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"Tehran (Iran) Maps."
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Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods
Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping.
Journal Article
Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances
The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520
(
AUC
AHP
=
75.70
%
,
AUC
SI
=
80.37
%
,
and
AUC
BLR
=
85.20
%
)
. Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.
Journal Article
Delineation of groundwater potential zones using remote sensing, GIS, and AHP technique in Tehran–Karaj plain, Iran
Evaluation of groundwater resources in dry areas without enough data is a challenging task in many parts of the world, including Tehran–Karaj plain in Iran, which includes Tehran, the capital city of Iran and Karaj, one of Iran’s biggest cities. Water demand due to increasing agricultural and industrial activities caused many problems in the field of water resources management. In this study, the potential of groundwater resources was evaluated using remote sensing, geographic information system (GIS), and analytic hierarchy process (AHP) for the first time. Digital Elevation Model from Shuttle Radar Topography Mission was used to generate a slope map and drainage density map. Three Landsat-8 satellite images were utilized to provide lineament density and land cover/land use maps. Geological and soil type maps were provided from the Geological Survey and Mineral Explorations of Iran (GSI). Tropical Rainfall Measuring Mission data were used to prepare average annual precipitation map. Discharge values from 102 pumping wells in the time period of 2002–2014 were used to evaluate the results. Seven data layers were prepared, and the geodatabase was made in GIS. The layers and their classes were assigned weights using AHP method. Finally, the layers were overlaid based on their weights, and the potential map of groundwater resources was generated. The area was classified into five zones with very high, high, moderate, low, and very low potentials. The zones covered 5.95, 32.90, 22.70, 10.20, and 28.25% of the study area, respectively. The results showed good agreement with the field data obtained from discharge wells.
Journal Article
Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran
In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.
Journal Article
Urbanization dynamics of Tehran city (1975-2015) using artificial neural networks
Land-use dynamic is a major challenge for town and country planners especially in developing countries such as Iran. Iran has been under rapid urban expansion and population growth for past three decades which led to lack of resources, environmental deterioration and haphazard landscape development. In this paper, an attempt has been made to map the urbanization dynamics of Tehran in 40 years based on remote sensing imagery and by means of artificial neural networks. The presented scheme could be taken into consideration when planning initiatives aimed at surveying, monitoring, managing and sustainable development of the territory. Moreover, it can serve the experts in the fields of geography, urban studies and planning as a background for number of geographical analyses.
Journal Article
Kinematic and dynamic analysis of north-Tehran tectonic wedge formed in south central Alborz, Iran
To answer the question \"Whether the North-Tehran tectonic wedge is a dynamic tectonic wedge or not?\" we applied paleostress techniques to investigate fault slip data. The mean reduced stress tensor is defined for all stabilized stress regions. Unscaled Mohr's circles drawn for fault slip data were used to obtain the relative slip tendency of clusters on diagrams. It showed that the slip tendency in the vicinity of fault junction is much lower than expected. The mean σ1 defined for a combination of fault slip data trends N14°E nearly parallel with the overall pressure in Iranian crust at the latitude of Central Alborz. This trend suggests the least effect of boundary faults and the wedge between them on stress orientations inside the wedge. Finally, the stress trajectory map was prepared showing the configuration and relative intensity of σ1. The map did not illustrate any convergence in σ1 trajectories and the consequent concentration of stress and seismic potential in fault junction. That is a direct evidence for disagreeing the dynamicity of this tectonic wedge. Copyright 2012 Geological Society of India
Journal Article
1979: Iran's Islamic Revolution
\"Tehran, Iran's capital, was in a state of revolt on Jan. 19, 1979. The Shah, Iran's ruler for nearly four decades, had fled the country. Ayatollah Ruhollah Khomeini, the Shiite Muslim cleric who had worked for years to overthrow the Shah, was still in exile in Paris, but vowing to return and form an Islamic government. A million people took to the streets to cheer on Khomeini and denounce the Shah. Within two weeks, Khomeini had returned, replacing Iran's secular government with a theocracy ruled by Islamic religious leaders called mullahs. By year's end, young supporters of Khomeini--angered by America's long support of the Shah--had stormed the U.S. embassy in Tehran, taking dozens of hostages. 'Death to the Shah!' gave way to 'Death to America!' and U.S. officials knew they had a powerful new foe on their hands in a radicalized Middle East.\" (New York Times Upfront) Learn more about how \"an American ally became one of its biggest adversaries.\" A quiz is included.
Magazine Article