Adel Khazaei; Majid Abaspour; Sasan Babaei Kafaky; Lobat Taghavi; Yousef rashidi
Abstract
Introduction: The metropolis of Tehran as the largest capital of the Middle East is faced with phenomena such as environmental degradation, land use change and high concentration of agricultural and industrial disasters. Knowing the changes of land use in the past and predicting its future status is ...
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Introduction: The metropolis of Tehran as the largest capital of the Middle East is faced with phenomena such as environmental degradation, land use change and high concentration of agricultural and industrial disasters. Knowing the changes of land use in the past and predicting its future status is necessary in order to carry out a principled, dynamic planning. In this study, the spatio-temporal dynamics of land use changes in the Tehran in a 20-year period and the prediction of future changes in these land uses in the next 40-year were selected as the general objectives of this study.Material and Methods: After forming a database of Landsat 5 and 8 satellite images for three times of 2001, 2011 and 2021, the land use map of this times were prepared. For the validation of the maps Google Earth images, ground points and accuracy and Kappa coefficients were used. The time period from 2021 to 2061 was considered to predict future changes. In order to zoning and predict the future of land use changes, 6 land use change transfer sub-models with artificial neural network, Markov chain, and LCM model were used. Evaluation of the accuracy of the model was obtained from the comparison of the ground map of 2021, the future map of 2061, and the values of Null success, success, Miss and False Alarm were obtained.Results and Discussion: The results showed that the period from 2001 to 2021 was associated with the expansion of residential areas, the growth of urban areas and the reduction of green spaces including gardens and parks. The expansion of residential areas has been primarily in poor and barren soils and then in gardens and green spaces. This urban growth was clearly in region 5, 21, 22 and its physical development process was linear. The decrease in the level of gardens and green space is very catastrophic and this decrease is especially evident in the central areas of the city due to the high density of buildings. Urban parks are clearly in a complicated condition in the eastern areas of Tehran. The area of rain fed agriculture has increased and the area of barren soils and poor lands has decreased. Most of the changes in land use related to low capacity lands and agricultural lands have occurred. Studying the maps of the future of land cover showed the continuation of the same trend of the past 20 years. Although the growth of residential areas will be slower than the previous period, but the capacity and dimensions of the city will continue to increase, especially in the western, southern and southwestern regions. The decreasing trend of gardens, parks and urban green space is still observed. This process is more intense in the case of parks and they will be destroyed more quickly. The reason for this is besides drought and withering of trees due to climate change, pollution, and conversion of these green land uses to rain fed agriculture, parks, poor rangelands and urban areas. As for the urban green spaces, the 16 and 4 regions have the worst positions, and the 17, 19. 2, 5 and 22 regions will not be safe from this damage either. The decrease in the area of rangelands and cities moved to new areas will increase; the cycle of destruction of vegetation will increase from the outskirts of Tehran.Conclusion: Construction was more in the south of Tehran and the decreasing trend of urban green space will continue to be observed. The central areas of Tehran will be completely devoid of trees due to the predominance of the urban areas, and the point to consider is the destruction of the green belt in the north of Tehran in the future.
Morteza Sharif; Saeid Hamzeh
Abstract
Introduction: One of the most important factors that play a major role in reducing soil fertility and agricultural land degradation is soil salinization. Soil salinity problem is more severe in agricultural lands of arid and semi-arid regions. In many cases, human activities and irrigation of agricultural ...
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Introduction: One of the most important factors that play a major role in reducing soil fertility and agricultural land degradation is soil salinization. Soil salinity problem is more severe in agricultural lands of arid and semi-arid regions. In many cases, human activities and irrigation of agricultural lands with saline water are the cause of salinization. This is a serious problem in different regions of Iran, especially in Khuzestan Province. Therefore, the present study was conducted with the aim of monitoring and evaluating the effect of Gotvand Dam on the salinization of the downstream area and changing its plant ecosystem before and after water intake using remote sensing imagery.Material and methods: The time series of two ETM+ and OLI sensors from 2019-1999 were collected using plant indices (NDVI, SAVI), biophysical index of leaf cover (LAI), and salinity indices. The soil was classified by salient decision-making method of changes in halophyte and non-halophyte plants according to the threshold obtained from the indicators used in each year. Then, the final results were evaluated according to the trend of changes obtained from the used indicators and their correlation with changes in the plant ecosystem of the region.Results and discussion: The rate of vegetation changes in the four years of 2018, 2013, 2002, and 1999 was more than other years, which was prepared by the method of supervised classification of the area under normal vegetation and saline plants. According to the results obtained from 1999, the total vegetation area of the groves was about 1117 hectares, of which about 134 hectares were related to halophyte vegetation. However in 2018, these values were estimated at 921 hectares, with areas covered by halophyte changing to 445 hectares and halophyte to 476 hectares.Conclusion: The results of the study indicate the onset of the highest stresses in the plant ecosystem of the region and the simultaneous decline in leaf cover and NDVI with the water intake of Gotvand Dam since 2011. This coincidence, which is due to the salinity of the water of Gotvand Dam Lake and consequently Karun River, has a significant effect on increasing salinity and changes in soil quality of the region and thus increasing halophyte plants as well as high vegetation degradation in the region. These conditions can create more serious challenges for the ecosystem of this area and in the long period change the ecosystem and vegetation cover of this region to halophyte plants.
Zahra Asadolahi; Mostafa Keshtkar; Zia Badehian
Abstract
Introduction: Due to the importance of ecosystem services mapping in decision making, different approaches have been developed for mapping ecosystem services. InVEST software is based on models that combine land use/cover with ecosystem services, and so land use is considered an essential component of ...
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Introduction: Due to the importance of ecosystem services mapping in decision making, different approaches have been developed for mapping ecosystem services. InVEST software is based on models that combine land use/cover with ecosystem services, and so land use is considered an essential component of all models. Currently, remote sensing technology is one of the top techniques in land use/cover map extraction using both pixel-based and object-oriented methods. While the pixel-based method is based on the classification of numerical values of images, object-oriented image processing is more accurate in the classification process, due to the use of spectral information, texture, and content information, being widely used in all sectors including environmental sciences. In this regard, the present study aimed to apply satellite images and object-oriented processing in land use/cover mapping and habitat services modeling. Material and methods: The research was carried out using eCognition 9.01 and InVEST3.0 software in four steps including preparing information, object-oriented processing of satellite images, object-oriented classification, and finally, habitat modeling. Images of Landsat and Sentinel satellites were fused to the eCognition software and processed in conjunction with ASTER digital elevation model data. Segmentation was performed as the first step of object-oriented classification using multi-resolution segmentation algorithm. Due to the size of the study area and the average spatial resolution of Landsat images, the images were segmented with 30 scales, 0.4 coefficient, and 0.5 compression. Geometry, vegetation (NDVI), Pixel Gray Surface Composition (GLCM), and lighting degrees were classified using the Assign class classification algorithm. Then, by matching the extracted map with 130 teaching points, the accuracy of the kappa coefficient was determined. Next, land use/cover map was introduced into InVEST software. Results and discussion: The results and statistics obtained from object-oriented classification accuracy presented acceptable results with a kappa coefficient of 0.93. In the study area, land use/cover classes were prepared in six categories including irrigated and rain-fed agriculture, forest, rangeland, man-made areas, and water resources. According to the results, rangeland and forest types with an area of 39.8% and 33.0% covered more than 72.8% of Lorestan Province’s area. Major problems with land use mapping were the inability of 30-m pixel Landsat satellite imagery to distinguish between rain-fed agriculture, rangeland, and low-density forest types due to their spectral similarity as well as rural areas due to their small surface area. Hence, we attempted to overcome this limitation by modifying the segment characteristics such as shape, tone, texture, and other information. Habitat suitability was considered for each land use class. The susceptibility of each habitat type to the threats in the study area was also weighted. Human threats affecting habitat quality were classified into three groups of agricultural lands, residential areas, and roads. Finally, the model was implemented and a habitat quality index was obtained with values ranging from zero to one. Conclusion: There are different approaches to ecosystem services mapping, one of which is extracting ecosystem services information directly from land use/cover maps. Such an approach is appropriate for large-scale areas that are restricted in terms of available data and expert knowledge, and the service is directly related to land use.
