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Ensuring the availability of accurate and consistent datasets is crucial for reliable vegetation monitoring and trend analysis. While many studies in the Islamic Republic of Iran concentrate on vegetation change detection using data from satellites with coarse spatial resolutions, such as MODIS (Moderate Resolution Imaging Spectroradiometer), there is a noticeable gap in the literature regarding suitable datasets for moderate satellite image resolution like Landsat 8 (OLI). These datasets offer unique capacity and challenges that warrant specific attention in the development of change detection methods. This study addresses this gap by presenting a Landsat 8 dataset of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) covering the years 2013-2023. To compile this dataset, we downloaded 880 Landsat 8 scenes for Iran, with the majority (84%) acquired in August. Of the 880 Landsat 8 scenes, 461 had zero cloud cover, accounting for 52.3% of all scenes analyzed in the study. Although this study did not directly undertake a vegetation trend analysis, it provides valuable insights into the challenges and considerations necessary for vegetation trend analyses in Iran. Future research could explore the impact of pixel size on vegetation trend analysis by comparing datasets from different sources, including Landsat 8, to further enhance our understanding of vegetation dynamics in Iran. [ABSTRACT FROM AUTHOR]

Air pollution poses significant risks to human health and the environment, necessitating effective air quality management strategies. This study presents a novel approach to air quality management by integrating an autoencoder (AE) with a convolutional neural network (CNN) algorithm in Tehran city of Iran. One of the primary and vital problems in deep learning is model complexity, and the complexity of a model is affected by data distribution, data complexity, and information volume. AE provide a helpful way to denoise input data and make building deep learning models much more efficient. The proposed methodology enables spatial modeling and risk mapping of six air pollutants, namely, particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). For air pollution modelling, data from a spatial database containing the annual average of six pollutants from 2012 to 2022 was utilized. The model considered various parameters influencing air pollution: altitude, humidity, distance to industrial areas, NDVI (normalized difference vegetation index), population density, rainfall, distance to the street, temperature, traffic volume, wind direction, and wind speed. The risk map accuracy was assessed using the area under the receiver operating characteristic (ROC) curve for six pollutants. Among them, NO2, PM10, CO, PM2.5, O3, and SO2 exhibited the highest accuracy with values of 0.964, 0.95, 0.896, 0.878, 0.877, and 0.811, respectively, in the risk map generated by the CNN-AE model. The findings demonstrated the CNN-AE model's impressive precision when generating the pollution risk map. [ABSTRACT FROM AUTHOR]

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale. [ABSTRACT FROM AUTHOR]

Soil erosion has attracted the attention of researchers and managers as an environmental crisis. One of the effective factors in soil erosion is land use and land cover (LULC) change. Accordingly, the purpose of the present study is to explore the effect of LULC change on soil erosion in a semi-arid region in the southwest of Iran. LULC change map was generated over a period of 30 years (1989–2019) using a new approach in which the Normalized Difference Vegetation Index (NDVI) time series of each year was classified in the google earth engine (GEE). For classifying the NDVI time series, a nonparametric Support Vector Machine (SVM) classification method was employed. The LULC maps were also used as an input factor in the soil erosion estimation model. The amount of soil erosion in the region was estimated using the Revised Universal Soil Loss Equation (RUSLE) empirical model in the Geographical Information System (GIS) environment. Validation of LULC maps generated in GEE indicated overall accuracy higher than 86% and the kappa coefficient higher than 0.82. The study of LULC change trends revealed that the area of forests, pastures, and rock outcrop in the region has diminished, while the area of agricultural and residential LUs has been expanded. Also, the highest rate of LULC conversion was related to the conversion of forests to agricultural lands. Estimating the amount of soil erosion in the region using the RUSLE model indicated that the average annual erosion in 1989 and 2019 was 15.48 and 20.41 tons per hectare, respectively, which indicates an increase of 4.93 tons in hectares, while the hot spots of erosion in the area have increased at the confidence levels of 90, 95, and 99%. Finally, matching the LULC change map with the soil erosion map revealed that the degradation of forests and pastures as well as their conversion to agricultural lands has had the greatest impact on the increase in soil erosion. [ABSTRACT FROM AUTHOR]

When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and electricity production for households, create and sustain continuous pollution over the megalopolis of Tehran, Iran. In conjunction with daily meteorological forecasts, remote sensing can be used to identify and predict days with hazardous levels of air pollution, providing an opportunity for air quality alert systems to be triggered and warnings circulated to reduce health risks to citizens of Tehran province. Combining remotely sensed ozone column density (OCD) data from the Sentinel-5 TROPOMI sensor with NASA Giovanni data concerning meteorological parameters (temperature (T), wind speed (WS) and specific humidity (SH)), geographical parameters and population data, this study considers the drivers and effects of ozone pollution on the urban climate and vegetation condition (normalized difference vegetation index (NDVI)) of 16 counties in Tehran province, Iran during 12 months (i.e., January 2021 to December 2021). Future monthly forecasts of the OCD, climatic and terrestrial factors in 2022 are also presented. Google Earth Engine and the NASA Giovanni platforms were employed for the processing and analysis of data using an interpolation technique. Additionally, a Box–Jenkins ARIMA and Exponential Smoothing (ETS) models were compared and tailored to generate monthly forecasts of OCD, T, WS, SH and NDVI. The highest and lowest OCD was obtained in June and December 2021, with a concentration of 0.14277 mol/m2 and 0.12383 mol/m2, respectively. However, the annual average OCD was higher in the cities of Shahriar and Pakdasht in March, with values of 0.13237 mol/m2 and 0.13244 mol/m2, respectively. The lowest OCD recorded was 0.13105 mol/m2, in Shemiranat city, in the north of Tehran. The results indicate a positive correlation between OCD and NDVI, and a negative correlation between OCD, SH, WS and T. A strong seasonal trend in OCD was identified for all cities, but across the entire province, altitude and population size were the most significant explanatory variables for spatial variations in OCD. This research demonstrates that an effective OCD monitoring and forecasting model may be generated from remote sensing and meteorological variables. The implementation and utilization of these models are of paramount importance as they offer vital information to authorities for continuous air quality monitoring and strategic planning, particularly for days with hazardous air pollution. By effectively implementing the OCD model, it has the potential to directly contribute to improved health outcomes in major cities across Iran. [ABSTRACT FROM AUTHOR]

Land subsidence (LS) due to natural and human-driven forces (e.g., earthquakes and overexploitation of groundwater) has detrimental and irreversible impacts on the environmental, economic, and social aspects of human life. Thus, LS hazard mapping, monitoring, and prediction are important for scientists and decision-makers. This study evaluated the performance of seven machine learning approaches (MLAs), comprising six classification approaches and one regression approach, namely (1) classification and regression trees (CARTs), (2) boosted regression tree (BRT), (3) Bayesian linear regression (BLR), (4) support vector machine (SVM), (5) random forest (RF), (6) logistic regression (LogR), and (7) multiple linear regression (MLR), in generating LS susceptibility maps and predicting LS in two case studies (Semnan Plain and Kashmar Plain in Iran) with varying intrinsic characteristics and available data points. Multiple input variables (slope, aspect, groundwater drawdown, distance from the river, distance from the fault, lithology, land use, topographic wetness index (TWI), and normalized difference vegetation index (NDVI)), were used as predictors. BRT outperformed the other classification approaches in both case studies, with accuracy rates of 75% and 74% for Semnan and Kashmar plains, respectively. The MLR approach yielded a Mean Square Error (MSE) of 0.25 for Semnan plain and 0.32 for Kashmar plain. According to the BRT approach, the variables playing the most significant role in LS in Semnan Plain were groundwater drawdown (20.31%), distance from the river (17.11%), land use (14.98%), NDVI (12.75%), and lithology (11.93%). Moreover, the three most important factors in LS in Kashmar Plain were groundwater drawdown (35.31%), distance from the river (23.1%), and land use (12.98%). The results suggest that the BRT method is not significantly affected by data set size, but increasing the number of training set data points in MLR results in a decreased error rate. [ABSTRACT FROM AUTHOR]

The current study investigated the spatiotemporal distribution and detection of drought severity using ground indices in R-Studio software and applying indices on the images of Landsat 8 and Sentinel 2 satellites in the growing season and annually. The outcomes showed that typical months had the largest range in terrestrial indices results, and the Normalized Difference Vegetation Index (NDVI) index had the greatest compatibility with the Standard Precipitation Evaporation Index (SPEI). By comparing these two times, during the growing season, plant health was evident in the northwest and annual images in the northeast. Moreover, a negative correlation was found between Land Surface Temperature (LST) and NDVI in elevation, which was more moderate at northern heights. High mean annual temperatures during growing seasons were common in the east and south. The eastern side of the study area had high annual vegetation density, but soil moisture was higher in the southwest. Floods negatively affected plant health; with the 2019 flood, the canola yield was very low. In summary, Sentinel 2 images showed drought better in the growing season and Landsat 8 images showed drought better in the dry season or at the end of plant growth. [ABSTRACT FROM AUTHOR]

The drying up of the saline Namak Lake (NL) is one of the most severe environmental problems facing Iran's central plateau. A sharp decline (reduction of 509 km2) was identified in the NL area based on the NDWI (Normalized Difference Water Index) index over the last three decades. Therefore, the fluctuations and probable impacts on the NL area were investigated using a comprehensive approach from three perspectives: climatic, hydrologic, and anthropogenic. Ten-hydrologic, eight-climatic, and four-human-made indices were obtained using local and satellite datasets (Landsat, ERA5-Land, and GRACE datasets on the Google Earth Engine (GEE) platform). The long-term spatio-temporal variations of parameters were explored by applying the Mann–Kendall test over the last three decades. The results indicated a significant warming trend with a non-significant rainfall trend and a sharp decline in the basin's surface and groundwater levels, particularly following the construction of the 15-Khordad Dam. Dam construction had significant effects on the basin's hydrological regime, especially by reducing annual, monthly (up to 90%), and 1-day maximum streamflow values. Investigation of long-term vegetation coverage changes with the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) indices showed a growing trend, especially since 2012, as a result of an increase in farmland area under cultivation. Then, the contribution of each of these parameters to the NL area's fluctuations was investigated using machine learning and Deep Neural Network (DNN) methods, revealing the prevailing nonlinear relationships and interactions. Using the deep learning method, streamflow values were identified as the most influential factor in reducing the lake's area. These findings suggest that the warming trend and prolonged droughts have hastened the lake area decline. However, human interventions, such as the construction of streamflow control structures, the lack of supply of lake water rights, the overexploitation of groundwater, and agriculture, have been the primary contributors to the lake's demise. [ABSTRACT FROM AUTHOR]

Background: Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. Methods: The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. Results: The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease. Conclusions: The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible. [ABSTRACT FROM AUTHOR]

The nexus between prenatal greenspace exposure and low birth weight (LBW) remains largely unstudied in low- and middle-income countries (LMICs). We investigated a nationwide retrospective cohort of 4,021,741 live births (263,728 LBW births) across 31 provinces in Iran during 2013–2018. Greenness exposure during pregnancy was assessed using satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We estimated greenness-LBW associations using multiple logistic models, and quantified avoidable LBW cases under scenarios of improved greenspace through counterfactual analyses. Association analyses provide consistent evidence for approximately L-shaped exposure-response functions, linking 7.0–11.5% declines in the odds of LBW to each 0.1-unit rise in NDVI/EVI with multiple buffers. Assuming causality, 3931–5099 LBW births can be avoided by achieving greenness targets of mean NDVI/EVI, amounting to 4.4–5.6% of total LBW births in 2015. Our findings suggest potential health benefits of improved greenspace in lowering LBW risk and burden in LMICs. Quantifying well-being effects of greenness is of great importance for achieving the Global Nutrition Targets 2025 for low birth weight. Here, the authors show underlying health benefits of improved green space in lowering low birth weight risk and burden in Iran. [ABSTRACT FROM AUTHOR]

• Preparation of Spectral, thermal and pollution indices. • Examining the relationships between spectral and thermal indices with LST. • Prediction of land surface temperature (LST) using Markov and CA-Markov chain. • Prediction of urban pollution using MLP, RBF, LSTM neural networks. Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH 4 , SO 2 , and CO 2 , underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO 2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas. [ABSTRACT FROM AUTHOR]

The total precipitable water vapour (TPW) extraction algorithm using the Sentinel-3A Ocean and Land Colour Instrument (OLCI) has the potential to be improved on a regional scale. The aim of this study was to improve the TPW extraction algorithm of OLCI for the first time using environmental variables, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and elevation from mean sea level, on a regional scale over Iran. A previously developed TPW recovery algorithm was applied to the OLCI data during cloud-free days throughout a one-year period (1 January–29 December 2020). The artificial neural network (ANN) methodology was utilized in eight models. The evaluation results revealed the effectiveness of the models varied based on the topography and climate of each station. The assessment findings demonstrated that model 2, which integrated LST, elevation, and NDVI data in the ANN framework, outperformed other models across the study area. [ABSTRACT FROM AUTHOR]

In this study, the Analytic Hierarchy Process (AHP) is applied to generate erosion susceptibility maps in four basins of Kalat-e-Naderi county, namely Archangan, Kalat, Qaratigan, and Chahchaheh basins, situated in northeast Iran. The Kalat-e-Naderi region is characterized by a partial coverage of loess. Given the agricultural significance of loess and its susceptibility to erosion, this research focuses specifically on regions covered by loess. Geographic Information System (GIS) tools, including ArcMap and Quantum Geographic Information System (QGIS), were utilized to facilitate the creation of erosion susceptibility maps. Seven factors, including slope, aspect, elevation, drainage density, lithology, the Normalized Difference Vegetation Index (NDVI), and precipitation were selected for consideration. Recognizing the variability of precipitation and vegetation cover across different seasons, seasonal data for the specified factors were employed. Consequently, erosion susceptibility maps were generated on a seasonal basis. Pairwise comparison tables revealed that precipitation, lithology, and slope emerged as the dominant factors contributing to erosion susceptibility in this region. The resultant maps distinctly delineate basins with higher precipitation values, unresistant lithology (such as loess, characterized by high porosity and permeability), and steeper slopes, exhibiting heightened susceptibility to erosion (Archangan and Kalat basins). The credibility of the research findings was examined through on-site observations. The outcomes of this study may provide pertinent insights for decision-makers and planners. This information can be effectively employed in formulating strategies aimed at conserving soil quality in areas vulnerable to erosion hazards. [ABSTRACT FROM AUTHOR]

Investigating vegetation changes, especially plant phenology, can yield valuable information about global warming and climate change. Time series satellite observations and remote sensing methods offer a great source of information on distinctions and changing aspects of vegetation. The current study aimed to determine the trend and rate of changes in some phenological components of Iran's vegetation. In this regard, the current study employed the daily NDVI (Normalized Difference Vegetation Index) product of the AVHRR sensor with a spatial resolution of 0.05° × 0.05°, named AVH13C1. Then, using the HANTS algorithm, images of amplitude zero, annual amplitude, and annual phase were prepared annually from 1982 to 2019. Using TIMESAT software, the starting, end, and length of time of growing season were calculated for each pixel time series to prepare annual maps. The Mann–Kendall statistical test was used to investigate the significance of changes during the study period. On average in the entire area of Iran, the annual phase was declining with a trend of −0.6° per year, and the time for the start and end of the season was declining by −0.3 and −0.65 days per year, respectively. Major changes were noticed in the northeast, west, and northwest regions of Iran, where the annual phase declined with a trend of −0.9° per year. Since the annual growth cycle of the plant (equivalent to 356 days) was in the form of a sinusoidal signal, and the angular changes in the sine wave were between zero and 360°, each degree of change was equivalent to 1.01 days per year. Therefore, the reduction in the annual phase by −0.9 degrees almost means a change in the time (due to the earlier negative start phase) of the start of the annual growth signal by −0.9 days per year. The time of the start and end of the growing season declined by −0.6 and −1.33 days per year, respectively. The reduction in annual phase and differences in time of the starting season from 1982 to 2019 indicate the acceleration and earlier initiation of various phenological processes in the area. [ABSTRACT FROM AUTHOR]

Increasing anthropogenic pressures have caused drastic population decline of wild goats Capra aegagrus. The study aim was to determine habitat suitability and explore the factors influencing habitat selection and dispersal of wild goats in the Khaeiz and Sorkh protected area, southwestern Iran. We utilized ensemble modelling based on 25 replications of 10 species distribution models GLM (generalized linear model), GAM (generalized additive model), GBM (gradient boosting model), CTA (classification tree analysis), FDA (flexible discriminant analysis), ANN (artificial neural network), MARS (multivariate adaptive regression spline), RF (random forest), MaxEnt (maximum entropy), SRE (surface range envelope). Wild goat occurrence data were collected from 2019 to 2020 and related to five variables: predator distribution, slope, distance from road, distance from water resources, and normalized difference vegetation index (NDVI). We found that distribution of predators, slope, distance from water resources and distance from roads were the most important predictors of potential habitats for wild goats. The least important variable was NDVI. Probabilistic predictions suggest that only 5.89% of the area was highly suitable habitat, whereas 46.24% was unsuitable. Ensembling individually classified presence/absence maps further indicates that 79.72% of the study area was certainly unsuitable while only 8.06% was certainly suitable for wild goats. The results can help wildlife managers and policymakers work towards conservation and management goals and decreasing the conflicts between wildlife and humans. [ABSTRACT FROM AUTHOR]

In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings. [ABSTRACT FROM AUTHOR]

In recent decades, the earth's surface data have been collected more efficiently using remote sensing, which needs drought indexes update. In this study, soil moisture (SM) data were collected from the surface layer of a high humidity climate in northern Iran using Soil Moisture Active Passive (SMAP) and field measurement. After analyzing the data, we found that the average RMSE between the field and SMAP measurement was 0.054 m 3 m 3 . Considering the same agricultural land use and the strong correlation of 0.92 between them, the validated SMAP data were used to propose an agricultural drought index. After data validation, the extreme learning machine (ELM) model was put to the test using sigmoid, triangular, sine, and hard-limit activation functions. Of all the activation functions tested, the model with the sigmoid activation function yielded the lowest amount of error and was therefore chosen. Five years of continuous daily SM as a target, five-year daily normalized difference vegetation index, land surface temperature, and precipitation were inputs to predict one-year daily SM time series in the humid climate. From 2021 to 2022, daily surface SM was predicted with the average RMSE = 0.03 m 3 m 3 compared to the SMAP data. Finally, a new regional agricultural drought index based on 4 years of SMAP and 1-year prediction of SMAP from 2022 to 2023 was proposed. Further investigation is needed to conclude that the application of the presented index is reliable in other climates. [ABSTRACT FROM AUTHOR]

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine-learning-based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions, namely Iran, Mozambique, Sri-Lanka, and Sudan, where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction, and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 and 0.98 across the four test regions at hand. [ABSTRACT FROM AUTHOR]

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Introduction. Bronchopulmonary lophomoniasis (BPL) is a protozoan pulmonary disease that has been reported sporadically, but its incidence has been increasing. However, the epidemiology and risk factors of the disease have not been clearly identified. The current study aims to identify BPL cases molecularly and assess the demographic and some environmental factors for the first time on the prevalence of BPL as a national registry-based study in Iran. Methodology. The study tested 960 patients with lower respiratory tract symptoms whose bronchoalveolar lavage samples were submitted from seven provinces of Iran to the Iranian National Registry Center for Lophomoniasis. They were tested for BPL by a newly developed polymerase chain reaction test. The study assessed the association of Normalized difference vegetation index (NDVI), digital elevation model (DEM), and geographic latitude as environmental factors and sex and age as demographic factors on the prevalence of BPL. Geospatial information systems methods and chi-squared and Pearson's correlation tests were used for the assessment of geographical and environmental factor effects and statistical analysis, respectively. Results. Of the 960 patients, 218 (22.7%) tested positive for BPL; the highest and lowest prevalence rates were reported from the south and northeast of Iran, respectively. The study found a correlation between geographic latitude and age with BPL prevalence, but no association was found for gender, NDVI, or DEM. Most patients were over 40 years old, and the rate of disease was higher in southern latitudes. Conclusion. Age and geographical latitude were found to be risk factors for BPL. More exposure to dust and/or chronic pulmonary problems may explain the higher prevalence of the disease in older adults. Higher rates of BPL in lower latitudes may be due to warmer weather and longer days, which can confine individual activities indoors and result in more contact with domestic insects and infected dust. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for drought forecasting based on combining Long Short-Term Memory (LSTM) and Multi-Resolution Analysis Wavelet Transform (MRA-WT), called MRA-WT-LSTM. In fact, Deep Learning (DL) methods provided an outstanding performance in several forecasting fields, especially drought. LSTM has proved a high ability in dealing with time-series drought indices compared to the others existing methods. Therefore, in this study LSTM was used to provide long-term drought forecasts. However, the non-stationarity of the drought indices is a major challenge that needs to be taken into consideration. In this regard, MRA-WT was applied to analyze the non-stationary time-series drought indices. Experiments were carried out in the Sarab region, Iran in the period between 1988–2016 in order to predict the standardized precipitation Evaporation index (SPEI). Drought is the most natural hazards in Sarab region that affect the socioeconomic development. The input data include the station data (i.e. rainfall, temperature (mean, minimum, and maximum), humidity, pressure, and Evaporation) and Normalized Difference Vegetation Index (NDVI) derived from Landsat images. Through the experiment, the proposed methodology is evaluated compared to three ML methods (i.e. Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR)) and LSTM. The different models were compared in terms of statistical metrics such as the coefficient of determination ( R 2 ), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results revealed the effectiveness of MRA-WT-LSTM for drought forecasting with R 2 up to 0,93 and RMSE reached 0,02 for the different time lags. [ABSTRACT FROM AUTHOR]

Purpose: In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale. Design/methodology/approach: To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI. Findings: The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach. Originality/value: To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield. [ABSTRACT FROM AUTHOR]

The Hyrcanian Forests comprise a continuous 800-km belt of mostly deciduous broadleaf forests and are considered as Iran's most important vegetation region in terms of density, canopy cover and species diversity. One of the few evergreen species of the Hyrcanian Forests is the box tree (Buxus), which is seriously threatened by box blight disease and box tree moth outbreaks. Therefore, information on the spatial distribution of intact and infested box trees is essential for recovery monitoring, control treatment and management. To address this critical knowledge gap, we integrated a genetic algorithm (GA) with a support vector machine (SVM) ensemble classification based on the combination of leaf-off optical Sentinel-2 and radar Sentinel-1 data to map the spatial distribution of box tree mortality. We additionally considered the overstorey species composition to account for a potential impact of overstory stand composition on the spectral signature of understorey defoliation. We consequently defined target classes based on the combination of dominant overstorey trees (using two measures including the relative frequency and the diameter at breast height) and two defoliation levels of box trees (including dead and healthy box trees). Our classification workflow applied a GA to simultaneously derive optimal vegetation indices (VIs) and tuning parameters of the SVM. Then the distribution of box tree defoliation was mapped by an SVM ensemble with bagging using GA-optimized VIs and radar data. The GA results revealed that normalized difference vegetation index, red edge normalized difference vegetation index and green normalized difference vegetation index were appropriate for box tree defoliation mapping. An additional comparison of GA-SVM (using GA-optimized VIs and tuning parameters) with a simple SVM (using all VIs and user-based tuning parameters) showed that our suggested workflow performs notably better than the simple SVM (overall accuracy of 0.79 vs 0.74). Incorporating Sentinel-1 data to GA-SVM, marginally improved the performance of the model (overall accuracy: 0.80). The SVM ensemble model using Sentinel-2 and -1 data yielded high accuracies and low uncertainties in mapping of box tree defoliation. The results showed that infested box trees were mostly located at low elevations, low slope and facing north. We conclude that mortality of evergreen understorey tree species can be mapped with good accuracies using freely available satellite data if a suitable work-flow is applied. [ABSTRACT FROM AUTHOR]

Climate change and urbanization along with uncontrolled development in less developed countries have led to an increased ecosystems' thermal environment. Some factors such as environmental indices and landscape pattern changes can alter Land Surface Temperature (LST). Thus, the accurate evaluation of the relationship between these factors and LST is considered important for managing ecosystems, especially fragile ones under high stress. The southeast of Iran has witnessed many destructions in the environmental dimension in the past years. Moreover, this region has a low socio-economic situation, which increases the need to study in this region. In the present study, we used Landsat TM5 satellite images (1989), Landsat 8 OLI/TIRS ones (2019), and Google Earth Engine (GEE) system to prepare the maps of temporal-spatial LST changes, Land Use/Land Cover (LULC), and selected environmental indices including Normalized Difference Vegetation (NDVI), Built-up (NDBI), Water Indices (NDWI), Land Surface Moisture (LSM) and albedo. Then, the correlation levels of LST with the aforementioned indices were assessed by using Geographically Weighted Regression (GWR), as well as assessing LST variation following LULC change. In addition, the Moran index was used to analyze global and local spatial autocorrelation. The results represented an 8.67-degree increase in the mean LST during 1989–2019. Urban and built-up areas had a significant effect on increasing the temperature of the region. Additionally, water bodies and vegetation cover in the region were the most crucial parameters in LST reduction. All of the applied indices were strongly related to LST (>0.70), while some exhibited more correlation in each year. Further, the highest correlation of LST was observed with LSM and NDBI in 1989, as well as with NDVI and NDWI during 2019. In addition, the Moran index value reduced from 1989 to 2019 (from 0.93 to 0.89). Finally, the region rehabilitation based on sustainable development principles played an important role in the direct and indirect decrease in LST. [ABSTRACT FROM AUTHOR]

Drought as a natural disaster is one of the human's ecological, hydrological, agricultural, and economic concerns. In this study, multiscale intelligence methods were proposed for drought severity detection and mapping in the northwest part of Iran for the years of 2007 to 2020. In the modeling process two scenarios were considered and in-situ and remote sensing datasets were adopted with two machine learning models namely M5 Pruning tree (M5P) and Random Forest (RF). In the first scenario, the in-situ datasets including the precipitation, relative humidity, evaporation, and temperature were used as inputs of the intelligence models to assess drought severity in terms of the Standardized Precipitation Index. In the second scenario, the SM2RAIN-ASCAT precipitation product and Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) products of the MODIS were considered as inputs. During the drought severity modeling process, the input time series were first broken down into several subseries via the Variational Mode Decomposition; then, the most effective subseries were selected and imposed into the M5P and RF as inputs. Also, the potential of the relatively new TemperatureVegetation Water Stress Index (T-VWSI), which has developed based on the NDVI and LST, was assessed in drought severity monitoring. The results proved the appropriate efficiency of the proposed multiscale methods in effectively detecting drought severity. Also, it was observed that the T-VWSI could be successfully used for detecting drought occurrences in areas without meteorological datasets. [ABSTRACT FROM AUTHOR]

Wildfire has significant impact on plant phenology. The plants' phenological variables, derived from time series satellite data, can be monitored and the changes in satellite imagery may be used to identify the beginning, peak, and end of the growing season. This study investigated the use of remote sensing data and land surface phenology (LSP) parameters to evaluate the impacts of fire. The LSP parameters included the start of growing season (SOS), the length of the growing season (LOS), the end of the growing season (EOS), maximum greenness of the season (Gmax), and minimum greenery in the season (Gmin) in the fire-impacted, semiarid oak forests of Iran. These LSP parameters were extracted from multitemporal normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2) data, acquired from MODIS sensor images in Zagros of the Ilam province in western Iran. By extracting LSP indices from the NDVI and EVI2 data, the indices were compared between burned forest areas, areas surrounding the burned forests, and unburned areas and for timesteps representing pre-fire, fire (i.e., year of fire), and post-fire (i.e., 2 years) conditions. It was found that for the burned area, there were significant differences in Gmax and the day that Gmax occurred. Furthermore, there was also a significant difference in Gmin between the pre- and post-fire conditions when NDVI was used and a significant difference between Gmax when EVI2 was used. The results also showed that in both time series there was a significant difference between the burned and control area in terms of Gmax. In general, the results showed that the fire had a negative effect on LSP, but in the two years after the fire, there were signs of forest restoration. This study provides necessary information to inform forest and resource conservation and restoration programs. [ABSTRACT FROM AUTHOR]

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI*scaled, and ET-NDVIKc) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI*scaled estimations were close. ET-NDVIKc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVIKc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI*scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons. [ABSTRACT FROM AUTHOR]

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann–Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann–Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics. [ABSTRACT FROM AUTHOR]

Due to the importance of the forest fire susceptibility zonation for proper management of this environmental hazard, this study presents two different hybrids of artificial neural network (ANN) for spatial analysis of forest fire in northern Iran. To this end, ant colony optimization (ACO) and biogeography-based optimization (BBO) evolutionary algorithms are synthesized with ANN to optimize its computational parameters. In this work, slope aspect, elevation, land use, wind speed, soil type, plan curvature, temperature, distance to river, distance from road, distance from village, slope degree, topographic wetness index, annual mean evaporation, annual mean rainfall, and normalized difference vegetation index are considered as the forest fire ignition factors. Notably, the frequency ratio model is used to demonstrate the spatial interaction between the forest fire and ignition factors. The findings showed that the BBO and ACO could improve the accuracy of the ANN from 81.3% to 84.0 and 83.9%, respectively. Moreover, the ranking results (obtained by applying mean square error, area under the curve, and mean absolute error indices) revealed the superiority of the BBO-ANN. [ABSTRACT FROM AUTHOR]

Exploring spatial and temporal land-use changes is valuable for local governments to address issues of sustainability and planning policy where urbanization and industrialization are taking place. Besides anthropogenic effects, natural driving forces like climate change may also affect sustainability. However, such relationships have not been studied minutely. Hence, this study first investigates the land-use changes and their relationship with land surface temperature (LST) for the Shazand Watershed, Iran, in 1986, 1998, 2008, and 2016 coincided with supplementary industrialization stages. Furthermore, the relations among LST and other biophysical parameters, including Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI), and Normalized Difference Water Index (NDWI), were analyzed, and corresponding variations were explored. The results indicated that the mean LST of the study watershed has an increasing trend from 1986 to 2008 due to land-use change and drought intensification. Later, LST decreased in 2016. Lower LST was associated with irrigation farming and orchard, and higher LST was related to sparse oak forest areas. There was also a negative correlation between LST and NDVI. As a result, it was inferred that greenery declined LST. Conversely, a positive correlation was found between LST and NDBI resulting from the built-up areas. Since LST could influence biological, physical, chemical processes, it can therefore be supported as an effective index for environmental sustainability assessment. [ABSTRACT FROM AUTHOR]

Soil erosion is a major environmental concern because of its devastating effects on agriculture and food production worldwide. In our research, we developed a sophisticated and effective model to predict soil erosion risk and developed a risk map to aid decision making to address earth surface degradation as a means to preserve soil. We describe the modeling process adopting the ICONA (Instituto Nacional para la Conservacion de la Naturaleza) model, in conjunction with an Analytic Hierarchy Process referred to as the AHP, together with the application of a geodatabase and remote sensing data for the Rudbar basin, Guilan Province, Iran. The basin is highly sensitive to soil erosion. With advances in remote sensing and GIS, challenges of conventional methods can be overcome such as the time-consuming and data-intensive identification of soil erosion-prone areas. The land slope, the lithofacies, land-use/cover, vegetation cover, and a digital elevation model (DEM) were our key layer inputs for the model. The ASTER DEM data were used to extract the land slope layers while the land-use/cover layers were prepared using NDVI (Normalized Difference Vegetation Index) data from a Landsat 7 ETM + image of the year 2017. Land slope and lithofacies data were used to create soil erodibility map, whereas land-use/-cover, vegetation cover and the DEM were used to create a soil map for soil protection. The erodibility and the soil protection map were then overlapped to create a risk map for soil erosion. Zones of high to very high risk of erosion were identified in a quarter of our study area. We discussed the practical application of the results as a guideline to planning and implementation of effective regional land and water management strategies. [ABSTRACT FROM AUTHOR]

Water availability in lakes must be studied in order to better manage ecosystems within lake basins and meet economic development needs. Despite being Iran's largest lake, Lake Urmia's water level and surface area have declined dramatically over the past two decades. During the same period, Lake Van in Turkey maintained a relatively stable water level and surface area. As a result, comparing factors related to water level and surface area in these lakes, which have similar geographical and climate conditions but different management policies, can be an appropriate way to identify the causes of water declines in Lake Urmia. Comparing these variables may help explain observed differences in lake behavior between 2000 and 2016. Hydrometric and climatic parameters, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were used to achieve this goal. Changes in precipitation, temperature, and evapotranspiration in both lakes show essentially identical trends, but this is not a convincing explanation for Lake Urmia's water surface changes. The results revealed that dam construction and water diversion projects, the expansion of irrigated agriculture, and the lake's shallow depth in most parts were the primary causes of Lake Urmia's shrinkage compared to Lake Van. [ABSTRACT FROM AUTHOR]

Within water resources management, surface water area (SWA) variation plays a vital role in hydrological processes as well as in agriculture, environmental ecosystems, and ecological processes. The monitoring of long-term spatiotemporal SWA changes is even more critical within highly populated regions that have an arid or semi-arid climate, such as Iran. This paper examined variations in SWA in Iran from 1990 to 2021 using about 18,000 Landsat 5, 7, and 8 satellite images through the Google Earth Engine (GEE) cloud processing platform. To this end, the performance of twelve water mapping rules (WMRs) within remotely-sensed imagery was also evaluated. Our findings revealed that (1) methods which provide a higher separation (derived from transformed divergence (TD) and Jefferies–Matusita (JM) distances) between the two target classes (water and non-water) result in higher classification accuracy (overall accuracy (OA) and user accuracy (UA) of each class). (2) Near-infrared (NIR)-based WMRs are more accurate than short-wave infrared (SWIR)-based methods for arid regions. (3) The SWA in Iran has an overall downward trend (observed by linear regression (LR) and sequential Mann–Kendall (SQMK) tests). (4) Of the five major water basins, only the Persian Gulf Basin had an upward trend. (5) While temperature has trended upward, the precipitation and normalized difference vegetation index (NDVI), a measure of the country's greenness, have experienced a downward trend. (6) Precipitation showed the highest correlation with changes in SWA (r = 0.69). (7) Long-term changes in SWA were highly correlated (r = 0.98) with variations in the JRC world water map. [ABSTRACT FROM AUTHOR]

To studies a suggested method for estimating fractional green canopy cover (FGCC or CC) from normalized difference vegetation index (NDVI) in Iran, a research project was carried out on four wheat farms in the large-scale region, in Fars and Khuzestan provinces during four growing seasons from 2015 to 2019. Two different image classification methods were used to provide the CCs of farms. In both methods, R2s were greater than 0.95 and NRMSEs were less than 0.15. A simple regression equation was constructed between CC and NDVI of 64 Landsat 8-Oli images with high accuracy from 2015 to 2018. The R2s of CC-NDVI equations were 0.84, 0.85, 0.88, 0.80 and 0.76 in Dezful, Omidieh, Darab, Zarghan and all sites together. The simple regression equation was used to simulate CC in 2018-2019 for validating the equation and had a good agreement with the measurements, especially in using the general equation of all sites. The p-values of the four equations were less than 0.03. As a suggested method, the CC-NDVI equation can be used to predict CC from NDVI as a low-cost and less time-consuming method in the study area to use in crop growth studies, crop growth models and crop growth management. [ABSTRACT FROM AUTHOR]

Fasciolosis, a parasitic disease affecting humans and animals, is uniquely influenced by climatic and environmental factors. Gilan province in northern Iran is recognized as a high-endemic area for this parasite. This study aims to assess the prevalence of fasciolosis in Gilan province during the current period and forecast the distribution pattern of the parasite in future periods by analyzing climatic variables and identifying the most critical factors impacting Fasciola. To evaluate the present status of fasciolosis in Gilan, we collected 189 sheep fecal samples from different parts of the province and quantified eggs per gram of feces in each sample. Meteorological and environmental data were obtained and clipped to the study area. A total of 19 presence points were used to model the habitat suitability of Fasciola spp. through the maximum entropy (MaxEnt) algorithm, with jackknife analysis to determine variable importance. To project the potential distribution of Fasciola spp. in Gilan province under future scenarios, we employed MaxEnt using current (1970–2000) and projected climatic data based on three representative concentration pathway scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) to predict habitat suitability in 2030, 2050, and 2070. The results of this study indicate the proportion of Fasciola spp. infection was highest in Talesh (46.37 %) and Langarud (45.7 %), while Rudsar (0 %) and Shaft (16.25 %) exhibited the lowest infection rates in Gilan province. MaxEnt modeling highlighted the significance of bioclimatic variables, particularly those associated with vegetation and temperature, such as temperature seasonality (Bio4) and normalized difference vegetation index (NDVI). The ecological niche modeling illustrated that the highest potential distribution for Fasciola in Gilan province is concentrated in the north-western and central regions, exhibiting an 80–100 % potential. However, projections for the future indicate a decrease to less than 20 % suitability for most of the province under all three scenarios until 2070. This study provides valuable insights into the dynamic relationship between climatic variables and Fasciola distribution, enabling better preparedness and control strategies for this trematode in Gilan province and other regions with similar climates. • Gilan province in northern Iran is recognized as an endemic area for Fasciola spp. • Utilizing environmental and bioclimatic variables, MaxEnt model predicted high-risk areas for Fasciola spp. • Temperature seasonality (bio4) and NDVI are strongly linked to the distribution of Fasciola spp. • Under climate change scenarios, the presence of Fasciola spp. in Gilan province is predicted to decrease until 2070. [ABSTRACT FROM AUTHOR]

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran's greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices. [ABSTRACT FROM AUTHOR]

The preparation of land cover maps provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land cover maps using the classification of Normalized Difference Vegetation Index (NDVI) values and prepare land cover maps from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the map produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values was extracted in order to classify it and the obtained map was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0.75 and overall accuracy of 80.86%. The results of evaluating the accuracy of the NDVI Based land cover Classification (NBC) index also indicated that this map has extracted the land cover map with an overall accuracy of 83% and a Kappa coefficient of 0.77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values for the production of natural land cover maps and can be used in order to prepare these maps for geographic monitoring and achieving sustainable environmental development. [ABSTRACT FROM AUTHOR]

Due to climate change, drought events will probably occur more frequently and be more intense. Therefore, effective drought monitoring and assessment is vital in developing knowledge of drought, drought adaptation, and mitigatory actions. Remote sensing has been widely used for monitoring drought in recent years. In the current research, three groups of remote sensing indices, i.e. vegetation, thermal and moisture indices, were applied to determine the correlation between them and the standardized precipitation index (SPI) as drought index for the growing season (April to September) from 1999 to 2005 for rangeland areas in the Alborz province of Iran. The results indicated that normalized difference vegetation index (NDVI) (with a correlation coefficient of 0.74) and land surface temperature (LST) (with a correlation coefficient of 0.67) had the highest correlations with rainfall. Therefore, it concluded that the assumed indices are suitable for drought monitoring for this land use. Temporal analysis of the results showed that the best correlations of remote sensing indices belonged to the 6- and 9-month SPI and indicated the effect of long-term rainfall on plant growth. [ABSTRACT FROM AUTHOR]

This study used NDVI, ET, and LST satellite images collected by moderate resolution imaging spectroradiometer and tropical rainfall measuring mission sensors to investigate seasonal and yearly vegetation dynamics, and also the influence of climatological factors on it, in the area of the Caspian Sea Watersheds for 2001-2019. The relationships have been assessed using regression analysis and by calculating the anomalies. The results showed that in the winter there is a positive significant correlation between NDVI and ET, and also LST (R = 0.46 and 0.55, p-value = 0.05, respectively). In this season, the impact of precipitation on vegetation coverage should not be significant when LST is low, as was observed in the analysed case. In spring, the correlation between NDVI and ET and precipitation is positive and significant (R = 0.86 and 0.55, p-value = 0.05). In this season, the main factor controlling vegetation dynamics is precipitation, and LST's impact on vegetation coverage may be omitted when precipitation is much higher than usual. In the summer, the correlation between NDVI and ET is positive and significant (R = 0.70, p-value = 0.05), while the correlation between NDVI and LST is negative and significant (R = -0.45, p-value = 0.05). In this season, the main factor that controls vegetation coverage is LST. In the summer season, when precipitation is much higher than average, the impact of LST on vegetation growth is more pronounced. Also, higher than usual precipitation in the autumn is the reason for extended vegetation coverage in this season, which is mainly due to increased soil moisture. [ABSTRACT FROM AUTHOR]

A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale. [ABSTRACT FROM AUTHOR]

Soil erosion is a complex process with socio-economic and environmental impacts and acts as the main factor in land degradation in arid regions. We evaluated soil erosion risk in Iran's arid northeast based on the potential and actual soil erosion risks using the Coordination of Information on the Environment (CORINE) model in a Geographic Information System (GIS) environment to estimate the economic burden of soil erosion in the region. Soil erodibility, erosivity, topography, slope, and vegetation cover were used to evaluate actual and potential soil erosion risks. The map of potential soil erosion risk showed that 43.62% and 25.02% of the study area faces with low and moderate erosion risk, respectively. Moreover, 5.22% of the study area has a high-potential soil erosion risk. According to the actual map of soil erosion risk, about 78% of the region is categorized within the risk classes of moderate to high. The output of the model did not significantly differ from the observed conditions at 110 ground control points. Sensitivity analysis showed that potential soil erosion risk was most sensitive to soil texture, slope, and soil depth, and that vegetation factor played a significant role in determining actual soil erosion risk. Comparison of Normalized Difference Vegetation Index (NDVI) for the study area revealed a 2.57% decrease in vegetation cover during the three years leading to the study (2015–2018). The Mann–Whitney U test (P value = 0.17) confirmed a good match between actual conditions and model predictions. Potential damage and economic impacts from soil erosion to at-risk elements were evaluated at about 6.8 million dollars annually. This study illustrates how the CORINE model can identify areas threatened by soil erosion for management action. [ABSTRACT FROM AUTHOR]

This study developed a groundwater sustainable management index including components of environmental, social, economic and institutional responsibility. Four significant indicators: integrated groundwater footprint, population density, percentage of groundwater used for agricultural and industrial sectors, and density of observation wells were adopted and executed for 443 of Iran's aquifers. The results indicated that the groundwater sustainable management for 32% of Iran's aquifers is poor–very poor, and this index is more sensitive to economic and social indicators. Finally, an inverse correlation was observed between the groundwater sustainable management index values and the coefficient of variation of normalized difference vegetation index (R2 = −0.437). [ABSTRACT FROM AUTHOR]

Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT-EEMD-K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period. [ABSTRACT FROM AUTHOR]

Agricultural planning is a very complex task, since there are numerous goals, which should be achieved simultaneously, and various components and elements, which must be considered at the same time. The process of agricultural suitability evaluation for crop production requires specialized geo-environmental information and the expertise of a computer scientist to analyze and interpret the information. The main objective of this paper is to test a new model (based on Iranian ecological and FAO models) for ecological capability evaluation with geometric mean evaluation for better planning management of irrigated lands. Next, the proposed method was verified and compared with other well-known methods such as the Iranian ecological model with Boolean logic, arithmetic mean, and WLC. To test the models, we used the normalized difference vegetation index (NDVI). The test results indicated that the method revised by geometric mean evaluation (overall accuracy %=95 and Kappa coefficient =0.91) was the best among the used methods, and the arithmetic mean method (overall accuracy %=46 and Kappa coefficient =0) had the lowest accuracy. Thus, this method (Geometric mean evaluation) has high flexibility in locating agricultural lands. Overall, this study can be used as a basic method to evaluate ecological suitability for other regions with similar conditions owing to its simplicity and high precision. [ABSTRACT FROM AUTHOR]

Forest land has a vital role in our planet ecosystem health. Forest areas are under natural and human pressure worldwide. Pests may have irreparable damages to vegetation cover; Tortrix viridana is one of the most important pests in the western forests of Iran and is mainly hosted by oak trees. In this study the performance of Sentinel-2 images to detect infected oaks by T. viridana in the Zagros forest habitat was considered. Vegetation indices (VIs) were extracted from affected and non-affected areas by T. viridana. The indices indices included normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) and inverted red-edge chlorophyll index (IRECI) which were extracted from Sentinel-2 satellite images. The results of the present study show that VIs in affected and non-affected areas of the study site have significant differences at 99% of confidence level. In addition, the Spearman's correlation coefficients between the VIs values in the affected and non-affected were 0.213, 0.213, 0.168 and 0.121 for IPVI, NDVI, IRECI and SAVI, respectively. This shows that Sentinel-2 images can be used to detect pests in forest areas. [ABSTRACT FROM AUTHOR]

Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983-2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD-PE-AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is considered as one of the most significantly effective factors on the regional climate and ecology, playing an important role in connecting surface energy and water exchange. In mountainous regions, LST reveals lots of inconsistencies due to the effect of such factors as topography, vegetation, solar radiation, etc. We sought to investigate the the temporal and spatial variation LST in different years and its relationship with effective factors in 5 dimensions using Multiple statistical methods, the sepidan region in northwest Iran. The multi-factorial land use, topographic (elevation, slope, aspect), biophysical indices (normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference built up index (NDBI), and modified of normalized difference water index (MNDWI)), socio-economic (fossil fuel CO 2 emissions (FFCOE) and road density(RD)), and climate (temperature and solar radiation) was studied in the current research. To this end, Images of July 1998 and 2017 were extracted from Thematic Mapper (TM5) and Operational Land Imager/Thermal infrared sensors (OLI/TIRS8). Moreover, ordinary least squares regression (OLS), Best subset regression, and Hierarchical Partitioning Analysis (HP) were used to investigate the relationship between LST and relevant effective factors. The results indicated that the temperature range varied from 10 to 53 °C in the time period mentioned. The highest amount of LST was observed in barren land use and the lowest one was found in garden lands. An negative correlation was found between LST and elevation. On the other hand, the highest value of the Laps rate of surface temperature was observed in the southern aspects and the lowest one was observed in the western aspects. Furthermore, the highest and lowest values of lase rate were found in slopes less than 10°, and in 50 to 60-degree slopes, respectively. The results of the OLS correlation indicated a negative correlation between LST and NDVI, NDMI, and MNDWI, and a positive correlation of LST with climatic and socio-economic indicators. LST's highest and lowest correlations were found to be with vegetation (R2 = 0.95) and road density (R2 = 0.1). Finally, while in 1998 temperature and vegetation were identified as the most influential factors on LST, it was the elevation that was found to be the most effective factor on LST in 2017 with the effective rate of 82.72%. This study offers a valuable viewpoint on the temporal and spatial variations of LST, their complexity, and the environmental factors that affect them. The viewpoint could, therefore, be used for prospective studies on the analysis of the ecosystem's reaction to climate changes. [ABSTRACT FROM AUTHOR]

Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid area. [ABSTRACT FROM AUTHOR]

Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran's major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is one of the important factors in arid and semiarid areas, which describes the soil quality. Spatial estimation of SOM is important to understand the SOM storage and the emphasis of the SOM in the global carbon cycle and environmental issues. Mapping of SOM content can have significant uses in environmental modeling. In the current study, various methods have been evaluated for estimating the SOM content through soil samples and using auxiliary variables in the west of Eastern Azerbaijan province, Iran. In this study, support vector machine (SVM), multi-factor regression (MFR), and multi-factor weighted regression model (MWRM) approaches have been suggested for predicting and investigating the spatial distribution of SOM. In total, 155 surface soil samples (from the depth of 0 to 30 cm) were obtained. These soil samples were randomly divided into training data set (105 soil samples) and testing data set (50 samples). According to the results, SOM is affected by soil properties as well as environmental factors (normalized difference vegetation index (NDVI)). In this study, clay, silt/sand, NDVI, and soil moisture were used as auxiliary variables in the estimation of SOM. Three methods were compared to determine a suitable method for spatial estimation of SOM, and results showed that SVM has the lowest estimation error (RMSE = 0.100, MAE = 0.07, and MRE = 3.32) and highest regression coefficient (R2 = 0.719) during SOM estimation. The present results show the indirect effect of elevation by controlling auxiliary variables and confirm the importance of auxiliary variables in spatial distribution patterns of SOM. [ABSTRACT FROM AUTHOR]