scholarly journals Detecting Land use Changes Affected by Human Activities using Remote Sensing (Case Study: Karkheh River Basin)

2015 ◽  
Vol 10 (2) ◽  
pp. 473-481 ◽  
Author(s):  
Saeid Maddah ◽  
Saeed Karimi ◽  
Hadi Rezai ◽  
Jabbar Khaledi
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Cost Effective ◽  
Well Being ◽  
Likelihood Method ◽  
Support Vector ◽  
Running Water ◽  
Land Water

Population growth and abundant activities in order to achieve maximum well-being has forced human to make a lot of changes in the nature. These changes will be cost-effective when they have the minimum damage on the landscape. One of the activities that human did for obtaining the water and preventing flood was making the dam in the track of running water. Since the dam is established until its impoundment and after impoundment, the condition of ecosystem and the appearance of the upstream and downstream of the dam will undergo changes. In this study, using satellite data and remote sensing, these changes have been studied and the landuse changes in vegetation, arid land, water level and residential and non-residential lands is measured in 1998 and 2014 using Maximum Likelihood method and support vector machine.

scholarly journals Land Use Change Detection and Prediction Using Markov-CA and Publishing on the Web with Platform Map Server, Case Study: Qom Metropolis, Iran

2018 ◽  
Author(s):  
Mojtaba Pirnazar ◽  
Nasrin Haghighi ◽  
Donya Azhand ◽  
Dr. Kaveh Ostad-Ali-Askari ◽  
Professor Saeid Eslamian ◽  
...  
Keyword(s):  
Land Use ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Kappa Coefficient ◽  
Likelihood Method ◽  
Land Cover Changes ◽  
Ca Model ◽  
Control And Management ◽  
Server Application

To achieve sustainable development, detailed planning, control and management of land cover changes that occurs naturally or by human caused artificial factors, is essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55 % and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used that after assessing the accuracy, the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. ThenWebGis application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website Css, HTML and JavaScript languages were used. HTML is responsible for creating the foundation and overall structure of webpage but beautify and layout design is on Css.

scholarly journals Land Use Change Detection and Prediction Using Markov-CA and Publishing on the Web with Platform Map Server, Case Study: Qom Metropolis, Iran

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Mojtaba Pirnazar ◽  
Nasrin Haghighi ◽  
Donya Azhand ◽  
Kaveh Ostad-Ali-Askari ◽  
Saeid Eslamian ◽  
...  
Keyword(s):  
Land Use ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Kappa Coefficient ◽  
Likelihood Method ◽  
Land Cover Changes ◽  
Ca Model ◽  
Control And Management ◽  
Server Application

To achieve sustainable development, detailed planning, control and management of land cover changes that occurs naturally or by human caused artificial factors, is essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55 % and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used that after assessing the accuracy, the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. ThenWebGis application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website Css, HTML and JavaScript languages were used. HTML is responsible for creating the foundation and overall structure of webpage but beautify and layout design is on Css.

scholarly journals Land use change detection and prediction using Markov-CA and publishing on the web with platform map server, Case Study: Qom Metropolis, Iran

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mojtaba Pirnazar ◽  
Nasrin Haghighi ◽  
Donya Azhand ◽  
Kaveh Ostad-Ali-Askari ◽  
Saeid Eslamian ◽  
...  
Keyword(s):  
Land Use ◽  
Maximum Likelihood Method ◽  
Land Use Changes ◽  
Web Gis ◽  
Likelihood Method ◽  
Gis Application ◽  
Ca Model ◽  
Control And Management ◽  
Server Application

To achieve sustainable development, detailed planning, control and management of land cover changes that occur naturally or by human caused artificial factors, are essential. Urban managers and planners need a tool that represents them the information accurate, fast and in exact time. In this study, land use changes of 3 periods, 1994-2002, 2002-2009, 2009-2015 and predictions of 2009, 2015 and 2023 were assessed. In this paper, Maximum Likelihood method was used to classify the images, so that after evaluation of accuracy, amount of overall accuracy for images of 2013 was 85.55% and its Kappa coefficient was 80.03%. To predict land use changes, Markov-CA model was used after assessing the accuracy, and the amount of overall accuracy for 2009 was 82.57% and for 2015 was 93.865%. Then web GIS application was designed via map server application and evoked shape files through map file and open layers to browser environment and for design of appearance of website CSS, HTML and JavaScript languages were used. HTML is  responsible for creating the foundation and overall structure of webpage but beautifying and layout design on CSS.

scholarly journals ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE

2016 ◽  
Vol XLI-B8 ◽  
pp. 73-77
Author(s):  
Ge Han ◽  
Wei Gong ◽  
Xiaohui Cui ◽  
Miao Zhang ◽  
Jun Chen
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Rough Set Theory ◽  
Evaluation Framework ◽  
Likelihood Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Ambient Atmosphere ◽  
Electrical Insulator ◽  
Severity Levels

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

scholarly journals Bayesian Estimation of the Limiting Availability in a Repairable One-Unit System

2019 ◽  
Vol 42 (1) ◽  
pp. 223-143
Author(s):  
Víctor H. Salinas Torres ◽  
Cristián A. Vásquez ◽  
José S. Romeo
Keyword(s):  
Prior Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Coherent System ◽  
Repairable System ◽  
Unit System ◽  
System A ◽  
Informative Prior ◽  
Informative Prior Distribution

 This work presents a Bayesian approach for estimating the limiting availability of an one-unit repairable system. A Bayesian analysis is developed considering an informative prior and a less informative prior distribution, respectively. Simulations are presented to study the performance of the Bayesian solutions. The maximum likelihood method is also revisited. Finally, a case study is considered, the Bayesian methodology is applied to estimate the limiting availability of a palletizer, which is used in the packaging of glass bottles. Extensions to a coherent system are also discussed.

scholarly journals Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Geofizika ◽  
2020 ◽  
Vol 37 (2) ◽  
pp. 131-156
Author(s):  
Vahdettin Demir ◽  
Asli Ülke Keskin
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Two Dimensions ◽  
Water Levels ◽  
Likelihood Method ◽  
Roughness Coefficient ◽  
Sieve Analysis ◽  
Flood Modeling ◽  
Manning Roughness

Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

scholarly journals Identification of environmental conditions based on the carrying capacity and holding capacity analysis (case study: Kecamatan Barat, Magetan)

2021 ◽  
Vol 778 (1) ◽  
pp. 012022
Author(s):  
H Idajati ◽  
E Umilia ◽  
F U Nurliyana ◽  
R Sianturi
Keyword(s):  
Environmental Conditions ◽  
Carrying Capacity ◽  
Descriptive Analysis ◽  
Land Use Changes ◽  
Population Increase ◽  
Capacity Level ◽  
Study Results ◽  
Qualitative Descriptive ◽  
Land Water

Abstract A rapid increase in population and unsustainable land use changes that are not following its capability are likely to lead to severe urban problems. An analysis of the carrying capacity and holding capacity of the environment is needed to determine whether lands can sustain the increasing population. The present research aims to identify the environmental conditions of the Kecamatan Barat based on the carrying capacity and holding capacity of land, water, and demography. Further, an overlay analysis is used to understand the overall carrying capacity level of the sub-district. Qualitative descriptive analysis is used to elaborate the study results. Two villages have deficits in the carrying capacity and capacity of land in West District in 2020, including Purwodadi and Tebon. Six villages are experiencing a water deficit in 2020, including Purwodadi, Karangsono, Bogorejo, Tebon, Manjung, and Mangge. All villages in Kecamatan Barat are still able to accommodate the projected population increase until 2042. The overlay of land, water, and demographic carrying capacity and water holding capacity shows 8, 4, and 2 villages with very high, high, and low overall carrying capacity values.

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