The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of −4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: −0.76), night LST and IS in spring (0.60), night LST and albedo in winter (−0.53) and day LST with fractional vegetation cover in summer (−0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

Background: One of the natural phenomena which have had considerable impacts on various regions of the world, including Iran, is "dust storm". In recent years, this phenomenon has taken on new dimensions in Iran and has changed from a local problem to a national issue. This study is an attempt to investigate the formation of the dust storms crossing the Western Iran. Methodology: To find the sources of the dust storms entering Iran, first we examine three determined dust paths in the region and their temporal activities, using MODIS satellite images. Then, four regions were identified as dust sources through soil, land cover and wind data. Finally, atmospheric analyses are implemented to find synoptic patterns inducing dust storms. Results and discussion: Source 1 has covered the region between the eastern banks of Euphrates and western banks of Tigris. Source 2 is in desert area of western and south-western Iraq. Finally source 3 is bounded in eastern and south-eastern deserts of Saudi Arabia called Rub-Al-Khali desert, or Empty Quarter. Moreover, south-eastern part of Iraq (source 4) was also determined as a secondary source which thickens the dust masses originating from the above mentioned sources. The study of synoptic circulations suggests that the dust storms originating from source 1 are formed due to the intense pressure gradient between the low-pressure system of Zagros and a high-pressure cell formed on Mediterranean Sea. The dust events in sources 2 and 3 are outcomes of the atmospheric circulations dominant in the cold period of the year in mid-latitudes.

Objective: Proper shelter site selection is necessary for long-term welfare of earthquake affected people. This study aims to explore the criteria that need to be considered after earthquakes. Methods: Through a systematic review, 273 articles found that were published till April 2014. Among these, seven articles have been selected and analyzed for the criteria that they introduced for sheltering site selection after earthquakes. Results: Out of 27 proposed criteria, accessibility and proximity to homes of affected people were stressed in all the papers. Moreover, seven other criteria were the same in most of the papers including suitable size, suitable distance from hazardous areas, geological hazards and land slope, suitable distance from medical centers, water supply and Security. We categorized all the mentioned criteria in six main categories. Size and location, disaster risk reduction, relief and rescue facilities, feasibility of the site, environmental and social aspects are the main categories. Conclusion: Selection and applying proper criteria for shelter site selection after earthquakes is a multi-disciplinary task. The decision needs relevant models and/or tools. Geographic Information System (GIS) is a useful tool for this purpose. Key words: Disaster, earthquake, shelter, site selection, systematic review

Dust storm occurs frequently in arid and semi-arid areas of the world. This natural phenomenon, which is the result of stormy winds, raises a lot of dust from desert surfaces and decreases visibility to less than 1 km. In recent years the temporal frequency of occurrences and their spatial extents has been dramatically increased. West of Iran, especially in spring and summer, suffers from significant increases of these events which cause several social and economic problems. Detecting and recognizing the extent of dust storms is very important issue in designing warning systems, management and decreasing the risk of this phenomenon. As the process of monitoring and prediction are related to detection of this phenomenon and it's separation from other atmospheric phenomena such as cloud, so the main aim of this research is establishing an automated process for detection of dust masses. In this study 20 events of dust happened in western part of Iran during 2000–2011 have been recognized and studied. To the aim of detecting dust events we used satellite images of MODIS sensor. Finally a model based on reflectance and thermal infrared bands has been developed. The efficiency of this method has been checked using dust events. Results show that the model has a good performance in all cases. It also has the ability and robustness to be used in any dust storm forecasting and warning system.

Introduction: After a devastating earthquake, the site selection for the sheltering of earthquake victims is an important task. In order to generate a list of appropriate criteria for deciding on temporary sheltering site selection, we systematically combined the experience of experts and the findings of published documents in this study. Methods: Having explored published papers, we generated a list of criteria for the selection of the best location for temporary sheltering. In the next step, all criteria were presented to a group of experts in Iran and after a scientific discussion, the list was updated. In the last step, the final list of criteria was developed using the Delphi method in three rounds. Results: Based on our previous systematic review, 27 criteria were presented for sheltering site selection. Expert interviews added 12 more items to them. The Delphi process approved 21 criteria of all proposed ones. These items then grouped into four categories: land suitability, socio-cultural considerations, service availability and disaster risk reduction. Discussion: After an earthquake, our list of criteria may help the disaster team to select the best locations for temporary sheltering with minimum confusion. The consent of the earthquake victims and cost reduction of the operation would be the minimum benefits of using the appropriate criteria. These criteria also could be used by researchers to make objective and reproducible assessments of temporary sheltering site selection. Key words: Criteria, Earthquake, Model, Site selection, Temporary shelter,