جستجوی مقالات مرتبط با کلیدواژه « پایش تغییرات » در نشریات گروه « کشاورزی »

Agricultural lands are valuable and limited resources so the importance of preserving and maintaining such lands is necessary due to the importance of agricultural products, economic aspect, and to meet the population's livelihood needs. In this regard, one of the most important challenges of the last century in the field of agricultural lands is the land use changes in the productive agricultural lands, which, in addition to numerous environmental problems, such as the destruction of fertile soils, vegetation, has the reduction of animal diversity. Today, with the aim of appropriate planning and successful implementation of programs, accurate and up-to-date information on the ratio of land uses and landscape of the field is needed, therefore, since remote sensing technology has a spatial and integrated view of the region and Geographic Information System has the analysis ability, modeling and preparation of land use maps can be effective in this regard. Therefore, the present research has conducted with the aim of studying the process of changes and transformation of agricultural lands in Shahryar county in the last 30 years using Remote Sensing technology (RS) and Geographic Information Systems (GIS). In this research, Landsat satellite images and sensors (TM-ETM-TIRS) were used in the years 1989 to 2022 (4 periods) to analyze the information and prepare the agricultural land use map. The results showed that garden lands have decreased a lot in the last 30 years, and irrigated agricultural lands have increased, and the analysis of vegetation using the NDVI index showed the decreasing of the vegetation during the study years. Moreover, the climate change pattern showed decrease in rainfall and increase in the surface temperature of agricultural lands in the last 30 years which has faced the farmers with water shortage and reduction and unwillingness to do agricultural activities. According to the findings, it is evident that the trend of climate change, such as increasing temperature and decreasing rainfall, and agricultural land changes, have become a serious and concerning problem in this county, and it requires the implementation and presentation of new solutions and policies in order to develop and promote agricultural activities.

Groundwater is the sole source of water for drinking, irrigation, and industrial uses in many arid and semi-arid regions of the world. Groundwater can be contaminated by natural as well as anthropogenic influences. Residential, municipal, commercial, industrial, and agricultural activities can all affect groundwater quality. Groundwater contamination results in poor drinking water quality, loss of water supply, high cleanup costs, high costs for alternative water supplies, and/or potential health problems. In Iran, dependence on groundwater has increased tremendously in recent years. Groundwaters can be contaminated by natural as well as human influences. Land use changes, residential and agricultural activities can affect the quality of groundwater. Groundwater contamination can lead to poor drinking water quality, loss of water resources, high cleanup costs, high costs for alternative water sources, and problems for watershed health. In Iran, dependence on underground water in most watersheds has increased greatly in recent years. Therefore, the evaluation, protection and proper management of underground water resources is very necessary for optimal and sustainable use of water resources. Water quality assessment includes the assessment of the physical, chemical and biological nature of water in relation to its natural quality, human effects and intended uses, especially uses that may affect human health and the health of the water system itself. The use of geographic information system technology has greatly simplified the assessment of natural resources and environmental concerns, including groundwater. In groundwater studies, GIS is commonly used to analyze site suitability, manage watershed assessment data, estimate groundwater vulnerability to contamination, model groundwater flow, model solute transport and leaching, and integrate groundwater quality assessment models with spatial data to create Spatial decision support systems are used. This study attempts to assess the influence of changing land-use patterns on the groundwater quality in the Dehram, Fars province. The study area is an agricultural developing region with land development progressing at a fast pace. The objective of this article is to demonstrate the influence of land-use transformations, land-use transition, on the quality of groundwater using geographical information systems. The study also aims at evaluating the significance and applicability of a groundwater quality index (GQI) generated using geographical information system (GIS) approach for the assessment of groundwater quality in a medium sized catchment. Moreover, a simple methodology for the preparation of a groundwater quality sustainability map, for use in planning and management decisions by local government authorities, is developed in this work.

The water samples were collected from 6 wells in the study area in 2021, which are added to the urban water system for drinking purposes. For 2014, the available data were used to observe the general changes in the quality of underground water during this period. Sampling in the summer season, due to the lack of water in this season, by taking water from wells in the area and injecting it into the drinking water system, in three repetitions for the year 1400 according to the standard method of the American Public Health Association (APHA) with field sampling and the available data of year 2013 were used to show seasonal changes in different water quality parameters during both years. Therefore, samples were taken from each well of the available data three times in 2013 and three times in summer (July to August) 1400. The geographic coordinates of the sampling wells were recorded manually using a Garmin e-Trex GPS receiver. The hydrochemical data obtained from the laboratory analysis of the water samples were linked to the spatial database of the sampling points. Spatial data shapefiles were prepared in vector format showing the locations of sample wells along with associated hydrochemical data. To evaluate the location of the sampling wells according to the potential sources of groundwater pollution, these shapefiles were placed on the land use map. Then, point shape files were used to prepare variable concentration maps by applying Kriging interpolation method. The GQI proposed was used for quality assessment. To generate the index, seven parameters listed in World Health Organization guidelines for drinking water quality were selected from the main dataset. Six parameters (Cl-, Na+, Ca+2, Mg+2, SO4-2, and TDS) can be categorized as chemically derived contaminants that could alter the water taste, odor, or appearance and affect its ‘‘acceptability’’ by consumers. NO-3 was categorized under chemicals that might inflict ‘‘potential health risk’’ and a guideline value of 50 mg/l was assigned. The GQI integrates the different water quality parameters to give a quantitative index value that can be used for spatio-temporal provides in groundwater quality. In the present study, the land use/cover map for two different periods (2013 and 2014) was prepared to evaluate land use and land cover transition patterns using Landsat satellite images from ETM+ sensors (2013) and OLI (2014).

The land-use pattern has changed drastically with the increased agricultural and built-up area at the expense of other land uses. The analysis reveals a rapid deterioration of groundwater quality related mainly to the increase in built-up land, drought and land-use change in agricultural lands and uncontrolled withdrawal of water by farmers from wells in the region. Mean GQI decreased from 86.42 to 57.36 over a period of 7 years from 2014 to 2021, which indicates a decrease in water quality. The quality of groundwater in the region in 2014 has a desirable quality and is in a very suitable range. But in 2021 the water quality changed from very good and good to poor and bad.

GQI and land use were integrated into GIS to yield groundwater quality, in terms of water quality. Zones of sustainable and unsustainable groundwater use were demarcated for better decision making related to land use allotment in this rapidly changing region. The GQI index provides the possibility of mapping the spatial changes of groundwater quality in the study area, which shows that the water quality of the area is generally good, but the deterioration has started with the onset of urbanization. The main sources of pollution identified in this study are agricultural and residential activities. Although agricultural activities and the application of fertilizers related to it have been the main factor in reducing the quality of groundwater, in addition, this study showed that the increase in urbanization has a dominant contribution to pollution in the region. Agricultural activities must comply with methods that ensure minimal impact on groundwater. This study also shows the effectiveness of GIS in groundwater quality assessment. Similarly, GIS-based assessment techniques can be used to characterize groundwater contamination preferably in large watersheds. Of course, the selection of parameters and weights may be different in each location depending on the prevailing land use conditions.

Wetlands, as one of the most sensitive ecosystems on Earth, are always facing various changes in their range, and changes in cover and use are among the most effective of these changes. The land has always been affected by human activities and uses. Those human activities that are limited to certain places and find a relatively stable position, create human uses. Therefore, analysis of wetland change has become a management priority. land use/land cover (LULC) plays a key role in the study of environmental developments at the local, regional and global levels. Human activity and change in the Earth's surface lead to changes in the structure and ecological processes of the Earth's natural systems. These changes mainly affect the main aspects of land functions (including energy balance, water, soil, and food network). In addition, pressure on natural resources, which is due to the human need for environmental resources and is often influenced by population growth drivers, leads to changes in the Earth's surface. Landscape changes due to human interventions lead to different developments and trends in land use/land cover. Therefore, time/coverage analysis is very important for understanding and routing spatial changes from the past to the present and planning for the future. Today, high-resolution multispectral and multi-temporal satellite data are used as an essential tool for estimating aspects such as vegetation, deforestation, and urban sprawl. Remote sensing and GIS technology provide a platform for studying landscape deformation across the earth's surface. Remote sensing data provide valuable information in a relatively short time and cost-effectively. High-resolution satellite imagery or aerial photographs can be used to study land use/land cover changes in different ecosystems and areas. The fact that Shadegan Wetland is one of the international wetlands in the country, which is currently on the Montreux list due to human interventions, can assess the developments around the wetland, especially in the process and type of land use/land cover changes, in identifying the drivers The main impact on this wetland is associated with its practical importance and helping to remove this wetland from the Montreux list. And waterfront can be used to adjust the exit bill of this wetland from the Montreux list. In this study, integrated remote sensing and GIS methods have been used to detect land use/land cover changes in the enclosed area and affect Shadegan wetland.

The study area is located in Shabangan Wetland, surrounded by the Ozon Plain. Due to the immediate man-made effects on Shadegan Wetland, especially the role of the surrounding roads and waterways, this area was closed on the latest Google Earth satellite images and then transferred to the layers used. In this area, the international distance is 48 degrees and 19 minutes and 16 seconds to 49 degrees and 3 minutes and 44 seconds and the northern latitude is 29 degrees and 55 minutes, 44 seconds to 38 degrees, 28 minutes and 42 seconds at a distance of about 60 kilometres. It is located south of Ahvaz, the capital of the province, and 5 km south of Shadegan. In this research, images of the 20 years of the Landsat satellite from the years 1999-to 2019 have been used. ENVI software is also used to classify images. After preprocessing and making the relevant corrections using the supervised classification method and the algorithm, the maximum likelihood of processing and highlighting the images was done, and also the kappa accuracy and coefficient of each layer were estimated for accuracy. Then, the preparation of cover and land use maps included different classes of natural land cover and human land uses. In the detection, the most important changes were made around the Shadegan wetland, so in this process, major changes in the existing classes were considered. To detect changes, the Change Detection method was used in ENVI software, which can provide complete information on changes in land use/land cover types. Land use changes were selected in 5 periods with a time interval of 20 (2019-1999).

Five-time periods of satellite data on the use and coverage of Shadegan Wetland in the years 2017, 2014, 2001, 1999, and 2019 were prepared after pre-processing and making relevant corrections using the supervised classification method and the maximum probability of processing and highlighting algorithm. Pictures were taken. The Kappa coefficient and the overall accuracy coefficient were used to evaluate the accuracy of the generated maps and according to the results, the 2019 data had the highest kappa coefficient and the highest overall accuracy. According to land cover and land use classes, the land use/land cover map of the study area was prepared for the mentioned five time periods. The findings of this study show that the land area of Shadegan wetland has changed from about 90,000 hectares in 2001 to about 150,000 hectares in 1999 during the 20 years ending 2019 the area of the wetland has decreased by about 40% in two years. After that, the wetland lands have increased and this increase continues gradually until today. However, despite this increase, the area of the wetland has not been provided in 1999, the area has decreased by about 16% compared to this year.

Considering the trend of bare lands without cover and saline lands, it can be concluded that these two diagrams have an inverse trend towards each other, which can be seen at this point or the intersection of the two desired covers. For this purpose, the desired cover must be obtained, which is created by runoff, so that in a period, the lands began to lose their coverage and became saline lands and salt ponds. Also, considering the increase in uncovering land in 2001 and the water trend, it can be concluded that this increase was due to the decrease in surface water. Due to the trend of saline lands in the relevant period and being in line with the water trend, if the water supply of the wetland is provided, thousands of saline’s will become natural lands. Also, the relative increase in water in recent years and the decrease in bare uncovered land, and the increase in saline land, indicate that the water that replaces bare uncovered land is saline. The two groups of land use and agricultural activity did not cause drastic changes in the study period and according to Table 4, the average percentage of changes in these two land uses was 4.5% and more than 1%, respectively, which is expected to have a significant impact on There is no process of destruction and destruction of lands around the wetland and therefore cannot be considered as a critical factor.

Monitoring the changes in the rangelands makes it possible to make informed decisions about the application of management and protection plans. The purpose of this research is to monitor the changes from rangelands in the five-year period from 2000 to 2015 using geographic information systems and ENVI software. In this regard, Landsat images were first prepared at the specified times. By using the Minimum Distance, Mahalanobis, Maximum Likelihood, and Decision Tree, three NDVI, EVI, and SAVI indicators were modeled in 2016. Then, the best classification method was obtained by evaluating accuracy by Kappa coefficient and overall accuracy. As a result, other images for the past years were classified by this method and five classes were classified as low-density, semi-collimated, dense, non-covered, and gardens, and agricultural lands according to the subject. Educational points were also selected for both land surveying and ROI. The results showed that the tree of decision-making with the SAVI index yielded the highest accuracy with a precision of 94.73. Thus, although the area of low density and semi-dense pastures in 2005 and dense pastures in 2010 has increased, overall, during the period 2000 to 2015, all three classes of rangelands decreased. The attention and the class of gardens and agricultural lands have increased at a low level. Based on the findings of the study period, the behavior of the indicators is similar. From 2000 to 2010, the indicators were incremental, but in 2015, they decreased. Hence, it can be said that during the destruction of rangelands, the amount of non-covered areas has increased.

The prediction and modeling of land use changes is important for understanding the quantity and quality of possible future changes. The purpose of this research is to monitor land use changes in the past and to investigate the possibility of predicting them in the future using the LCM model in the Beheshabad watershed of Chaharmahal va Bakhtiari province. In this study, Landsat 5 TM sensors images of 1991 and 2008 and Landsat 8 OLI sensors images in 2016 were used and analyzed. Images of all three periods were classified into five categories of range lands, urban-construction areas, agricultural lands, garden lands and Bare lands. The prediction of land use status for 2016 was carried out using user maps of 1370 and 1387 using the LCM model based on artificial neural networks and Markov chain analysis. For this purpose, the spatial variables of the distance from the pastures, the distance from the residential and urban areas, the distance from the agricultural land, the distance from the garden, the distance from the Bare Lands, distance from the river, distance from the road, elevation or digital elevation model, slope and direction of the slope, are used as factors affecting changes in artificial neural network. The results of modeling the transmission force using artificial neural network in most. of the sub-models showed high accuracy (62 to 94 percent). The total error in modeling for the year 2016 obtained approximetly 23%, which reflects the large mismatch of the predicted image of the model with the image of the Earth reality and the acceptability of the model. The prediction results for the years of 1420 and 1429 showed that the area of range land and bare lands would be reduced and the area of urbanconstruction areas, garden lands and agricultural lands would becincrease.

Coastlines are one of the most important phenomenon in the surface of the earth, with a dynamic nature. Monitoring of coastal area is an important parameter for sustainable development and environmental protection. Coastal area monitoring requires the extraction of coastlines at different times. Today, data collection is considered as the most efficient source of information for the study of coastal landforms, tidal levels, coastline variation, and depth of the water. The purpose of this study was to estimate the changes in the coastline of the Miankaleh gulf area that is located on the southeast coast of the Caspian Sea, using remote sensing techniques. This study examines the qualitative and quantitative interpretation of coastline changes in the 44 years period. The main research tool is the MSS, TM, RVB, OLI and ETM+ Land Rover satellite images. The results of net changes show that, a deramatic rise was occurred in the gulf of Miankaleh. But after year of 1977, this trend was reversed. The coastal variation line with estimation of mean weighted suggests that, the study area was appears to be moderately antecedent, in the period of 44-year.

In this research, occurred changes in the forests around Khorramabad between 1986 and 2018 using TM and OLI Landsat images were investigated. For this purpose, after making the necessary atmospheric and geometric corrections, the images were classified by the maximum likelihood algorithm in five classes with a total accuracy of 95% and a kappa coefficient of 0.94. By overlaying the images, the amount of lost forest (34 km2) was determined and as a dependent variable was imported into the multilayer perceptron (MLP) model. In the GIS environment, were prepared the effective factors in the process of deforestation (independent variables); then by using MLP, the deforestation process in the years under review was determined. It was also land use changes was extracted that the results show the highest changes belonged to the forest to barren land changes and finally the deforestation forecast for 10, 20 and 30 years displays a decrease of 4.6% for the year 1407, 7.5% for the year 1417 and 9.3 for 1427. The results of the network training involving all variables with mean squared error (RMS) of 0.13 indicate that the MLP-based modeling is accurate and also, using Receiver Operating Characteristic (ROC) index, the real amount of deforestation was compared to the result of the MLP model; which showed the high accuracy of the MLP model with 0.88 of the ROC.

Continual access to precise information about the land use/land cover (LULC) changes of the Earth’s surface is extremely important for any sustainable development program in which LULC serves as one of the major input criteria. In this study, a supervised classification was applied to three Landsat images collected in 1986, 1998and 2018, providing mangrove forests change data in the coastal area of the  Bushehr province. The supervised classification results were further improved by employing image enhancement and visual interpretation. Visual interpretation was not only useful in increasing the classification accuracy of the Landsat images, but also in identifying the mangrove forest cover. Post-classification comparisons of the classified images of the mangrove forest area of the Assaluyeh habitat zone during a period from 1986 to 2018 indicated that the area of these lands had been increased by 68.94 hectares, with a percentage change of 43.85%, which was an increase of 1.41% per year. These changes in the Deir habitat zone showed that the area of the land covered by the mangrove forest was 5.25 hectares, with a percentage change of 0.87. In the Mela Ganza habitat zone, the area of the land covered by the mangrove forest was 26.82 hectares, with a percentage change of 6.33, which was an increase in the annual rate of 0.20.

Predicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CA- Markov model in the Safarood-Ramsar watershed of Mazandaran province. In this research, land use and NDVI maps were prepared using Landsat TM (1986), ETM+ (2000) and OLI (2014) satellite images. The accuracy of the CA-Markov model was estimated using the Kappa index of 87%. In order to calibrate the CA-Markov model, the land use map was prepared in 2014, and the Kappa coefficient of the mapping from modeling and user base map (2014) was 82%. The results showed that during the period between 1986 and 2014, the area of forest lands decreased by 10.26% and the total area of residential areas increased by 3.27%. The land use map for the years 2021 and 2028 was predicted by the CA-Markov model. The results showed that during the period 2014-2028, forested lands and rangelands decreased by 4.92% and 1.7%, respectively. Residential areas will increase by 8.04% and the agricultural land will change slightly, indicating the changes in land use to residential land.

To conserve and manage wetland resources, it is important to inventory and monitor wetlands and their adjacent uplands. Satellite remote sensing has several advantages for monitoring wetland resources. Therefore, in order to implement better management and in order to protect the wisely of Miankaleh wetland, the evaluation and monitoring of the dynamic changes of this wetland using satellite imagery and remote sensing was done. For this purpose, the Landsat images (OLI and ETM sensors) of 15 years’ timescale (2001 -2016) provided. After geometric and atmospheric correction of these images, supervised classification with maximum likelihood method was done and these images were classified in to 5 classes Including bayer and wet lands, man making, vegetation, agriculture and aquatic zone. The results showed that there was a wide change in the wetland’s area during the 15-year period Due to land use changes and contamination has been caused. During the study period, the aqutic zone has been reduced by 11879 hectares and wetlands with the highest area of 5.52 Percent and vegetation cover with 0.55 Percent of the least increase in area. As a result, due to the process of changes in the level of the wetland, it is necessary to prevent the destruction and destruction of this wetland with proper and integrated management and planning.

Deforestation affects watershed processes and biochemical cycles and lead to soil erosion and lack of water in the catchment areas. This study is aimed to investigate the changes in forest land in the Chaloorood watershed on the west of Mazandaran province using Geomod. In this study, maps of forest in the years of 1987 and 2015 were prepared using satellite images. Then the suitability forest map was produced by making a regression equation between suitability criteria maps and forest changes map in the period of 1987-2015. Finally, by using forest map in 1987, forest suitability map and the number of modified pixels in forest land between 1987 and 2015, Forecast of the forest map for 2043 was done using Geomod. Also, by using the Validate function and classified forest map 2015, as a reference map, and the forecasting forest map 2015, as a comparative map, the validity of the production map was evaluated. The results showed that the area of forest land in 1987, 2015, and 2043 was 38683.65, 2464.354 and 15227.25 hectares, respectively. The extent of forest changes in the last 28 years and the next 28 years is 35.72% and 38.76% respectively. Forest changes in the period between 1987 and 2015 under the influence of factors such as distance from the road, forest cover density, distance from the village, slope and elevation above sea level, respectively. The Pseudo R2 and ROC coefficients are 0.29 and 0.85 respectively, which indicates the proper ability of the model to estimate forest changes over the past 28 years and the relative agreement of the model with the real changes. In this study the accuracy of resulting land use maps was 96%, which represent the appropriate capability of Geomod in land use changes modeling in Chaloosrood watershed.

In order to protect the reasonable and sagely of wetlands and also recognize the changes in their characteristics which can be caused by natural factors or human activities used remote sensing techniques and satellite image analysis. This study aimed to evaluate land use changes of Hoor Al Azim Wetland using LandSat ETM (2003) and OLI (2014) satellite images. After geometric and atmospheric correction, maximum likelihood and post-classification techniques were used to detect land use/cover changes. The overall classification accuracy and the Kappa coefficient for the produced maps to 2003 and 2014 were 0.91 and 0.89, respectively. Map classification of 2014 showed that the area of Hoor Al Azim wetlands has been decreased from 84300 to 45500 hectares. The results of change detection showed during the study residential, and rangeland area had increased, but agricultural and wetland had reduced. The findings of this study denoted that remote sensing data can provide appropriate information for specifying land use changes due to their repeatability, and broad vision. This approach will support adaptive management of wetlands such as Hoor Al Azim wetland.

In recent decades Caspian forest has been attacked by human intervention. Easy access, abundance and diversity of valuable forest products led to an increase in population density, development of new residential areas and activities of deforestation. Change detection is essential in the assessment and management of natural resources. The aim of this study, was to monitor changes in forests of Siyahmezgi watershed in two time periods (2000 and 2015), using LandSat ETM (2000) and OLI (2015) images. Images were geometric corrected using 20 ground control points that are randomly taken from all over the watershed area, and topographic maps. After selection of the best indicators of using Bhattacharyya distance, image classification using an artificial neural network algorithm was performed. The results of classification of neural network method of Siyahmezgi watershed in two time periods (2000 and 2015) showed that overall accuracy is equal to 95.75% and 95.96%, respectively. The area of forest lands during 2000 and 2015 has been reduced in size 213.55 ha. In addition, in this area dense rangelands have declined, but during this period the extent of dry farming and semi-dense rangelands have 169.95 and 9.6 hectares were added, respectively.

Flood spreading system (FSS) is one of a variety of methods for artificial aquifer recharge. The system's main problem is sedimentation on the FSS area, which reduces its effectiveness for aquifer recharge. In this regard, satellite image change detection technique is a proper method for accurate sediment distribution pattern diagnosis with the FSS. A relevant study has been conducted by Sarreshtehdari (2005), where classified images of 1987 and 2001 were compared and revealed a sediment distribution pattern and variations. In the present study, which was conducted on Jarmeh FSS in the north of Khuzestan Province, sedimentation area and distribution pattern in several periods was determined by satellite image change detection technique.
The changes were detected for three periods: 1999 (FSS construction year) to 2000, 2000 to 2006 and 2006 to 2013. Change detection follows three main steps: 1- Data pre-processing including geometry and radiometry corrections 2- Selection of change detection method 3- Results accuracy assessment. In the data pre-processing step, image to image method was used for geometry correction and Dark Object Subtraction method was used for radiometry correction. Post-Classification comparison algorithm, change detection statistics method was selected for the present study. The method is capable of preparing a complete change matrix with details. Some indices including overall accuracy, Omission, Commission and kappa coefficient extracted from error matrix, were used to assess the accuracy of results. Maximum likelihood algorithm was selected and used for image classification. Also, Optimum Index Factor (OIF) was used to improve image color composite selection, for the preparation of training samples.
Based on the FSS area properties, three regions including sediments, disturbed and undisturbed terrains were separated on the FSS and the images were classified accordingly. Classification overall accuracy for 2000, 2006 and 2013 images was 78, 85 and 83%, respectively. The results of post-classification comparison algorithm by change detection statistics showed that, in the 2000 to 2006 period, sediment area increased from 8.5 to 27.5 ha. In 2006 to 2013, due to drought and the spread of flood on the FSS, the sediment area increased a little and reached 29.5 ha.
The results showed that, the sediment area was well extended in the first period. Proper amounts of precipitation and flooding on the one hand and no sediment initial surfaces on the other hand resulted in a good change detection. However, in the second period, the sediment area slightly decreased. Such a result is logically unexpected but drought in recent years and no flooding especially on the middle area of FSS, result in old sediment surfaces with different reflection properties. In this case, the classification algorithm was unable to enhance the entire sedimentation area. Hence, not only is there no reduction in sedimentation area, but the area slightly increased in the upper FSS for the second period.

This study is designed، aiming to detect changes in the composition and configuration of landscape in Rasht township between 1989 and 2011. It has been conducted applying synoptic analysis. Hence، landscape maps have been generated from three TM satellite images at 1987، 1999 and 2011 applying hybrid classification method. Some composition and configuration-based metrics including NP، PD، LSI، LPI، SHDI، CONTAG، ED and PLAND were then calculated at two class and landscape levels and their changes is detected. The results indicated that، in general، the landscape has been more fragmented، more complex and irregular in shape، more disconnected in continuity of patches and more diversified in the term of land use/ land cover types. It was in spite of the differences between trend and gradient of changes in Rasht county landscape at two period of time (1987- 1999 and 1999- 2011). Moreover، the results revealed that sprawl of the manmade areas have caused increase in fragmentation، irregularity and complexity of agriculture patches and decrease in total area of agriculture class. Hence، an intense degradation of ecosystems might be resulted if the current trend continues in future in the study area.

The loss of spatial balance and inharmonious physics development across regions، especially in the metropolitans has made monitoring changes in land use and its direction in harmony with reducing the harmful effects in the long and short run became the main issues of policymakers and policy planners’ concerns. The aim of the study was to detect changing in Shahriar''s land use via three methods of support vector machine (SVM)، neural network classifier (NNC) and maximum likelihood classifier (MLC) during (1987-2009) using LandSat digital images. The results showed that during a 22 years period، the urban development increased and agricultural land declined. The decline in agricultural land has been 37، 26 and 25 percent based on SVM، NNC and MLC methods، respectively. Furthermore، the result indicated that urban land has been increased 57 and 41 percent via SVM and NNC، while agriculture land has been decreased four percent based on MLC، which demonstrates the weaknesses in accounting estimates of this method.