جستجوی مقالات مرتبط با کلیدواژه « پوشش زمین » در نشریات گروه « علوم پایه »

Unfortunately, in recent decade, due to land use change and vegetation cover degradation, most of the rainfall changed into run off and causes abundant loss of life and property by creating enormous floods. The purpose of this evaluation was to study the run off impacts on the erosion of the catchment area using the MPSIAC model.

For this purpose, the basic information and maps of the Khadijeh Khatoon basin in 2018 such as topographic and geology maps were collected. Then the calculation steps of nine model factors including surface geology or metrology, soil, climate, run off, topography, land cover, land use, current erosion and river erosion, and erosion and Sediment is estimated. Finally, the relationship between runoff and erosion of the basin was determined and the model of erosion and sediment estimation from runoff was determined.

The results of this study showed that the amount of soil erosion is equal to 5806.97 tons per year and is 3.58 tons per hectare per year. If the value of one ton of agricultural land is equal to 50 US dollars and if the condition of erosion is available, the value of 5806.97 tons per year is equal to 290348.5 US dollars, which is equal to 11613.9 million Rials. The MPSIAC's seventh and eighth factor basins are the most effective factors.

This conclusion suggests that pastures around Khadijeh Khatoun are not well-conditioned due to over-capacity utilization, and the whole of the region's pastures due to climatic conditions. Also, the results showed that the runoff factor had a linear correlation of 0.98 with erosion, which indicates that the higher the runoff, the increase in the amount of erosion and deposition.

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.

The main purpose of satellite image processing is preparing thematic and efficient maps, so choosing appropriate classification algorithm has important role in this case. In parametric methods such as maximum likelihood main problem is their dependence on the statistical distribution of input data. Artificial neural network is nonparametric classification method which is not dependent on any particular distribution and extract desired functions from within data. This study aimed to compare the efficiency of neural network and maximum likelihood to classify land cover Using Landsat Satellite Images. Determine classes and samples to classify land cover Using field operations, topographic maps, aerial photographs and maps were made and using the above information four classes vegetation cover, building, water and outdoor were selected. After applying two algorithms, the neural network and maximum likelihood on the Landsat 8 satellite image with OLI sensors, land cover map of the arvand coastal area was prepared. Multi-layer perceptron network neural network structure consists of three input neurons, seven intermediate neurons, and four output neurons. For network training, a back propagation algorithm has been used. with Kappa coefficient, the accuracy of the classification methods was evaluated. Based on the results, Artificial neural network method with kappa coefficient of 0.92 in comparison to maximum probability algorithm with kappa coefficient of 0.79 has a better performance in providing land cover map of the arvand coastal area which is due to Neural network is nonparametric and nonlinear.

Land degradation is a global challenge that results in reduced soil fertility and eventually becomes one of the major environmental issues worldwide. The issue of land use and land cover changes by mankind is one of the most important factors of environmental change at the local and global scale which has significant implications for ecosystem health, water quality and sustainable land management (Foley et al., 2005; Lubchenco, 1998). The problem of land degradation in the Ilam province is affected by a combination of natural and anthropogenic factors, including deforestation, drought, overgrazing, intensive agriculture, increased sediment performance due to soil erosion, evaporation, dam construction and drought. These environmental problems are ultimately linked to changes in land cover and land use. Therefore, it is important to consider land cover and land-use changes to monitor environmental changes and sustainable resource management plans for fragile ecosystems such as Ilam province and provide results to area managers and planners for restoration and prevention.

In this study, we used MODIS Terra Satellite Land Cover Products (MCD12Q1) to study the monitoring of land cover changes in the Ilam province for the period of 2001 to 2013. All data for the study area were mosaicked and sampled with the UTM Global Coordinate System, using the nearest neighbor sampling method. Then they were Geo-referenced and finally, on Gis-Envi software correction operations (geometric, radiometric and atmospheric) were run and then the study area was clipped. The method initially classified the images into 17 classes and then calculated the number of pixels (zeros up to 255) of each class for the duration of 13 years and mapped the trends and percentages of changes occurring to each class.

Shrub forests have begun to grow in size since 2009 and their numbers appear to have increased in the coming years. Conversely, forest-savannahs and savannas in 2001-2007 had a small area (34 pixels in total) of their own, and since 2007 species of this type have become extinct and are not observed at all, in the studied level. The water levels (code zero) in the study area underwent very little change from 2001 to 2013, and only from 2004 to 2008 did the extent of the water levels show greater values than other years. The maximum amount of water in 2005 was 29 pixels (that means, 7250000 m = 500*500*29), which is mostly related to the Karkheh and several pixels of the Simare River in the south and east of the border. But water levels appear to have increased since 2013 due to the construction of several dams in the study area. Also, the levels for urban and residential areas (Code 13) from 2001 to 2013 are consistently 159 pixels per year across the study area, and we don't see any increase or decrease in it. Finally (code 7, 10, 12 and 15), respectively, each are 564.424 and 8.953 and 47.030 and 443.520 pixels for 13 years. That is %99.78 of the area covered. Among these areas, open forest shrubs with 564,424 pixels and no vegetation areas with 443,520 pixels had the highest amount of land cover (%94.5) in the area.

According to the extracted map (Map No. 2) the story lacking vegetation named Code 15 which is marked yellow in the map, is mostly located in the west and southwest of the study area, including the lowland basins, namely Western Abbas plain, Eastern Abbas plain, Chenane, Abadan, Mehran, Dehloran, Salehabad, and Sumar. In some years, due to the increasing trend of land degradation, it is advancing toward the center and east of the region. Overall, the trend of changes in the amount of no vegetation levels over the past 13 years show an increase, and this can be a concerning issue in terms of land degradation. This should be seriously taken into consideration. As the maps show, there is a decreasing inclination in the trend of open shrub forest zones during the study period as well.

Ecosystems provide a wide range of services that vary in quantity and quality depending on the type of ecosystems and their status. Estimating the ecosystem services value (ESV) is very important to support land use planning processes. Ecosystem services are under great pressure due to urban growth and its effects. The human activities that reduce ecosystem services include land use/land cover (LU/LC) change driven by agricultural activities and urban growth. In this paper, we assessed LU/LC dynamics in Golestan Province using change detection and Markov chain as inputs for ESV of different land use types to estimate the amount of services. We also used sensitivity analysis to explore the robustness of results by 50% adjustment of value coefficients.

Golestan Province is one of the 31 provinces of Iran, located in the south of the Caspian Sea. In this study, multi-temporal data of LU/LC were produced from multispectral Landsat imagery acquired on two separate years (1984 and 2015). We also analyzed ecosystem service values for 2035. Markov chain was used for quantity forecasting. Markov Chain Analysis is a convenient tool for modeling land use change, when changes and processes in the landscape are difficult to describe. LU/LC data were analyzed using Ecosystem Services Value (ESV) for different biomes.

Land cover classification indicated that settlements were the most increased land cover over the study period. From 1984 to 2014, ecosystem service values of forests decreased from 1722 to 952 million dollars. Annual value of 22 ecosystem services was estimated. In terms of the estimated ESV, food production, raw materials, climate regulation, nutrient cycling, biological control, genetic diversity and recreation were reduced while the rest of ESVs were increased/decreased. The effect of using different coefficients to evaluate the total ESV in Golestan Province was evaluated. The coefficient of sensitivity (CS) of these analyses was less than one in all cases.Our results of analyses indicated that the total ecosystem values estimated for Golestan Province were relatively inelastic, which also suggests that our ecosystem value evaluation is reasonable.According tothe LU/LC within the landscape, our results indicate that rangelands and forests
provide a higher level of ecosystem service than others due to the relatively larger area coverage and higher value coefficients and hence a reduction in this particular component of the ecosystem hampers a balanced flow of services from the landscape.

Ecosystem service value estimation based on LU/LC analysis is very important to indicate the changes in the amount of services through urban growth on spatial and temporal scales. Such estimationsin local, regional and global scales are important in influencing land use planning processes through modifying national accounting systems to reflect the true values of ecosystem services so that it will be ultimately used as a basis for sustainable development. Another benefit could be related to land use optimization projects to increase ecosystem services.

Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times that can play is an important role in regional planning.This study is an integration of remote sensing data and Geographic Information System in order to detecting changes of land use and land cover around the Arjan Lake in Fars province from 1987 to 2004. After geometric and atmospheric corrections We classified satellite images in four categories zone water, land farming, shrubbery and bare lands using maximum likelihood algorithm and compared the output images using post- classification method which lead to calculating rates of changes. Results showed when ever lake water decreases in a period of time, bare and shrub lands increase, which corresponds the dryness of the lake. Comparing water Arjan Lake area during a period of 17 years indicates total decrease of 6.5 square kilometers for the water mass of the lake. The overall accuracy and kappa coefficient was calculated for the output data showed all data are classified with high accuracy.

Change detection is process of identifying differences in the state of an object or phenomenon by observing it at different times when it can play an important role in regional planning. This study is an integration of remote sensing data and Geographic Information System in order to detect changes of land use/land cover around the Bakhtegan Lake in Fars province from 1956 to 2007. After geometric and atmospheric corrections, we have classified satellite images in five categories of water zone, land farming, shrubbery, bare and salt lands using maximum likelihood algorithm. Then, we have compared the output images using post- classification method. This method leads to calculating rates of changes. We have also maintained the effect of rainfall oscillation on water lake area because it has huge effect on ecologic condition of the lakes in semi-arid regions. Results showed whenever lake water decreases in a period of time, bare and saline lands increase, which correspond the dryness of the lake. Comparing water lake areas during a period of 51 years indicates a total decrease of 296 square kilometers from the water body of the lake. Calculated overall accuracy and kappa coefficients for output results showed maximum accuracy in classification of TM data and minimum accuracy for MSS images. The Bakhtegan Lake is the second largest lake of Iran with a length of 77 km, average width of 10 km and an area of about 750 km2. It is located in eastern area of Fars province. In this study the aerial images of 1/50000 zones where have been taken from the zone on 3/06/1956 as well as satellite images of ETM 13/05/2005, MSS 28/06/1976, TM 9/05/1990, and ASTER 15/06/2007 have been used as the material. The changes related to water width of lake as well as covering changes around the lake of Bakhtegan have been studied during time period from 1956 to 2007 and their relation was determined with the study of precipitation rate of the period. In this research the aerial images of 1/50000 related to the year 1956 (oldest accessible source) as well as the images of MSS in 1976, the TM of 1990, ETM of 2005 and the ASTER of 2007 related to Bakhtegan lake changes and the range around it have been studied during time period of 1956-2007. First, the aerial images have been used to prepare land use and land cover maps and to determine Lake Boundary in 1956. As two or more images are used in change detection methods and as these images should completely be coinciding with each other geometrically, they have been used by two hybrid methods that are the combination of image repairing methods to map and stabilize the images. First, the ETM image have been corrected geometrically based on 1/25000 topography maps then all images have been stabilized on this image. Re-sampling has been done to create the new image by using the prevalent closest neighborhood method. The internal average relative reflectance has been used in order to achieve an atmospheric correction and the images have been classified after correcting. Generally, the classification methods are divided into parametric, non parametric and non metric groups. The parametric classification method has considered the statistical distribution of educational data in image classes and uses a statistical model to distribute pixels in these classes. One of the most prevalent methods of parametric classification, the maximum likelihood method, has been used in this research and its accuracy have been evaluated and studied. The maximum likelihood algorithm allocates each unknown pixel to the most likely class. This method supposes that the distribution of educating data is in normal form in each class. In this article the images from different dates have been grouped into five classes of water width, agricultural covering, shrubbery, bare and saline lands.The water width class due to differences in water reflection in different depths has been divided into three parts, low depth, medium and deep and their total was considered as water width.After changing the outputs format to suitable format in geographic information system, the land covering area has been calculated separately in each time phase. The post processing method, considered as the most prevalent qualitative methods has been used to statistically evaluate the changes. In this method, at first the images of different dates are classified by one of the prevalent methods and following that, the corresponding pixels of images are compared to each other. Then digital results are presented and analyzed in confusion matrix. The images are also compared two by two to detect the changes in the study area. To compare the land covering in two time phases of 1956 and 2007, a ready GIS map, obtained from 1/50000 aerial images is compared with ASTER image to calculate rates of changes in a 51 years time distance. Then the accuracy of classification is calculated for different images using the parameters of general accuracy and Kappa coefficient calculated for output data. In the following to determine effect of precipitation on the lake water rate, the annual average rainfall in the period is calculated and its relation with water width of the lake is studied. The interpretation of aerial images from 1956 and MSS satellite images processing in 1976, TM of1990, ETM of 2005, ASTER of 2007, achieved the land use and land cover maps related to around Bakhtegan Lake in five classes of water width, agricultural cover, shrubbery, bare and saline lands in related years. The land use and land cover maps have indicated that the water width class has the maximum area of change compared with other classes and the change tone is not a linear process; the major known reason is the oscillation in precipitation. As the oscillation has the maximum effect on the water width changes and ecological conditions of the lake, it is strived to be dealed with in more details. The data from four rain gauging stations of Arsanjan, Neiriz, Abadeh tashk and Sahl Abad has been used to determine the effect of rainfall oscillatoin on water width area of Bakhtegan Lake. Due to the lack of data related to rainfall before 1976; the average annual precipitation was calculated for time period of 1976-2007 and it has been compared with water width change in lake. The surveys has indicated that in the time periods with low precipitation rate, the water width area of the lake has been decreased and during the time period of 1990-2005 when the annual precipitation rate was maximum, the Bakhtegan Lake also has the maximum water width area. To evaluate the accuracy, the educational samples of the zone using the topographical maps of 1/50000 and 1/25000 and the satellite images is determined. After data classification, the detection accuracy of each image is calculated using the parameters of general accuracy and Kappa coefficient of confusion matrix. The overall accuracy and Kappa coefficients for image are determined as 59/6 % and 0/54 % for MSS, 92/3 and 0/90 for TM image, 93/9% and 0/92 for ETM and 92.7% and 90% for ASTER images. The producer’s accuracy related to each of the specified zones in image is obtained from the division of the number of the pixels that are placed in that class by the detection changing method, (Diagonal elements of confusion matrix) by the whole existing pixels in that classe which are existed in reference data. The user accuracy is obtained from the division of the number of the pixels (that are placed in that class using the detection changing method) by the number of the pixels allocated to that class using detection changing method. The results indicated that most of the data groups have been classified with high accuracy. To determine the land use and land covering changes in the limited area of Bakhtegan Lake, the maximum likelihood algorithm has been implemented on MSS satellite images in 1976, TM in 1990, ETM in 2005 and ASTER in 2007 and the images have been compared two by two. In addition, to determine the land use and land cover changes in the time period of 1956-2007, the GIS ready map resulted from the interpretation of 1/50000 aerial images have been compared with the similar map resulted from the classification of ASTER image. The studies have indicated that during a time period whenever there is a low precipitation rate, the water width area of the lake has also been reduced and during time period of 1990-2005when the water width area has been increased, the under cultivation area indicated a decrease due to water salinity of the lake. In addition, the change of saline land area is in relation to water width area of the lake as during the time period of 1976-1990 when the lake has minimum quantity of water the width of saline land area was maximum and during the time period of 1390-2005 when the lake water is maximum, the width of saline land area decreases. The change detection during time period of 1956-2007 has indicated that the area of water width and shrubby covering around the lake has a decrease of 296 and 5/5 square km2, respectively. However, the bare lands, the agricultural covering and the width of saline lands had an increase of 130, 12 and 71 square km2, respectively.

Wind erosion and associated dust emissions play fundamental roles in many ecological processes and provide important biogeochemical connectivity at scales ranging from individual plants up to the entire globe. Yet, most ecological studies do not explicitly consider dust-driven processes, perhaps because most relevant research on aeolian (wind-driven) processes has been presented in a geosciences rather than an ecological context. To bridge this disciplinary gap, we provide a general overview of the ecological importance of dust, examine complex interactions between wind erosion and ecosystem dynamics from the scale of plants and surrounding space to regional and global scales, and highlight specific examples of how disturbance affects these interactions and their consequences. It is likely that changes in climate and intensification of land use will lead to increased dust production from many drylands. To address these issues, environmental scientists, land managers, and policy makers need to consider wind erosion and dust emissions more explicitly in resource management decisions.

Today most destruction of environmental is the habitats. Hence, the need for methods that can help them to evaluate the habitat, and the over time obtained quality in this habitat. In this study was performed habitat evaluation of the Afghan Pika with Habitat Evaluation Procedure (HEP) and Binary logistic regression. Then, the results of these two methods were compared. Major factors as a variable habitat for species habitat evaluation were measured included: Average of the dominant slope, the aspect, altitude and land cover. Results of the two methods of modeling suggest that land-cover variable has an important role in the Pika habitat suitability which for this species is in rocky areas with cliffs and slopes over 30 degrees with small spaces between the rocks and also the geographical aspects which this species preferred are from north to south. Results showed that the height depending on other variables can be positive or negative, and usually in this region pikas live at altitudes between 2100 to 2300m. Finally, we obtained a model for Pika Habitat Suitability Index (HIS) and also models to predict the Pika presence.