Remotely sensed data capacities to assess soil degradation

AbstractThis research has tried to take advantage of the two-field based methodology in order to assess remote sensing data capacities for modeling soil degradation. Based on the findings of our investigation, preprocessing analysis types have not shown significant effects on the accuracy of the model. Conversely, type of indicators and indices of the used field based model has a large impact on the accuracy of the model. In addition, using some remote sensed indices such as iron oxide index and ferrous minerals index can help to improve modeling accuracy of some field indices of soil condition assessment. According to the results, the model capacities can significantly be improved by using time-series remotely sensed data compared with using single date data. In addition, if artificial neural networks are used on single remotely sensed data instead of multivariate linear regression, accuracy of the model can be increased dramatically because it helps the model to take the nonlinear form. However, if time series of remotely sensed data are used, the accuracy of the artificial neural network modeling is not much different from the accuracy of the regression model. It turned out to be contrary to what is thought, but according to our results, increasing the number of inputs to artificial neural network modeling in practice reduces the actual accuracy of the model

Analyzing the Trend of The Temperature Parameters Related to The Central Plateau of Iran Using a Time Series of Satellite Data

IntroductionThe temperature of the earth has been rising by about 0.74 degrees Celsius over the past century. A gradual increase in the average annual temperature has been reported by many researchers worldwide, while other reports suggest a decrease in this parameter. The assumption is that there will be more areas of the world experiencing higher temperatures. The climate changes are effectively represented by temperature changes, which is considered one of the main indicators in climate studies. The chemical composition of the atmosphere has changed because of the increase in human industrial activities, so it is responsible for unprecedented changes in the global climate in the past century. The increase in greenhouse gas concentration is the cause of this change. The evidence indicates that the increase in atmospheric gas concentration has caused a significant increase in global temperature. The use of thermal data from sensors is widely used in the study of terrestrial phenomena, as indicated by many studies. The temperature of the earth's surface is directly and indirectly linked to all human activities. It is still not possible to calculate the temperature of the earth's surface with perfect and accurate methods, but some sensors with suitable temporal, spectral, and spatial performance are able to take photos of the entire surface of the Earth. The study is more important due to the fact that various species of animals, such as Jebeer (belonging to the Bovidae), are exposed to climate changes in arid and desert areas. Due to its impact on humans, other creatures, and the entire environment, it is imperative to pay attention to climate change nowadays. In this regard, the main aim of the current study is to evaluate the LST (Land Surface Temperature) trends, changes, and temperature threats of the land surface in the Central Plateau of Iran. Time series remote sensing data of the MODIS (MOD11A2) sensor and Terra satellite, in 8 days with spatial resolution of 1km from 2002 to 2018 have been used. Material and Methods The current study has been focused on the central plateau of Iran. The central plateau of Iran lies within the arid lands belt of the northern hemisphere. The current study has been attempting to extract exact information from the images by employing specific techniques. To achieve this goal, the MOD11A2 product of Terra satellite MODIS sensor, the trend of temperature changes and time series construction of the significance of Man Kendall methods and linear correlation parameters such as maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature, minimum annually temperature for daily and nightly temperature were used in TerrSet software and Earth Trends Modeler section to extract significant increasing and decreasing areas. After identifying some parts of provinces with significant temperatures based on analysis and results, we can identify the vital numerical value of the temperature in each pixel of those significant parts in the next stage. This can be achieved by utilizing the difference between the final temperature and the initial temperature. Trend analysis was used to simulate daily and nightly temperature changes for parameters of maximum monthly temperature, maximum annually temperature based on maximum monthly temperature, median monthly temperature, maximum annually temperature based on median monthly temperature, minimum monthly temperature and minimum annual temperature. Results and DiscussionDaily temperature data in the Central Plateau of Iran, which includes monthly minimum temperature, annual minimum temperature, monthly maximum temperature, annual maximum temperature based on monthly maximum temperature, monthly median temperature and annual maximum temperature based on monthly median temperature, common in Semnan and Isfahan provinces, showed a significant increase in linear correlation according to the results. In Isfahan province, the linear correlation decreased significantly between the maximum annual temperature based on the maximum monthly temperature and the median monthly temperature. There was no significant trend in other provinces. The linear correlation between temperature data in Isfahan and Semnan provinces, including the minimum monthly, minimum annual, maximum annual, and median monthly temperature, decreased significantly. The linear correlation between average annual temperature, average monthly temperature, maximum annual temperature determined by maximum monthly temperature, average monthly temperature, and maximum annual temperature determined by median monthly temperature increased significantly in Yazd and Isfahan provinces. No significant trends were observed in other provinces. To estimate the amount and approximate number of significant increases and decreases, simulations of temperature changes were conducted. The range and approximate range of numbers for significant increase and decrease in temperature were calculated in degrees Celsius. In all analyses, the parts with higher temperatures had a reddish color. The intensity of the red color increased as the temperature increased, and as the temperature decreased, the red color became fainter and turned blue. The central plateau of Iran recorded a maximum temperature of 44C°and a minimum temperature of -7C°according to this study. The central plateau of Iran has three main provinces, which include Isfahan, Semnan, and Yazd. Considering the temperatures mentioned for these three provinces, the temperatures obtained from this study are very similar, which means that the conducted study is approved to a large extent. Animals are considered to be at risk due to temperature changes. Future research should emphasize the impact of climate change and temperature increase on the living conditions of various animals, particularly those found on the central plateau of Iran

Identification of potential dust sources using remote sensing data (Case Study: Alborz Province)

Dust is one of the important processes of arid and semiarid regions that its occurrence has increased in recent years in Iran. Identifying the dust and sand sources, it is the first step in the management and control of this phenomenon. Because of the arid and semiarid climates where dust phenomenon takes place, always there are large areas to monitor and control that practically makes it impossible to manage them. Therefore, reduce the candidate regions to actual sources is one of the main concerns of the researchers. In this paper, identification of potential dust sources using remotely sensed data has been studied. Various spectral indices of moisture and vegetation were applied on the OLI sensor data and finally, wetness spectral index of Tasseled Cap Transformation and DVI vegetation index were selected based on their variation in the study area and was applied on satellite images from 2013 to 2015 and credibility potential maps of moisture and vegetation was produced. Roughness index was applied on the ASTER digital elevation model and credibility potential map of roughness was produced. Erosion sensitivity map of rocks was produced using geological maps. Potential dust sources map was prepared with a combination of credibility potential maps in multi- criteria evaluation model and validate using field based and these areas were visited based on stratified random sampling scheme. Results showed that as well can be identified potential dust sources using satellite images and determining to apply various indices

Identification of the best algorithm for dust detection using MODIS data

Dust event is one of the atmospheric events of the world arid and semi-arid areas that had a significant increase in recent years and negative effects in different parts. In this study used MODIS data to identify and select the best algorithm for dust detection. For this purpose, three dust events of South West of Iran detected in 2012 using five different algorithms of dust detection including Ackerman BTD, Miller, dust index, TIIDI and DUST RGB methods, and methods compared. Studies show that methods of Ackerman BTD, Dust index, and Miller need to threshold regulation for each dust event; for this reason, suitable threshold was determined for each dust event using histogram method and dust identified. In addition, TIIDI method could separate dust phenomenon from other complications on the surface of the earth but as well could not identify dust on water. In DUST RGB method as well dust identified from other complication. In addition results of images classification and accuracy assessment showed that in all three dust events, DUST RGB method has maximum total accuracy among of other methods. Therefore, based on the results of matrix error and accuracy assessment, DUST RGB method was chosen as the best algorithm for dust detection

Development of Remote Sensing-Based Flood Estimation Methodology in Google Earth Engine

AbstractIntroduction: Flood is one of the main natural disasters in Iran, which has caused losses in different regions. The ability to produce accurate and timely flood assessments is an important safety tool for flood mitigation and response. Several methodologies have been developed to indicate the risks associated with flooding by using ground measurements. Satellite remote sensing data have been used for flood assessment because of their spatial resolution and capacity to provide information for areas of poor accessibility or lacking in ground measurements. High resolution satellite data is mainly useful for the spatial analysis of water pixels. When flood data (before and after of a flood event) are available, it is possible to classify land cover change, and thus identify which areas are flooded.Materials and Methods: The present study developed a methodology that uses Sentinel 1 images and global products to assess the losses caused by a flood in the province of Khuzestan (2020) and Chabahar-Konarak (2021-2022). In this study, in addition to Sentinel 1 satellite data, Landsat 8 satellite images have been used. The results of this research have turned into the development of a flood application in the Google Earth Engine software.Results: The results showed that the use of optically inactive images of this Landsat 8 or Sentinel 2 in cases where the cloud cover does not bother will increase the accuracy of the output. This issue is one of the specialization features in the conditions of uncertainty in determining the thresholds of changes in radar images. In the field of flood zone and subsequently estimation of losses from flood. Applying the method presented in the Google Earth Engine environment, due to the easy access to satellite images and global products, is a suitable solution for extracting the flood zone and subsequently estimating the agricultural and residential damages caused by floods.Discussion: Combining the information of radar and optical satellites can play an important role in the accuracy of the thresholds and extracting the flood zone. The limitations related to optical images such as cloud cover disturbances led to the use and evaluation of methodology based on radar images (without the use of optical images) in this research.  According to the research methodology, there is no need to prepare and collect land information and global products regarding the population and its spatial distribution, land cover, permanent water areas and the digital elevation model of the land, with appropriate spatial accuracy (all information with spatial accuracy less than 100 meters) has been used.  Fast access to processed satellite images, as well as general coding and processing of images and the implementation of the considered methodology in the Earth-engine environment are the main advantages of this study

Valuing the Economic Damage of the Dust Phenomenon on the Agricultural Sector (Case Study: Alborz Province, Iran)

Introduction: In recent years, the phenomenon of dust has become one of the most important environmental challenges around the world, which is one of its negative effects on the agricultural sector. Dust on agriculture can affect the growth and performance of plants by affecting the photosynthesis process and also by increasing the leaf temperature, and by providing favorable conditions for the occurrence of diseases and disrupting the population balance of pests against beneficial and predatory insects and Also, the effect of reducing the efficiency of spraying plants against pests and diseases creates the grounds for causing damage and reducing production in various agricultural and horticultural products. The purpose of this research is to estimate the willingness to pay to reduce the effects of dust on the agricultural sector of the centers of dust production and its surrounding areas in Alborz province.Materials and Methods: In order to achieve the goal of this research, first, according to the map of dust centers prepared in Alborz province, the affected villages were determined. Ahmedabad areas are affected by dust. Then, using the Contingent valuation method (CVM), the willingness of people to pay to prevent and reduce the negative effects of the dust phenomenon on the agricultural sector was calculated using 400 questionnaires. It should be noted that the number of questionnaires was determined by using the Cochrane relationship and the population of the villages affected by dust.Results: According to the results of this research, the correct percentage of the estimated willingness of farmers to pay in order to reduce dust damage is 73% in the derived model. Also, the amount of expected value (WTP) was calculated equal to 165423 rials (approximately equal to 5.5 dollars). According to the population of the affected area, the total value of preserving agricultural products against the phenomenon of dust is equal to 2743375032 rials ($9144.58) per year. The results showed that the variables of age, number of working people, education, income and environmental awareness index have a positive and significant effect on the willingness of farmers to pay to preserve agricultural products against dust. In other words, the increase in age, the number of working people in the family, the increase in the level of education, the increase in income and the increase in awareness of farmers increase their willingness to pay.Discussion: The evaluation of the willingness to pay of farmers in Alborz province in order to reduce the dust damage on the agricultural sector showed that increasing awareness, education and experience (age) increases the willingness of farmers to pay dust control costs. Therefore, it can be said that in order to control and stabilize the sources of dust production in Alborz province, the knowledge and awareness of farmers regarding the negative effects of dust on the agricultural sector should be increased by using advertising training classes and so on. As a result, the willingness to pay farmers will increase. Then, in the form of non-governmental organizations or cooperative projects with the government, he designed and implemented programs to control and stabilize dust production sources with the participation of farmers

Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas

Quantitative estimation of canopy biophysical variables are very important in different studies such as meteorology, agriculture and ecology, so knowledge of the spatial and temporal distribution of these variables would be highly beneficial. Meanwhile, remote sensing is known as an important source of information to estimate fractional vegetation cover in large areas. Today spectral indices have been very popular in the remote sensing of vegetation features. But often reflections of soil and rocks are much more than reflections of sparse vegetation in these areas, that makes separation of plant signals difficult. So in this study measured fractional vegetation cover of a desert area were evaluated with 20 vegetation indices in five different categories as the most appropriate category, or indicator for desert vegetation to be identified. The five categories were including: (1) conventional ratio and differential indices such as NDVI; (2) indices corrected and derived from the traditional indicators such as NDVIc and GNDVI; (3) soil reflectance adjusted indices such as SAVI; (4) triangle indices based on three discreet bands in their equation (Green, Red and NIR) like TVI; and (5) non-conventional ratio and differential indices such as CI. According to the results of this research, DVI index with 0.668 the coefficient of determination (R2) showed the best fractional vegetation cover estimation. But according to the sparse vegetation in desert areas and the results of this research it seems none of these indicators alone can accurately estimate the percentage of vegetation cover, however, to do a proper estimation it is possible to enter data of these indices in a multivariate regression model. Using this method enabled us to increase the coefficient of determination of fractional vegetation cover estimation model up to 0.797

Injury burden in individuals aged 50 years or older in the Eastern Mediterranean region, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Background: Injury poses a major threat to health and longevity in adults aged 50 years or older. The increased life expectancy in the Eastern Mediterranean region warrants a further understanding of the ageing population's inevitable changing health demands and challenges. We aimed to examine injury-related morbidity and mortality among adults aged 50 years or older in 22 Eastern Mediterranean countries. Methods: Drawing on data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we categorised the population into adults aged 50–69 years and adults aged 70 years and older. We examined estimates for transport injuries, self-harm injuries, and unintentional injuries for both age groups, with sex differences reported, and analysed the percentage changes from 1990 to 2019. We reported injury-related mortality rates and disability-adjusted life-years (DALYs). The Socio-demographic Index (SDI) and the Healthcare Access and Quality (HAQ) Index were used to better understand the association of socioeconomic factors and health-care system performance, respectively, with injuries and health status in older people. Healthy life expectancy (HALE) was compared with injury-related deaths and DALYs and to the SDI and HAQ Index to understand the effect of injuries on healthy ageing. Finally, risk factors for injury deaths between 1990 and 2019 were assessed. 95% uncertainty intervals (UIs) are given for all estimates. Findings: Estimated injury mortality rates in the Eastern Mediterranean region exceeded the global rates in 2019, with higher injury mortality rates in males than in females for both age groups. Transport injuries were the leading cause of deaths in adults aged 50–69 years (43·0 [95% UI 31·0–51·8] per 100 000 population) and in adults aged 70 years or older (66·2 [52·5–75·5] per 100 000 population), closely followed by conflict and terrorism for both age groups (10·2 [9·3–11·3] deaths per 100 000 population for 50–69 years and 45·7 [41·5–50·3] deaths per 100 000 population for ≥70 years). The highest annual percentage change in mortality rates due to injury was observed in Afghanistan among people aged 70 years or older (400·4% increase; mortality rate 1109·7 [1017·7–1214·7] per 100 000 population). The leading cause of DALYs was transport injuries for people aged 50–69 years (1798·8 [1394·1–2116·0] per 100 000 population) and unintentional injuries for those aged 70 years or older (2013·2 [1682·2–2408·7] per 100 000 population). The estimates for HALE at 50 years and at 70 years in the Eastern Mediterranean region were lower than global estimates. Eastern Mediterranean countries with the lowest SDIs and HAQ Index values had high prevalence of injury DALYs and ranked the lowest for HALE at 50 years of age and HALE at 70 years. The leading injury mortality risk factors were occupational exposure in people aged 50–69 years and low bone mineral density in those aged 70 years or older. Interpretation: Injuries still pose a real threat to people aged 50 years or older living in the Eastern Mediterranean region, mainly due to transport and violence-related injuries. Dedicated efforts should be implemented to devise injury prevention strategies that are appropriate for older adults and cost-effective injury programmes tailored to the needs and resources of local health-care systems, and to curtail injury-associated risk and promote healthy ageing. Funding: Bill & Melinda Gates Foundation