جستجوی مقالات مرتبط با کلیدواژه « Land cover changes » در نشریات گروه « جغرافیا »

Land use and land cover change includes the conversion of natural land cover into impermeable surfaces as a product of the rapid growth of urban and rural areas. One of the topics that has attracted the attention of researchers in recent years is the impacts of these changes on the temperature of the earth's surface as a vital parameter for the environment and human life. In this study, the relationship between the changes in land cover and the temperature of the earth's surface has been investigated using Madis sensor satellite images, focusing on Mazandaran province. In order to analyze the data, Moran's index and hot spot analysis have been used from 2012 to 2022. The findings showed that following the changes of the land cover in the studied area, the surface temperature of the province has increased by 1.46 degrees during the studied decade. Also, during the same period, the night temperature of the province has increased by 1.02 degrees Celsius and its daily temperature has increased by 1.89 degrees Celsius. Also, more than 98% of the urban lands, which are considered as the hottest parts of the province, are located in hot clusters. The results of this study can play an important role in better planning the regional development of Mazandaran province and create healthier residential environments and provide very useful information to help manage and plan the development of residential land in order to achieve environmental sustainability.The results of this study can play an important role in better planning the regional development of Mazandaran province and create healthier residential environments and provide very useful information to help manage and plan the development of residential land in order to achieve environmental sustainability.

Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.

One of the inevitable consequences of the ever-increasing growth of the world’s population is the expansion of urbanization. So, it is very important to provide a vision of the spatial development of cities with the aim of understanding the correct pattern of city growth and providing the necessary infrastructure. Since Qom metropolis faced urban growth and has recorded 95% urbanization, this research focused on the spatial development of urban lands around this metropolis. First, the land use/cover and the urban growth merit maps were produced. Land use/ land cover maps of the region for the years 2000, 2010 and 2020 were produced using the random forest method in the Google Earth Engine, and the urban growth merit map for the years 2000 and 2010 was produced using MCDM analyses based on GIS. Finally, based on the ANN-CA-Markov and SVM-CA-Markov algorithms, 2020’s land use/cover maps were simulated. The validation of the models showed that the SVM-CA-Markov algorithm with the ROC (0.96) was more accurate and was chosen as the optimal algorithm for modeling the horizon of 2040. The results indicate the increasing spatial development of this metropolis. The area of urban land in this region will increase from 139.62 square kilometers in 2020 to more than 183 square kilometers in 2040. The evaluation of the results can help the relevant managers in order to take necessary policies to manage the situation as best as possible. This importance can be realized through planning for the regular development of the road network, the expansion of urban green spaces, etc. official organizations and local officials should have a purposeful monitoring of this issue, while having full control over the development directions of this metropolis.

Nowadays, following the technological advances and increased accuracy of remote sensing and satellite imagery capabilities, it seems that the combination of these capabilities with the available information of changes or destruction of the resources in the past and present, and base on the modeling will be able to predict the land cover changes in the natural and anthropogenic areas such as protected area. One of the most important ways to obtain this object is the spatial modeling of land cover changes using various strategies. Therefore, the present study was to evaluate the ability of Ca-Markov model to estimate the land cover changes in protected area located in the Bijar state from 1987 to 2013 period and also modeling of this changes and predict of them for 2026. For this purpose, Landsat images of 1987, 2000, and 2013 were used to extraction of land cover maps. Validation of the model was taken by comparing the predicted map of 2013 using model and land cover map for the same year. The rangelands and agriculture lands use in 2013 were about 21180.65 and 10273.53 hectares which prediction result of the model showed 21935.97 and 9631.62 hectares, respectively. This result indicates the high accuracy of the Ca-Markov model to predict the land cover changes in 2013. The results showed that from 1987 to 2000, about 12 percent, and from 2000 to 2013, about 10 percent of the rangelands converted to agriculture lands. The prediction result of Ca-Markov model shows that if the current trend continues, between 2013 and 2026, about 13 percent of the rangelands will converted to agriculture land.

Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.                                                                                                              

materials and Method:                                                                                                                 

 In this study, to reveal the relationship between land cover levels and different land use classes, cross-information matrix analysis was used in the ARC-GIS software platform. Since one of the main objectives of the study was to investigate and reveal the albedo inductions of land cover classes in Lorestan province, so the relationship between these two factors was investigated by cross-matrix analysis technique. In this regard, two sets of data were used. The first set of data was related to land cover classes of MODIS sensor composite product with a spatial resolution of 1 km and hierarchical data format (MCD 12(Q2 (MCD product) which was obtained from the database of this sensor

 Land cover classes or perhaps it can be said that land use is one of the most important shapers and determinants of climate near the earth. In this study, it was observed that in general, 5 major land cover classes in the province are separable, among which rangeland and forest lands account for 85% of the total land cover of the province. On the other hand, it was seen in this study that the average spatial albedo of the province in spring, autumn and winter is about 0.2, which is very close to the global value of this component, but in winter the average value of this index in the province reaches 0.3, which can be increased Shows attention. The five land cover classes in the province had their own unique albido induction in winter, which was separable and distinct from each other, but in spring, summer and autumn, no significant distinction of albido induction of these land cover was revealed. 

IntroductionStudying land cover changes has a very long history which coincides with the beginning of human life. Following the formation of societies, primitive humans began to change the cover of wasteland to form suitable lands for agriculture and animal husbandry. More than half of the world's population recently lives in cities, urbanization and urbanism is rapidly increasing, and this trend will continue to reach its peak. Due to their extensive coverage, reproducibility, easy-access, high accuracy and reduction in necessary time and expenses, remote sensing data are generally considered a preferred method used to study land cover, vegetation, and their changes. Many researchers have shown an interest in land cover change in different cities of the world. The history of land cover studies dates back to the early nineteenth century and the studies performed by von Thünen (1826). Von Thünen have determined the economic benefits of different land covers based on their distance from the central city and found an optimal distribution for production and land cover in the form of a series of concentric circles. Land cover changes due to human activities are considered to be an important topic in regional and development planning. Since land cover changes and urban development in the study area have not been previously studied, Landsat time series satellite imagery and a combination of Landsat 7 and 8 panchromatic and multispectral bands were used to identify and detect changes in land cover and urban development in the urban areas of Abarkooh from 2000 to 2020. Materials & MethodsSatellite remote sensing data are used in the present study (Landsat 7 and 8 multi-temporal satellite images collected in 2000, 2010 and 2020). 3 images were retrieved from US Geological Survey website and used in the present study. Raw remote sensing images always contain errors in geometry and the measured pixel values. The former category is called geometric errors and the latter is called radiometric errors. Atmospheric corrections were performed for all images used, and stripping in the imagery collected in 2010 image was also corrected. For image enhancement and extraction of more information from the images, false color composites were used (5-4-3 infrared, red and green bands) for Landsat 8 and Landsat 7 (3-4-3 near infrared, red and green bands) images. Using this technique, vegetation is shown in red. Compared to other methods, Gram-Schmidt based pan sharpening method produced higher spatial resolution images of the study area and thus was used to combine the selected images. Maximum likelihood method is considered to have the highest efficiency among various supervised classification methods. Results & DiscussionThis method assumes the presence of a normal distribution for all training areas. The accuracy of this classification has to be calculated following the classification. To do so, the kappa coefficient and overall accuracy of each class were calculated in ENVI5.3. The results are shown in the error matrix. Overall accuracy is the average of classification accuracy. The kappa coefficient calculates the accuracy of classification as compared to a completely random classification. Based on the available data, spatial resolution of the images and the information researcher has access to, 5 classes of training data (urban constructed space, roads, barren lands, arable lands, and gardens) have been selected for each image. Results obtained from the maximum likelihood classification method in ENVI5.3 environment were changed into the vector format and then used as a shape file in GIS environment. After compiling the land database, land cover maps and its changes were extracted in three periods and the area of each land cover class was determined. Each of the land cover maps, 5 classes with different colors are determined and shown. To ensure the accuracy of the classification, the accuracy of the classification has been evaluated. ConclusionThe resulting kappa coefficient for 2000 and 2020 equaled 86% and overall accuracy equaled 89%, while for 2010 kappa coefficient equaled 90% and overall accuracy equaled 92%. Thus, the error rate is small and acceptable. Finally, post-classification comparison method was used to investigate the nature of changes. 13 square kilometers of land cover were investigated in the present study. To identify the exact type of land cover changes, categorized images collected in these years were compared. Total area of residential land use showed an increasing trend: a total 4.25 square kilometers in 2000 (32.69 percent of the total area under study) has reached 5.58 square kilometers (42.92 percent) in 2020. Overall area of arable land use did not change much in the period of 2000 to 2010. However, a declining trend was observed in 2020 changing a part of this land use into residential and barren lands. Results of satellite image processing and classification indicate that supervised classification and maximum probability algorithm were close to ground realities and had an acceptable accuracy. In general, results indicate that significant amounts of vegetation and agricultural lands have been converted into urban areas and thus, planning for urban growth in these areas should be in favor of preserving gardens and agricultural lands.

Study of resources changes in previous years could be useful in the planning and optimal using of resources to control inappropriate changes. Because land use changes occur on large-scale, remote sensing technique is a useful and valuable tool for monitoring the changes. The aim of this research is land  cover changes detection in a period of 32 years in Pishva town with using remote sensing technique .First TM, ETM and OLI images for the years 1986, 2002 and 2018 were collected respectively and after geometric and radiometric corrections, images were classified by using maximum likelihood classification methods. Kappa and overall indexes were used to calculate classification accuracy. Results showed in past 32 years, bare land and irrigated land have decreased while residential and greenhouse areas have increased. Classification accuracy showed that OLI, ETM and TM sensors have high accuracy respectively with kappa 0.96, 0.80 and 0.76 and also overall indexes of 97.56, 86.54 and 86 percent. Based on results, in the first period (1986-2002) 27.6%, in the second period (2002-2018) 29.60% and in the third period (1986-2018) 31.8% of area land cover have been changed. Results showed land cover changes in the area is related to climate changes like low precipitation, drought and social condition like population and food need increasing and economic condition like high production and efficiency.

Land cover changes as a basic factor in environmental change act and has become a global threat. In this research, changes in land cover in rural tourism areas by neural networks, Markov chains in software ArcGIS, ENVI, Terrset using the TM and OLI satellite imagery, Landsat Satellite was surveyed for a period of 30 years for three periods of 1985, 2000, and 2015. The findings of the first stage show that land cover changes at the period 1985-2015, were classified in five class residential spaces, Commercial, Green, Empty and mountainous spaces and communication networks. In this study, the area of mountainous and empty spaces (13.25%) has decreased and in contrast, has decreased the amount of green spaces (6.221%), Residential (5.258%), commercial (1.264%) and communication networks (0.529%). Changing land cover as one of the most important environmental risks has been directly influenced by the Commodification phenomenon. Also, the findings of the prediction using the Markov-CA chain showed that with the continuation of the current and excessive loading on the ground, on the horizon of 2030, green cover (Agriculture, gardens and grassland, garden and residential)  and  wild land  and mountain cover have been reduced and to cover residential and commercial villas will be added. Based on research findings concluded that land cover changes in rural tourism areas in order to achieve more profits has become incompatible applications. This change in land cover, in addition to the economic, social impacts, has led to the formation of environmental hazards in the Bharang area. Developing tourism in the study area by removing agricultural land from the production cycle has led to an increase in urban activities and the formation of new activities (service, Residential Garden, residential villa) instead of traditional activities(agriculture and livestock) that are economical. And by loading too much ecological power tolerable land, while posing environmental hazards, causing incompatible activities next to each other, they do not match. Therefore, tourism, which gradually formed over the years and now it has become a part of rural texture, Spatial Conflict and heterogeneity two strains has created for them. Spatial Conflict created, due to changes in land cover and acceptance of incompatible activities that derive from human-nature relationships. This means that the rapid and unpredictable trend of tourism development, the rural landscape has encountered a problem and with changes in land cover, has led to inconsistencies between different activities and eventually has shaped the Spatial Conflict.

Investigation about the influence of land-cover and land use changes on soil erodibility potential, case study: Gharesou, Gorganrood
Land use and land cover (LUC) change associated with climatic and geomorphologic conditions of the area have an accelerating impact on the land degradation. Natural as well as human-induced land use land cover change (LUCC) has significant impacts on regional soil degradation, including soil erosion, soil acidification, nutrient leaching, and organic matter depletion. Since the last century, soil erosion accelerated by human activities has become a serious environmental problem. It has a manifold environmental impact by negatively affecting water supply, reservoir storage capacity, agricultural productivity, and freshwater ecology of the region. In recent years, many researchers have highlighted the environmental consequences of soil erosion.
Soil erosion estimation at a regional scale is influenced by the complexity of the soil erosion process and the availability of data describing the soil erosion factors. In the last decade, regional and national level assessments of soil erosion were carried out using different approaches, ranging from indicator or factor-based approaches to process-based models. However, the revised universal soil loss (RUSLE) and its modifications are still widely used because of its simplicity and a greater availability of input parameters.
Gharesou basin is one of the sub-basins of Gharesou, it suffered from severe erosion in some areas over the past years. This erosion has occurred for different reasons and one of them is land use change and weak management of water and soil resources. The purpose of this research is to investigate the effects of land-cover changes on the potential of soil erosion in Gharesou Basin, a sub-basin of Gorganrood, in Golestan province. For this, we have employed RUSLE Model and used landsat satellite images from the sensors of TM, ETM, and OLI for 1985, 2000, and 2015. The potential soil erosion in this study was estimated using RUSLE model, which can be described using following equation: A = R × K × LS × C × P
where A is amount of soil erosion calculated in tons per hectare per year, R is rainfall factor , K is soil erodibility factor , L is slope length factor, S is slope steepness factor, C is cover and management factor, and P is erosion control practice factor. To run the RUSLE model in GIS, first, rainfall raster layer, soil, slope, Digital Elevation Model, and also layers of soil protection range were created. Each of the involved factors was calculated in separate units in the basin level. In this research, Gharesou basin was analyzed based on raster network data with 30 meters cell size, because, from one hand it's small enough to show heterogeneity of the basin and on the other hand, it matches pixel dimensions of landsat satellite images.
The results of land-cover changes have revealed a decrease in dense forest areas, low forest areas and the mixture of orchard, forest and pastures in a thirty years period. According to the results of RUSLE, changes of the classes indicate a general trend to the soil loss in the basin. Therefore, Gharesou basin is a basin with increasing soil erosion potential. In the plain and coastal plain areas of the basin, that is the mainly cultivated area, the amount of erosion is different from the other areas, and soil loss process is decreasing. It's due to the changes of cultivation method from traditional to modern, increase of irrigated farming area, choosing more environmentally friendly plants, and also, increase in the area of cities and villages from 7.14 percent to 29.04 percent during 30 years. In the study classes, for output of RUSLE model, in every 3 years of study, the maximum area relates to the classes of 100 to 200 Ton per year that is more seen in the mountainous regions. In these regions, all factors except vegetation are toward soil loss. Also, during 30 years, the amount of dense vegetation decreased from 34.56 to 31.55. In fact the only factor in protecting soil in (prone to erosion) areas has given its place to less effective vegetation, so, the area of this region has increased and Gharesou basin is in danger of soil loss in mountainous and forest parts. Also, areas with more than 200 Ton in hectare, with the lowest amount, have had a tangible increase during 30 year of study and its amount has increased from 11.74 to 12.50. These areas are usually located in mountainous parts with no vegetation. Also, the average of soil erosion potential estimated in Gharesou basin for 1985, 2000 and 2015 is 102.02, 103.11, and 103.76 (ton per hectare per year). This amount was found in the sub-basins too and except the sub-basin 4 located in coastal plain areas of the basin, with farming use, the amount of other sub-basins is increasing. According to the results of study, mountainous parts of Gharesou basin, has the most damage due to the accumulation of involved factors in the potential increase of soil loss. So, the necessity of watershed management is observed. Also modification of cultivation pattern and soil conservation training in farming lands of foothills and hillsides are required.

Changes resulting from changes in land use and land cover is one of the most important factors in the instability of rural settlements in recent decades. Monitoring of land cover pattern and simulation of future changes is an essential and vital issue for Planning of Ecological Resource Management. this issue in natural environment of guilan due to unique forest conditions is crucial in the sustainability of rural settlements.
This study analyzed changes in land cover of rural areas at East guilan (AstanehAshrafieh, Siahkal, Lahijan & Langerood with area of 2260 km2) over 3 periods. 1989-TM, 2000 –ETM & 2015 land sat 8 images were processed. In order to prediction of changes in land cover untill 2030probability matrix of changes in 6 classes (including urban, rice field, gardens, water source, forests & lands without cover) was calculated. all analyses were performed using markov chain model & cellular automata. Results of changes detection between 1989- 2015 show that area of rice fields, forests & lands without cover has been decreased, but area of urban regions & gardens has been increased.
According to these results, instability of rural settlements will escalate, if there is no fundamental action in making policy for conservation of resources. Results of simulation of future confirms this issue as well. Assessments predict that until 2030, area of rice fields will be reduced 800 hectares. area of urban regions and gardens will be increased respectively 5300 and 4500 hectares.

In recent decades, problems of urban growth and land cover changes has been one of the most important issues on a global and national scale that different technologys were used for the recognition of the nature, dimension and functions of the aforesaid causes and process , among these the remote sensing technology due to its ability to produce high spatial and spectral resolution satellite images is known to be an effective tool for the detection and evaluation of changes. The present study is descriptive – analytical approach which uses Landsat ETM , TM, MSS images acquired in 1973, 1985, 2000 and 2011 in order to analyze the trend of land cover and land use changes. To obtain the magnitude of changes, band subtraction method and fuzzy logic were used. Satellite images were classified using Maximum Likelihood Classifier (MLC) in ARCGIS10.1 software. Results show that as a consequence of increasing population and expanding of urban areas, fuzzy magnitude of changes were equal to 0.84843, 0.92983 and 0.92795 for the periods of 1973-1985, 2000-1985, and 2000-2011 respectively. The urban area have increased 7/6, 15/2, and 11/9 square kilometers during these three periods. And during 38 years, urban area has reached from 14.9 to 49.8 square kilometers which shows an average annual growth rate of 0.918 km2 and correlation between population growth and urban expansion is 0.976.