جستجوی مقالات مرتبط با کلیدواژه « زنجیره مارکوف » در نشریات گروه « محیط زیست »

Predicting future landuse changes is an important step in the proper planning and management of watersheds. Therefore, in this study, the land use modeling of the Doroodzan Dam watershed was discussed. First, the land use maps of 1996, 2005, 2016 and 2021 were extracted using Envi software, and the maximum likelihood method. The transition potential maps were modeled for each of the sub-models by using multi-layer perceptron artificial neural network and 6 variables and Markov chain was used to calculate the allocation change to each land use. Then, the land use map for 2021 was predicted using Land Change Modeler (LCM) and 2005-2016 calibration period. To verify modeling accuracy, Hits 1.92%, Misses 8.8%, False Alarms 2.94% were calculated. The ratio of Hits to the total pixels has changed 14% indicates that model results are acceptable. Then, the land use map for the year 2050 was predicted. The results showed that given the predicted LC for year 2050 in comparison with the year 2021, bare land, agricultural land, residential areas and orchards will increase by 54290, 7621, 4494, and 2391 ha, respectively. Whereas grassland and forest cover will decrease 68441, and 689 ha, respectively.

Assessing and estimating the high-accuracy transmission potential is an important step in the process of land use and land cover changes modeling and predicting. The aim of this study is to investigate the transmission potential of land use and land cover changes using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods.

The land use and land cover maps for a 30-year period (1985-2015) were prepared using Landsat 5 and 8 satellite imagery. Land use and land cover transmission potential modeling was done using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods and effective variables in the process of change. The accuracy of the results obtained from the models was determined by comparing with ground reality map for mentioned year.

The Kappa coefficient of Similarity Weighted Instance based Learning, Logistic regression and Geomod were 0.84, 0.76 and 0.67, respectively. The investigating predicted maps for 2030 prepared by Similarity Weighted Instance based Learning and Markov chain showed that the area of residential areas, gardens and agricultural lands is increasing and the area of bare land, forests, pastures and water resources will have a decrease trend.

Finally, the results indicate a relatively high accuracy of three methods in estimating the transmission potential for land use and land cover changes, but according to the kappa coefficients, the accuracy of Similarity Weighted Instance based Learning method more than the other two methods.

Natural habitats such as rangelands and forests as one of the most sensitive elements of the environment and an important role in living organisms, including humans. In the decades before the present era of urban development has been such that an imbalance in the use of urban land led and pristine converted to urban land in others. In the present study to understand the changes in urban land, pasture and forest in Behbahan, images Landsat ETM + sensor OLI 2000 and 2014 were analyzed. CROSSTAB was used to assess the changes occurred. As well as to predict the trends of the year 2028 from the transmission potential maps logistic regression and Markov chain method was used. Results of the survey area in the first period (2000-2014) shows that urban land area of 1605 hectares in 2000 to 3157 ha in 2014 has increased. The most degradation of rangelands (6233 ha) and forests has occurred. The second period (2014-2028) compared to the 2014forest area remains unchanged, but increased urban development and reducing the area of pastures in Outlook 2028 will continue. In the period studied, the destruction of agricultural land for conversion into residential areas has increased significantly so that during 2000 to 2014, 291 hectares and in 2028, 626 hectares of agricultural land for construction purposes have been demolished.

Updated land use change information is one of the necessary requirements for natural resource management. This research used LCM mol to tect and predict land use changes in Lahijan city in Guilan province during 1984-2020. Initially, Landsat satellite images of 1984, 2000, and 2016 were categorized into five classes of forest, rangeland, agriculture, human ma areas and water resources. Then elevation, slope, precipitation, distance from resintial areas, distance from agricultural lands, distance from rangeland, distance from forest margin and distance from roads were used as affecting factors on land use changes. Mol accuracy assessment was done by comparison of actual and predicted map of 2016. Accuracy assessment of prediction mol was done by comparison of actual and predicted maps of 2016. The values of Null success, Hits, Misses and False Alarms were 88.04, 2.65, 8.45 and 4.28%, respectively. Meanwhile, in the present study, the error of the prediction mol was about 12.6%, which confirmed the efficiency and ability of the mol. Results from 1984 to 2016 indicated that a significant area of forest cover was creased and the forestation will continue according to these observations. Regarding the process of forestation in the study area, the implementation of land use planning to reduce the negative effects of land use change is proposed.

Rapid population growth and human activities have resulted in unsustainable exploitation of natural resources. Studying land changes, change detection and prediction are essential for analyzing developmental consequences over time and also for decision-making and implementing appropriate policy responses relating to land uses.
Land-Use Change modeling have become a top research topic in many scientific field recently. There are many approaches and models in use to perform spatially simulations, but among many land use modeling tools, LCM offers many advantages.
This modeler evaluates land changes between two different times, calculates the changes trend, gain and loses, Persistence and displays the results with graphs and maps. There are three approaches to produce the probability map: logistic regression, multilayer perceptron (MLP) and a similarity-weighted (SimWeight).
Multi-layer perceptron (MLP) creates a transition potential map for each group of transitions in order to allocate the simulated transitions. It use a Markov matrix to extrapolate the quantity of each transition and persistence.
Markov matrix generally obtains through the comparison of the land use maps from two dates. Markov chain projection provides the model with the estimated areas of each land use category for future dates and the amount of change for each transition.
LCM needs explanatory variables to improve the understanding of the causes, locations, and trends of land use changes. These variables are selected when they exhibit relatively high Cramer coefficient values for land cover categories. Cramer’s Coefficient indicates the degree to which each explanatory variable is associated with the distribution of land cover categories.
This study demonstrated that human disturbance include road and urban distance were the key factors in determining transition model in land change prediction.
We predicted land use changes in Bojnourd city using multi-temporal remote sensing data and a multi-layer perceptron (MLP) neural network with a Markov chain model. Remote sensing techniques applied to classify satellite imagery for 2001, 2008 and 2015.
Several studies have developed different modeling methods to simulate the pattern and consequences of land use changes. Arekhi (2011) modeled deforestation using logistic regression, GIS and RS in the Iran's west forests. In this research the effects of seven factors, distance from roads and residential areas, forest fragmentation index, distance from forest edge, aspect, elevation and slope was studied. The results indicated that with decreasing the distance from residential areas and roads, forest degradation will be more and the most of the deforestation occurred in the fragmented forest cover.
In other reaserch Joorabian Shooshtari and Gholamalifard (2015) explored changes in landscape pattern in northern Iran's Neka Basin for 1987, 2001, 2006, 2011, and 2017. Their studies revealed that during 1987–2001, agriculture was the main contributor to the increased built-up area, between 2001 and 2006 agriculture converted to orchard and residential, and between 2006 and 2011 forest regenerated from orchard and agricultural lands.
Numerous studies have assessed urban growth with different modeling methods around the world. These studies though mapped and focused on determining whether a change has occurred and how the change has evolved over time.
Wilson & Weng (2011), studied impacts of urban land use and climate changes on surface water quality within Des Plaines River watershed, Illinois. Low density residential growth, normal urban growth, and commercial growth are three future scenarios in this study that specified with Land Change Modeler (LCM).
Land Change Models can be very useful tools for environmental and urban growth research concerning about land use change. LCM was used in this research to predict the land use map in 2015 using the following procedure: Change analysis and choice of explanatory variables, Transition potential modelling, Change prediction map and Model assessment.
A total area of 14438/03 hectares of Bojnourd city was taken as study area which has potential for expansion. IDRISI Andes was used to determine land change using three different land-use maps from 2001, 2008 and 2015. In This Study, A series of satellite images of Landsat Enhanced Thematic Mapper Plus (ETM) and Operational Land Imager (OLI) data (2001, 2008, and 2015) respectively were used to produce classified land use/cover map. It's necessary to assess the satellite data for their image quality.
Maximum likelihood classification were used to derive 3 land use categories in the study area. This way is based on the probability density function that is associated with a particular training site signature.
Accurate assessment land use maps, using ground control points, visual interpretation and Google earth were controlled. The classification accuracy and kappa coefficients was evaluated for land use maps.
The Land Change Modeler module in IDRISI software was utilized for land change detection and change trend analysis. For change analysis and prediction, first of all Land use maps for 2001 with 2008, 2008 with 2015, and 2001 with 2015 were used for analysis and detection of changes. Net change, Gains and losses, persistence and other modules were used to evaluate map transition potential.
The study used several variables including distance from road, distance from settlement, distance human disturbance, distance from vegetation edge, slope and qualitative variables.
Two land cover maps of two different times (2001 and 2008) were applied to predict potentially transition in the future. LCM available as IDRISI and ArcGIS extension) is a useful tool for the assessment and projection of land cover changes. Different modules are available to do this like cross tab module, gains and losses and etc.
The land-use maps were produced by supervised maximum likelihood classification and 3 classes (settlement areas, vegetation and burren land) were considered. Kappa coefficients obtained in this study was above 80%.
In this study, 3 major land use categories identified and mapped after field surveys, literature reviews and visual interpretation. Neural network training was carried out with the default setting (learning rate from 0.01 to 0.001, momentum 0.5, number of hidden nodes calculated as the average between numbers of input and output nodes, 10,000 iterations).
4 sub-models were identified which included burrenland to settlement areas, vegetation to settlement areas, vegetation to burrenland and burrenland to vegetation. Land use map of 2015 was predicted by using changes that occurred during the years 2001 and 2008.
The analysis of changes shows the expansion in settlement areas (1203 ha), burgenland (737 ha), vegetation area (554 ha) during the years 2001– 2015. The land use change analysis for the next period (2008-2015), indicates that the area of settlement areas has increased. The transition from Burgenland and vegetation to settlement areas was 980 ha.
Discussion and The objective of this research is to evaluate LCM as a land use model, focusing on its predictive power for the assessment of transition potential.
This study used Landsat ETM imageries of 2001 and 2008, and OLI/TIRS of 2015 to identify, classify, Assess and interpret changes in a city area. The land cover categories and their changes for these years were generated and analyzed in the Idrisi environment.
Land use scenarios for 14 years from 2001 to 2015 was performed using Markov analysis in LCM module in IDRISI software.
Land Change Models can be very useful tools for environmental and urban growth research concerning about land use change. Rapid urban growth in last decades is a big problem which prompt concerns about environmental issues over the accompanied environmental issues and the degradation of economical sustainability.
Land use maps are very vital for decision makers and environmental management purposes to evaluate land changes and the Causes of land degradation. LCM provides comparable and understandable maps and graph to demonstrate natural and environmental conditions. Monitoring land changes can provide valuable information for regional management and planning, but it's not enough. Prospective simulation supports decision-making for urban planner and environmental management. LCM, provides great advantages such as better monitoring changes, describe change trend, quantifying changes and also it can answer to questions with different scenarios like "what would happen if ".

In this research, the attempt is to introduce a probabilistic and statistical Method for identifying late winter frost in the west of Iran, applying statistical methods and Markov chain modeling. To do so, the acquired data about minimum temperature from 37 Synoptic stations and 2 Climatology stations of the 35-year period (1980-2014) were provided by the Meteorological Organization of the country. Then, by performing an interpolation, through Kriging method, for a period of 12784 days, in the time period under consideration, with 6 6 kilometer spatial resolution and a UTM video system, a network database was created. In this survey a late winter frosting event for each area of the surveyed region is a day in which the occurrence probability of a frost is 10% or less. Hence, for each day of the year and each cell (pixel) from the surveyed area, the occurrence probability of frost was estimated. The computational stages of the occurrence probability of frost included using the Markov chain in the MATLAB software and the maps were drawn in the Surfer software. The achievements reveal that in the southwest of the region, the late winter frost date is between 25 February and 5 March and in the north of the region between 21 April and 15 May. The severity of the late winter frosts in the southwestern area of the region is more than the northern part.

In this research, the attempt is to introduce a probabilistic and statistical Method for identifying late winter frost in the west of Iran, applying statistical methods and Markov chain modeling. To do so, the acquired data about minimum temperature from 37 Synoptic stations and 2 Climatology stations of the 35-year period (1980-2014) were provided by the Meteorological Organization of the country. Then, by performing an interpolation, through Kriging method, for a period of 12784 days, in the time period under consideration, with 6 6 kilometer spatial resolution and a UTM video system, a network database was created. In this survey a late winter frosting event for each area of the surveyed region is a day in which the occurrence probability of a frost is 10% or less. Hence, for each day of the year and each cell (pixel) from the surveyed area, the occurrence probability of frost was estimated. The computational stages of the occurrence probability of frost included using the Markov chain in the MATLAB software and the maps were drawn in the Surfer software. The achievements reveal that in the southwest of the region, the late winter frost date is between 25 February and 5 March and in the north of the region between 21 April and 15 May. The severity of the late winter frosts in the southwestern area of the region is more than the northern part.

The extent of spread and source degradation would be determined using prediction of land use/ land cover changes. In this way these changes would be guided in the right directions. The aim of this study is modeling the process of land use / land cover changes of Hamedan province using Landsat TM satellite image of 1989 and IRS LISS III image of 2008.
After running the necessary corrections, land use/ land cover maps of the study area in the past two years were obtained using supervised classification with maximum likelihood algorithm. Then probability matrix of land use transition (to each other) were calculated using Markov chain with respect to land use/ land cover map. In the next step, Cellular Automata method was used to geo specified these changes.
Findings: Finally land use/ land cover map of Hamedan province for 19 years later (2024) was obtained and the area of each land use/ land cover was calculated.
Discussion and Counclusion: The results of this research shows that natural land use/ land covers will be decreased and transmited to human land uses in future. These changes are conceivable due to population growth and increasing human needs to exploit the nature; but this process should be considered to exploit the natural resources in a sustainable manner to avoid severe consequences in future.