and post-classification image overlaying change detection techniques are useful to monitor coastline change. The image visual interpretation and on-screen digitising is the main quantitative method to detect the Niger Delta coastline change. The coast line was analyzed in sections starting and terminating a major river mouths. Quantitative measurement and analysis showed that for most of the coast line sections there have been period of erosion and accretion over the 36 year study period. Only very few sections show consistent erosion or accretion over the years. The natural fluvial and marine factors and also human activities played an important role on this development.
      PubDate: 2014-03
       
  • OIF Based Indeces Oriented Ecological Classification Using LANDSAT TM
           Digital Data – A Case Study on Beluchary and Dhulibasan Island
           Groups, Sunderban, West Bengal, India

    • Abstract: OIF Based Indeces Oriented Ecological Classification Using LANDSAT TM Digital Data – A Case Study on Beluchary and Dhulibasan Island Groups, Sunderban, West Bengal, India
      Pages 56-60
      Author Ratnadeep RayAshis Kr PaulBalen Bas
      The classification of vegetation from remotely sensed data has long attracted the attention of remote sensing community as the results are fundamental sources for many environmental applications. There are different approaches and techniquesto improve the classification accuracy. However, different uncertainty or errors may be introduced into classification due to many factors like complexity in the landscapes under investigation, selected remotely sensed data, image processing approaches, the availability of reference data etc. So much efforts should be devoted to identify these major factors in the image classification processes and then to improve them. In the present study, different vegetation indices (VIs) have been adopted for the betterment of vegetation classification accuracy. The analysis of correlation and standard deviation of each VI was used to identify the best combination for the separability analysis. The selection of the best combination was done using Optimum Index Factor technique based on the total variance within bands and correlation coefficient between bands. The OIF technique was applied to all the calculated seven VIs. A number of twenty one colour combinations were produced and analyzed using OIF. The combination having the highest OIF value has been selected for the classification in which a distinct spectral dissimilarity has been observed, which is very helpful for information extraction. Finally overcoming the spectral self similarity, after classification five ecological classes has been got from the Beluchari and Dhulibasan islands. Finally the technique of OIF has been successful in conclusively deriving the five ecological classes in Beluchari and Dhulibasan Islands by overcoming the spectral self similarly.
      PubDate: 2014-03
       
  • Remote Sensing and Unmanned Aerial System Technology for Monitoring and
           Quantifying Forest Fire Impacts

    • Abstract: Remote Sensing and Unmanned Aerial System Technology for Monitoring and Quantifying Forest Fire Impacts
      Pages 18-35
      Author Michael G. WingJonathan D. BurnettJohn Session
      Fire is a regular occurrence throughout the world’s forested landscapes and affects millions of hectares annually. A variety of remote sensing applications have been developed toquantify wildfire impacts in forests with varying success. Remote sensing technology applications for wildfires have typically involved quantifying burn severity, fuel levels, and forest resource recovery following burn. Wildfire remote sensing applications have recently included active or real-time technology applications for mapping burn impacts and wildfire detection and monitoring. A review of traditional remote sensing applications are presented in this paper to quantify fire impacts, tracking vegetation recovery, establishing fuel conditions, and fire detection and monitoring in forested landscapes; then recent examples of active and real-time remote sensing techniques were examined to monitor fire ignition and behavior, followed by an assessment of potential future applications. The application of unmanned aerial systems (UAS’s) with both spectral and thermal sensors may hold great promise for future remote sensing applications related to forest fires.
      PubDate: 2014-03
       
  • Application of Remote Sensing and Geographic Information Systems for Gold
           Potential Mapping in Birim North District of Eastern Region of Ghana -Gold
           Potential Mapping Using GIS and Remote Sensing

    • Abstract: Application of Remote Sensing and Geographic Information Systems for Gold Potential Mapping in Birim North District of Eastern Region of Ghana -Gold Potential Mapping Using GIS and Remote Sensing
      Pages 48-55
      Author Clement KwangE.M. Osei JnrA.A. Duke
      Remote Sensing and Geographic Information System (GIS) have played an active role in mineral exploration by helping in the identification or discovery of new gold deposits in most part of the world such as Spain, Nova Scotia (Canada) and Egypt. Different authors have used Remote Sensing and GIS in exploring minerals deposits. Birim North District of the Eastern Region of Ghana is one of the gold-mineralized districts but there is no gold potential map covering the whole district. This research work was aimed at producing a gold potential map covering the whole of Birim North District through the use of Remote Sensing and GIS technique. The Landsat Enhanced Thematic Mapper (ETM+) image of the Birim North was processed by applying the clay-mineral ratio (Band 5 to Band 7) and the principal component analysis. The result was further processed to obtain the alteration map of Birim North District which represented the altered rocks associated with gold-mineralization. The Aeromagnetic image of the same area was enhanced by using the Edge Detection Directional Filter and later digitized manually on-screen to produce the lineament map of Birim North District. These results obtained from the Remote Sensing processes were integrated into GIS environment with other geospatial datasets such as the soil geochemical data and geophysical data. The Arc-weight of evidence was used as the spatial data integration model in the prediction of the potential gold areas. A total of 250 known gold deposits was used, 180 were used as training samples and 70 were used for the validation. The results obtained from the research work indicated that the best predictors of the new gold deposits were the soil geochemical data, geophysical data and the lineament. The alteration was the least predictor. The gold potential map demarcates 158 km2 (i.e., 32%) of the total of 497 km2 as favourable for the occurences of the gold deposits within the study area. The gold potential map also has a success rate of 88% (i.e, the percentage of the training deposits or points in the predicted favourable gold deposits zones) and a prediction rate of 83% (i.e, the percentage of the validation deposits or points in the predicted favourable gold deposits areas). Many of the mining communities and Newmont Ghana Gold limited mine area were found in the areas associated with relative higher posterior probabilities.
      PubDate: 2014-03
       
 
 
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