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One of the most important aspects of climate change is alterations in the amount and characteristics of precipitation. The present study intended to project precipitation changes over Iran based on the regional models of the CORDEX project under two scenarios (namely, RCP4.5 and RCP8.5). For this purpose, six indices, including maximum 1-day precipitation, simple precipitation intensity index, maximum length of dry spell, annual count of days when PRCP ≥ 10 mm, annual count of days when PRCP ≥ 1 mm, and annual total precipitation in wet days, were extracted. MERRA2 reanalysis precipitation data, highly correlated with observation data from 48 weather stations all over the country, were used for the 1980–2006 time period to assess the CORDEX project outputs. The results were presented in three different time periods, including the base period (1980–2006), the near future (2021–2050), and the far future (2071–2100). The results of the analyses indicated that four indices, namely the maximum 1-day precipitation, the annual count of days when PRCP ≥ 1 mm, the annual count of days when PRCP ≥ 10 mm, and the annual total precipitation in wet days, demonstrated a decrease in precipitation and an increase in dry spells all over the country except for the east and the southeast regions where these indices will increase compared to the base period (except for the maximum length of the dry spell). Research Highlights: 1. The study focuses on monitoring and projecting spatiotemporal changes in extreme precipitation indices over Iran for historic (1980 to 2006), near future (2021-2050), and far future (2071-2100) time periods. 2. From 1980 to 2006, MERRA2 reanalysis precipitation data show a strong spatial correlation with monthly observational data, and total precipitation shows negative significant trends in the northwest and positive significant trends in the north. 2. From 1980 to 2006, MERRA2 reanalysis precipitation data show a strong spatial correlation with monthly observational data, and total precipitation shows negative significant trends in the northwest and positive significant trends in the north. 3. For projecting extreme precipitation, four CORDEX-WAS models based on two RCPs (RCP4.5 and RCP8.5) are used. 4. A precise plane is required to deal with precipitation reduction and increased dry spells in the near and far future. [ABSTRACT FROM AUTHOR]

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87 °C and 14.2 °C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and the none UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors. [ABSTRACT FROM AUTHOR]

Different parts of the climate system as well as precipitation are subject to the global warming and climate change. The aim of present study is monitoring different aspects of changes and trends in precipitation over Iran, using MERRA2 reanalysis data from 1990 to 2020 based on 7 indices including Maximum 1-day precipitation (Rx1day), Simple precipitation intensity index (SDII), Maximum length of dry spell (CDD), Maximum length of wet spell (CWD), Annual count of days when PRCP ≥ 10mm (Rx10mm), Annual count of days when PRCP ≥ 1 mm (Rxlmm), and Annual total precipitation on wet days (PRCPTOT). MERRA2 is the second generation of Modern-Era Retrospective Analysis for Research and Applications (MERRA) by NASA's GMAO (National Aeronautics and Space Administration, Global Modeling and Assimilation Office), with spatial resolution of 0.5° (approximately 50 kilometers) and temporal resolution up to hourly data. Correlation analysis between MERRA2 and observation data of 48 weather stations indicates acceptable results all over the country especially for the North, the North West and the Western parts. The highest values of R² was 0.94 calculated for the wet regions of the North and Northwest, the least values were 0.63 for the arid regions of the South and the Central parts of Iran. Mann-Kendall non-parametric test was used for trend analysis and detection. Mann-Kendall test uses time series data for consistently increasing or decreasing trends in a variable and can works with all statistical distributions. The results show a decreasing trend on 0.95 significance level in 3 indices including PRCPTOT, Rx1day, Rx10mm, in the West and Northwest of Iran. Previous researches confirmed and reported the similar results. In the North East part of the country (parts of the Khorasan-e-Razavi, Semnan and Golestan provinces) positive increasing trends was detected. Considering this distinct regional discrepancy in calculated trends (trends in indices), it is necessary to focus on the effects of possible changes in pressure and synoptic systems on the precipitation changes over Iran. For RX10mm increasing trends was detected for the South and South East of the country. Due to the high absolute humidity in these regions, if condition is suitable for convection and air ascending, Sudan low pressure and Indian monsoon make possible high amount of intense convective rainfall origin from Indian ocean. Overall, MERRA2 shows an underestimate for the region of highest rainfall (North of the country) and an overestimate for arid regions. As a conclusion, based on the results of this study it is necessary to have a distinct precise plane not only for managing the decreasing trends in precipitation and water deficits, but also for mitigating impacts of intense rainfall and flash floods. Further studies needed to determine simultaneous (combined) effects of precipitation and temperature changes in other to manag water resources. [ABSTRACT FROM AUTHOR]

Land use / land cover (LULC) has changed dramatically in recent years, especially in areas that have experienced severe climate change and population growth. Evaluation of LULC changes is the most accurate method to understand past land uses and predict their future changes. Analysis of multi-temporal optic and radar remotely sensed images is an effective tool for monitoring LULC changes, and fusion of these datasets has provided significant developments in the detection and modeling of land surface changes due to their unique features individually. Time-series image (TSI) analysis provides a history of changes in an area and allows for predicting future changes. This study aims to develop a method for modeling LULC changes and predicting future trends. To this end, a spatiotemporal regression model is proposed based on TSI. In this method, land surface changes are first modeled by considering the spatiotemporal behavioral patterns of cover changes in a given period. Then, future land covers are predicted by a spatiotemporal regression. To evaluate the proposed method, LULC changes of the Maharloo Lake (Fars province, Iran) were modeled using fused optical and radar TSI (12 consecutive image sets) and then predicted for time t = 13. Compared to ground truth data, the different land covers including; water bodies, vegetation cover, bare land, and saline area were predicted by accuracies of 84.9, 98.4, 90.7, and 97.3%, respectively, indicating the remarkable efficiency of the model proposed for predicting LULC. [ABSTRACT FROM AUTHOR]

Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method's effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%. [ABSTRACT FROM AUTHOR]

Monitoring the changes in forest cover has become an important tool for forest management due to its impact on climate change, desertification, soil erosion, and flooding. The Zagros Mountains forest-steppe is a temperate broadleaf and mixed forest ecoregion in Western Asia, and the present study analyzes the changes in Zagros forests from 1986 to 2019 using remote sensing techniques. Three Landsat images, acquired in 1986, 2002, and 2019, were used to map land cover classification in the study area. Classification kappa coefficients were 85.2, 94.1, and 94.7 for three images, respectively. Focusing on the class level, the results show that between 1986 and 2002, the primary cause of forest loss in the study area was the conversion of 7.8 thousand ha of oak forests to agricultural land. Since 2002, 0.8 thousand ha of oak forest cover have been changed to barrenlands. The decrease of Zagros forests is a main threat to the rich biodiversity found in the West of Iran. To avoid worsening soil erosion, restoration programs should prioritize planting patterns on rainfall and hill slopes, texturally dissimilar soils, and regeneration in forest gaps, particularly at high elevations and steep slopes. It is recommended that future research should use higher resolution datasets and encompass more expansive landscape units, such as the whole Zagros Mountain forests. [ABSTRACT FROM AUTHOR]

Forest nationalization policies in developing countries have often led to a reduction in local forest ownership rights and short- or long-term exploitative behaviors of stakeholders. The purpose of this research is to quantify the effect of Iran's Forest Nationalization Law (FNL) in a part of Zagros Forest over a 68-year time period (1955–2022) using 1955 historical aerial photos, 1968 Corona spy satellite photography, and classification of multi-temporal Landsat satellite images. A past classification change detection technique was used to identify the extent and the pattern of land use changes in time. For this purpose, six periods were defined, to cover the time before and after the implementation of FNL. A 0.27% deforestation trend was identified over the period after the FNL. Dense and open forested area has decreased from 7175.62 ha and 68,927.46 ha in 1955 to 5664.26 ha and 59,223.38 ha in 2022. The FNL brought decisive changes in the legal and forest management systems at the state level, mainly by giving their ownership to the state. Accordingly, the FNL and the related conservation plans have not fully succeeded in protecting, rehabilitating, recovering, and developing the sparse Zagros Forest ecosystems, as their most important goals. [ABSTRACT FROM AUTHOR]

Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran's mangroves for three decades, using the Landsat imagery available for the years 1990, 2000, 2010, and 2020. Four general vegetation indices and eight mangrove-specific indices were employed for mangrove mapping in three study sites. Additionally, six important landscape metrics were implemented to quantify the spatiotemporal alteration of the mangrove forests during the study period. Our results showed the robustness of the submerged mangrove recognition index (SMRI), validated as the most effective index (F1-score ≥ 0.89), which was used for mangrove identification within all nine sites. The mangrove area of southern Iran was estimated at approximately 13,000 ha in 2020, with an overall increase of 2313 ha over the whole period. A similar trend could be observed for both the landscape connectivity and complexity. Our results revealed that a stronger connectivity and higher complexity could be detected in most sites, while there was increased fragmentation and a weaker connection in some locations. This study provides an accurate map of Iran's mangrove forests over time and space. [ABSTRACT FROM AUTHOR]

Land serves as a vital production resource, and therefore land planning plays an important role in sustainable land‐use design. Iran includes large areas of the world's drylands, many of which are used as rangelands, and where the population has increased by 60% from 1985 to 2016. Further population increase in Iran would probably require more land resources to be allocated for human needs. However, the pace and patterns of these changes remain unclear. The aim of this study was to map land‐cover change from 1985 to 2016 and predict future land‐cover change in the Zayandehrood ecologic sub‐basins of Central Iran. By using multiseasonal LANDSAT imagery, we mapped land‐cover change with a Random Forest classifier for 1985, 1998, and 2016 with an overall accuracy of 80% for each period. Classification results revealed that from 1985 to 2016 residential areas doubled and industrial areas increased at the expense of rangelands. Our study also revealed cropland expansion at the expense of rangelands, cropland abandonment, and contraction of croplands due to residential and industrial development. Prediction of changes by 2036 with a multilayer perceptron neural network and Markov chain analysis showed further expansion of industries and residencies, particularly near the Ghamashlu Wildlife Refuge. Despite the partial restoration of rangelands in some parts of the world, our study provides evidence of ongoing rangeland degradation in Iran due to conversion to other land uses. Therefore, the study underscores the importance of sustainable land management of already cultivated areas and the development of strategies for the protection of global rangelands. [ABSTRACT FROM AUTHOR]

Maintaining the quality of surface water resources as one of the most vital water supplies has always been at the center of global concerns. A set of manifest and latent factors have yet been identified by researchers worldwide that are subject to affect the quality of surface water. Among which, the effect of land use change, due to a spatial and temporal complexity, is often not easily verifiable. The present study attempts to offer an index-based model to quantify vulnerability of surface water resources in a semi arid basin in central Iran against land use changes. For this, water quality data including Na, K, Mg, Ca, Sodium Adsorption Ratio (SAR), total anions (Sum. A), SO4, Cl, HCO3, EC, TDS, and pH were collected from hydrometric stations over a period of 26 years (1987‒2013). In order to detect land use changes, the land use maps of the years 1987, 1998, 2002, 2009, and 2013 were prepared from TM satellite images using supervised classification method. At next step, changing patterns of different land uses were traced by Shanon’s Diversity Index (SHDI) as a metric of patch diversity indicating diversity and heterogeneity of a landscape over time. Relationship between the SHDI values and water quality indicators revealed the impact of land use changes on quality of surface water resources. Statistical analysis confirmed a significant relationship between nine water quality factors and water discharge in the basin during the study period. From 1998 to 2009, the greatest changes were visible in the total anions, Ca, SO4, and HCO3. These parameters along with salinity were increasing in almost all sub-basins. According to the results, conversion of poor rangelands to rain fed agriculture fields is the most apparent land use change occurred in the study area over the study period. In 1987, SHDI as an indicator of the diversity and changes in the basin, showed a significant relationship with good rangelands (R2 =-0.835). This indicates that fragmentation of the entire watershed area was initiated in 1987, which reached its peak in 2013. Generally speaking, urbanization, poor rangeland, and irrigated agriculture were recognized as three influential land uses adversely affect the water quality in the study area. [ABSTRACT FROM AUTHOR]

A hierarchical intensity analysis of land-use change is applied to evaluate the dynamics of a coupled urban coastal system in Rasht County, Iran. Temporal land-use layers of 1987, 1999, and 2011 are employed, while spatial accuracy metrics are only available for 2011 data (overall accuracy of 94%). The errors in 1987 and 1999 layers are unknown, which can influence the accuracy of temporal change information. Such data were employed to examine the size and the type of errors that could justify deviations from uniform change intensities. Accordingly, errors comprising 3.31 and 7.47% of 1999 and 2011 maps, respectively, could explain all differences from uniform gains and errors including 5.21 and 1.81% of 1987 and 1999 maps, respectively, could explain all deviations from uniform losses. Additional historical information is also applied for uncertainty assessment and to separate probable map errors from actual land-use changes. In this regard, historical processes in Rasht County can explain different types of transition that are either consistent or inconsistent to known processes. The intensity analysis assisted in identification of systematic transitions and detection of competitive categories, which cannot be investigated through conventional change detection methods. Based on results, built-up area is the most active gaining category in the area and wetland category with less areal extent is more sensitive to intense land-use change processes. Uncertainty assessment results also indicated that there are no considerable classification errors in temporal land-use data and these imprecise layers can reliably provide implications for informed decision making. [ABSTRACT FROM AUTHOR]

Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and remote sensing researchers' interests. This study proposed an automatic method to map citrus orchards in Juybar, Iran, where planting citrus trees is booming there. In this regard, 148 Sentinel-1, Sentinel-2, and ALOS Digital Surface Model (DSM) tiles are processed in Google Earth Engine to provide a hybrid feature set including initial satellite images, Gray Level Co-occurrence Matrix (GLCM) textural features, and spectral features such as vegetation, built-up, bare-soil indices, and the proposed Vegetation Dynamic Index (VDI). A semi-automatic sample selection paradigm is also developed based on a time-series analysis of 12 monthly Normalized Difference Vegetation Indices (NDVIs), Otsu thresholding, multi-level thresholding (MLT), and using two proposed indices called Evergreenness Index (EGI) and Water-covered or No-vegetation (WCNV) index, and finally human post-revision. The output of the Support Vector Machine (SVM) classification using 60,000 samples and the post-classification operation showed that the classified map has an average overall accuracy (OA) and an average kappa coefficient (KC) equal to 99.7% and 0.992, respectively. The results show that multispectral bands lonely extracted orchards with high accuracy (OA: 99.55%, KC: 0.986), and the rest of the features only made a slight improvement to that. For the year 2019, an area of about 4351 ha is estimated as citrus orchards, which is in accordance with the agriculture department's reports (~4700 ha). The results indicate that from 2016 to 2019, despite a "citrus to non-citrus" land-use conversion of about 754 ha, the citrus orchards area was totally expanded by about 17%. Comparing the results with the Google Earth images indicates the precise extraction of orchards with a 10 m spatial resolution. To use the proposed method for extensive cases or areas with other types of evergreen trees, it is recommended to use high-resolution normalized DSMs (nDSMs) and textural features. [Display omitted] • This study aimed to map the citrus orchards in Juybar (Iran) automatically. • Ever greenness was a key feature to set up potential citrus orchards masks. • The automatic and pointwise selection method have better performance than manual and polygon based method. • Multi-modal feature set increased the accuracy of the classification. • In 2019, 4351 ha of croplands were detected as citrus orchards [ABSTRACT FROM AUTHOR]

Background: Residual inhibition (RI) is a temporary phenomenon that happens following offset of appropriate complete or partial acoustical and electrical masking stimulations in people who experience tinnitus. The biologic mechanisms associated with RI are not yet fully understood. Few studies have been focused on RI. Auditory mismatch negativity (MMN) as a change-detection tool may be an appropriate tool to explore the processing changes because of tinnitus and RI. Purpose: The purpose of this study was to investigate alterations in auditory change detection and auditory sensory memory related to RI induced by auditory electrical stimulation (AES) using MMN brain mapping in participants with tinnitus. Research Design: This investigation was a single-blind randomized controlled clinical trial study. Participants were randomly assigned into two groups: AES and placebo electrical stimulation (PES). Study Sample: Twenty-eight participants with chronic subjective tinnitus aged 22- to 45-yr-old participated in the study. Intervention: After randomization, all participants received both AES and PES for 1 min in different sessions. Data Collection and Analysis: Brain mapping of multifeature MMN paradigm was recorded from 29 scalp electrodes pre- and post-AES and PES. Following AES, participants were categorized into two groups: RI and nonresidual inhibition (NRI). The grand average MMN waveforms and isopotential topographic maps were obtained in RI, NRI, and PES groups. Results: Three MMN parameters for five deviants of frequency, intensity, duration, location, and silent gap were compared among three groups of RI, NRI, and PES. Statistical analyses revealed significant between-subject effects for AES on MMN amplitude of frequency and duration deviant, MMN area under the curve of frequency, intensity, and duration deviants. Conclusions: Presence of RI can reestablish change-detection mechanisms in the central auditory pathways. It is suggested that MMN is reliable for assessment of change-detection system in people with tinnitus. It can be a useful technique in monitoring effects of treatments and rehabilitation. [ABSTRACT FROM AUTHOR]

Modeling and prediction of land use/cover changes is an essential need for planning sustainable use of land in country like Iran with its high level of changes in land uses and land covers. This study aimed to analyze the capability of CA Markov prediction model and Landsat satellite images for land use/cover change detection within the framework of "Iran at 1404 prospective" in Esfarayen region, North Khorasan province for year 2025. The results of CA Markov modeling showed a reduction about 5000 and 400 ha in irrigated agricultural lands and rangeland class in 2025, respectively. Furthermore, the model predicted that the poor rangeland class and Esfarayen city will be increased 30 and 450 ha in 13 years from 2009 to 2025. According to the results, if current land use continues to intensify regardless of sustainable development considerations, severe degradation in the natural resources of the study area is unavoidable in the future. Overall, the results demonstrate that satellite remote sensing data and CA Markov model can be successfully used to predict land use/cover changes and the extracted maps can help policy makers to make better decision for the future of the region. [ABSTRACT FROM AUTHOR]

Land is becoming a scarce natural resource due to the burgeoning population growth and urbanization. Essentially, detecting changes in land surface is significant for understanding and assessing human impacts on the environment. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in Birjand of Iran using Landsat TM5 images between 1986 and 2010. Artificial neural network was used for classification of Landsat images. Five land use classes were delineated include Pasture, Irrigated farming Land, Dry farming lands, Barren land and Urban. Post-classification technique applied to monitor land use change through cross-tabulation. Visual interpretation, expert knowledge of the study area and ground truth in formation accumulated with field works to assess the accuracy of the classification results. Overall accuracy of 2010 and 1986 image classification was 89.67 (Kappa coefficient: 0.8539) and 88.78 (Kappa coefficient: 0.8424) respectively. The results showed considerable land use changes for the given study area. The greatest increase was related to Barren land class almost 378 percent. The dry farming lands reduced by almost 48 % during the study period. Urban class has increased drastically about 219 percent, 3% of dry farming lands, 61 % of pastures lands, 4% of irrigated farming land in 1986, converted to urban and industrial land in 2010 and alone 31 % of urban land in 1986 had conformity to urban in 2010. Irrigated farming land increased about 17.16 % predominantly due to population growth. The result of this study revealed a successful application of the ANN approach for land use change detection. Although this model demonstrated high sensitivity to training samples data, it required trial and error for attainment more accurate. But high accuracy of classification in last two years proved that ANN was highly efficient for classification of Landsat images in the study area. [ABSTRACT FROM AUTHOR]

Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000-2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000-2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously. [ABSTRACT FROM AUTHOR]

Abstract: In past decades, Iranian desert areas have undergone widespread land use transformation. Sand dune stabilizing projects and anti-desertification programs were initiated in 1965 and now cover about one million hectares of degraded land in Iran. This project aimed to stabilize sand dunes, to induce afforestation, to establish windbreaks and to apply mulch. Currently, more than three million hectares of arid land has been reclaimed across the country. In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat and TM/ETM images acquired in 1991 and 2002, respectively to map the anti-desertification process in southwestern Iran. A supervised classification was carried out for each of the two images. The results of this classification were refined using auxiliary data, visual interpretation and expert knowledge of the area. A post-classification change detection technique was used to produce change image through a cross module. Changes were assessed according to different classes. Observations made during the course of this study demonstrate that an obvious, positive change has taken place as a result of the petroleum mulch project. Results show that sand dune areas decreased from 66.4% to 56%, and in this case afforestation areas increased from 2.1% to 7.3%. However afforestation increased soil organic matter and recuperation of soil moisture storage conditions and chemical reactions such as dissolution of minerals and established more suitable conditions for crop growth. [Copyright &y& Elsevier]

Abstract: Green spaces play important functions in urban environments. Reducing air pollution, providing shade and habitat for arboreal birds, producing oxygen, providing shelter against winds, recreational and aesthetic qualities and architectural applications are the main functions of urban green spaces. With the rapid change of urban area in Mashad city during the past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these important urban elements. The objective of this study was to detect changes in extent and pattern of green areas of Mashad city and to analyze the results in terms of landscape ecology principles and functioning of the green spaces. In this research, we classified a Landsat TM and an IRS LISS-III image belonging to the years 1987 and 2006, respectively. We then used a post-classification comparison to determine the changes in green space areas of Mashad city during the 19 years covered by the images. Then, we applied landscape ecology calculations to derive metrics that quantified pattern of the changes in the green areas. The results showed that during 19 years from 1987, a significant decrease had occurred in the extent of urban green spaces with a concomitant fragmentation resulting in downgrading and destruction of the functions and services these areas provide. We conclude that the general quality of life in the central parts of the city has been diminished. We also state that a combination of remote sensing image classification, landscape metrics assessment and vegetation indices can provide a tool for assessing life quality and its trend for urban areas. [Copyright &y& Elsevier]

Kahak salt playa in South Khorasan province of Iran, have special geomorphological characteristics by the presence of ephemeral saline lakes, wetlands, salt crusts, surface accumulations of salt and zones of patterned ground. Salt crusts in the soil surface are unique in the region and have laminated horizons in the playa soil. Soil-surface salt accumulations are dominated by NaCl and gypsum. It has been found that distribution of chemical soluble is not uniform across the playa landscape, and this result influences on the variety form of patterned ground. In this study, the percent changes in some of the chemical elements such as NaCl, gypsum and also brine extent have been calculated in the playa. Indicating changes in Kahak salt playa is the main aim of this study by using remote sensing and GIS techniques. In this paper, techniques such as spectral un-mixing, maximum likelihood classification, band rationing, fuzzy classification and correlation relationships are discussed. This contribution presents modeling of temporal and spatial changes of salinity and playa developing using combined approaches that incorporate different data-fusion and data-integration techniques for two periods of date. Furthermore, percent changes in the surface-patterned ground of the playa have been calculated using texture and pattern analysis of the PCA1. Results have revealed that, in the playa developing, chemical materials such as sodium, NaCl, gypsum and also brine extent are positively correlated with each other and the most increased changes are related to gypsum and the most decreased changes are related to the NaCl. Also changes in the amount of agricultural area in the playa-lakes margin, show low effects in the desertification process. [ABSTRACT FROM AUTHOR]

[Display omitted] • Spatial changes in an urban meander river was detected. • Effects of an extreme flood on sediment budget was determined. • The amount of bank erosion after an extreme flood was determined. • The amount of incision and deposition in a meander river after an extreme flood were detected. Flooding and flood inundation are key hydrological characteristics of meander Loop Rivers. However, the effects of extreme floods on river morphology and spatial patterns of erosion and deposition are poorly understood. A detailed geomorphologic change detection and sediment/erosion process caused by an extreme flood (more than 100-year recurrence interval discharge) on 14 Apr 2016 was surveyed for a single urban meander loop of the Karoon River, Iran. The studied meander loop is located next to the city of the Ahavz and has a centerline length of about 14 km. The bathymetry in 8892 stable points was measured, also the detailed bathymetry layers for before (8 Apr 2016) and after (24 Apr 2016) the extreme flood event were compared. Geomorphological change detection (GCD) results show that in the studied reach about 106 ha of bank erosion was detected, with a total volume of approximately 450,600 m3. Furthermore, total volume of surface lowering and the total volume of surface rising volumes that were obtained were 95421.6 m3 and 98619.0 m3, respectively. The talweg length of the studied meander loop decreased about 126 m, and the average depth decreased from 3.71 m to 3.14 m after the flood event. Channel widening as a consequence of the extreme flood was found to be about 21 m (17%). The findings of this study indicate that extreme floods play an important role in the sediment budget and morphological evolution of meandering rivers. [ABSTRACT FROM AUTHOR]

• This study aimed to identify the variables that impact the land use change from 1997 to 2017. • The results showed that in all scenarios, economic drivers played the main role. • Social drivers played a significant role in conversion of the agricultural land and rangelands into residential areas. • Ecological drivers have had the least impact on land use change. • Based on the results over the 20-year period, agricultural and residential lands have increased. This case study has been carried out on the Hyrcanian Vegetation Area (HVA) located in Northern Iran. The current study aimed to identify the drivers and variables that have been effective in the land use changes (LUCs) of forests, rangelands, agricultural lands, and residential areas from 1997 to 2017 in 14 townships. For this purpose, satellite images are prepared and after the processing, the level of change is determined by using the Geospatial monitoring and modeling system. In the following, the level of LUCs along with economic, social, ecological, and legal drivers in the form of a simultaneous equation system was estimated by Two-Stage Least Square (2SLS) estimator. LUCs' results showed that the forest in 1997 was about 623529 ha, and it decreased by 618419 ha in 2017. In addition, the rangeland at the beginning of the period was 914780 ha, and in the end, it reached about 906103 ha. Satellite images showed that about 183592 ha of forests were converted into agricultural lands and about 276091 ha to residential areas. Moreover, in rangelands, 56035 ha of them have been farmed, and about 197894 ha have been changed to residential areas. Furthermore, the results stated that in all scenarios of changes, economic drivers such as income per capita, inflation, and products relative price played the main role. Moreover, social drivers play a significant role in changing the agricultural land and rangeland to residential areas. Factors such as population and the demand for food and housing show that the importance of this issue is more than ever before. Unfortunately, ecological drivers have unexpectedly had the least impact on LUCs. As a result, it is recommended to minimize the cost of agricultural inputs and adjust the market price of products considering the inflation rate. Moreover, population adjustment policies should also be considered to reduce the growing population pressure. Finally, the government should play a more effective role in protecting lands and preventing their change of use. This should be done through making fair distribution policies on wealth, applying the provision of funds and the long-term return to low-income farmers, strengthening the income sources of residents, and by developing and equipping protection units. [ABSTRACT FROM AUTHOR]

The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices' thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster. [ABSTRACT FROM AUTHOR]

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987-2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R2 = 0.94) than fall and spring (R2 = 0.58) seasons. Before 2000, ~ 50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population. [ABSTRACT FROM AUTHOR]