جستجوی مقالات مرتبط با کلیدواژه « Remote sensing » در نشریات گروه « محیط زیست »

Investigating changes in land cover is one of the most important aspects of natural resource management and environmental changes. With the increase of urban and rural areas and the necessities of human life, changes are made on the surface of the earth. These changes are caused by the conflict between the needs of human societies and environmental constraints on Earth. In this research, using remote sensing techniques, the monitoring of land surface changes in Khoda Afarin and Kalibar cities was investigated using remote sensing technology during a period of 20 years (2000-2020).

In order to perform statistical and visual analysis of MODIS sensor images installed on Terra and Aqua satellites, for the period of 2000 to 2020, the method of classification and extraction of the earth's surface cover was studied. The IGBP classification method was used in 17 classes to classify land use. Then, based on the model and classification method, they were processed in the Arc GIS software environment. The area of each land use was calculated in each year. Natural factors affecting land use changes were used from the daily rainfall data for the studied period. Based on these values, the trend of changes in precipitation and drought was drawn according to the SPI standard precipitation index in the software environment.

Twenty MODIS sensor images were analyzed for the period under study and annual maps and change trend charts of each land use were drawn. The land use coverage in the years 2001, 2002 and 2003 showed that the largest area was related to agriculture in 2001, followed by pasture. This trend continued from 2002 to 2003.. In these years, the process of forest destruction and change of use from forest to pasture and then agriculture has started. From 2004 to 2006, Bayer's unfavorable coverage is visible. In the years 2007 to 2009, regarding dam construction the water coverage and the area under cultivation increased and in the years 2010 to 2012, the city and man-made buildings increased significantly, and from 2012 to 2020, the process of destroying forests and turning them into pastures and then agricultural lands in the year. The end of the investigated period is also quite evident, and also by examining the natural factors, the amount of rainfall and drought during the 20-year period has had a downward trend.

The results show that the area of forest and shrub cover has decreased, and instead, there has been a significant increase in barren, water, pasture and agricultural cover, although the increase in water cover is due to the construction of dams in the study area. Also, the results of examining the amount of precipitation and drought show that the trend of precipitation is decreasing and relatively severe droughts have occurred in the region.

Climate change and global warming, caused by the increase in the concentrationof greenhouse gases, have garnered significant attention across various national and internationalsectors. The emission of greenhouse gases has long been recognized as one of the most pressingenvironmental issues, sparking widespread concern. Since the Industrial Revolution, the demandfor energy and the consumption of fossil fuels have escalated, leading to increased greenhouse gasemissions. While the input and output of atmospheric carbon dioxide have traditionally remainedin balance with nature, human activities and carbon dioxide emissions have disrupted thisequilibrium in recent decades, giving rise to climate change and global warming.

There are various methods for measuring atmospheric carbon dioxide.Remote sensing technology, in particular, has emerged as a solution, overcoming the limitationsof ground-based measurement methods by offering continuous monitoring and global coverageof greenhouse gases. Despite the absence of ground stations for monitoring greenhouse gases inthe Middle East region, spanning 7,207,570 square kilometers, this study investigates the monthly,seasonal, and annual atmospheric concentrations of carbon dioxide using data from satellites suchas SCIAMACHY, GOSAT, and OCO. The study period spans from 2003 to 2020.

The findings indicate a significant increase in atmospheric carbondioxide concentration over the 18 years examined by all three satellites. In addition toexamining annual changes, this study also investigated seasonal and monthly variations inatmospheric CO2 concentration. The lowest concentrations of this greenhouse gas occurredduring the summer months, particularly in August and September, while the highestconcentrations were observed during the spring months, specifically in April and May.Furthermore, the analysis of differences in atmospheric CO2 between seasons revealed the mostsignificant changes from spring to summer, with an average decrease of 6 ppm. Conversely,the highest increases in atmospheric CO2 between seasons were observed from summer toautumn, with a recorded average increase of approximately 4 ppm.

This research indicates a notable increase in atmospheric CO2 concentration in theMiddle East region from 2003 to 2020, accompanied by seasonal and monthly fluctuationsconsistent with global trends of this greenhouse gas. This long-term rise in greenhouse gaslevels can lead to various detrimental effects in the region, including temperature escalation,alterations in rainfall patterns, heightened drought severity, and damage to natural ecosystems.Consequently, the socio-economic stability of the region could be jeopardized, impactingagriculture, water resources, human health, and biodiversity. To effectively manage andmitigate greenhouse gas emissions, immediate action is imperative at both national andinternational levels. Such measures may involve promoting renewable energy sources,enhancing energy efficiency, curbing industrial pollution, advancing emission reductiontechnologies, and fostering regional and international collaboration in greenhouse gas reductionefforts. Moreover, public awareness campaigns and policy interventions are essential tomobilize stakeholders and facilitate the transition to a low-carbon economy in the Middle East,ensuring sustainable development and climate resilience for future generations.

One of the most important approaches to preserve and restore wetlands, is identifying environmental changes from past to present and developing an integrated management plan to control these changes and decision-making to provide solutions for improving the condition of these valuable ecosystems. Ovan Lake, as one of the beautiful and touristic landscapes in the forbidden hunting area of Eastern Qazvin, has distinct mountain habitats and various species of wildlife. By employing remote sensing techniques for a 30-year period, the process of changes and land use in the hydrological unit leading to Ovan Lake were identified and the trend of their changes was obtained quantitatively in this research. Then, the effect of the related hydromorphological indicators on the area and volume of the lake was investigated. The results showed that, according to the Modified Normalized Difference Water Index (MNDWI), the average area of the lake water zone was 8.15 hectares over the past eight years and based on univariate regressions, its hydrological regime is mainly related to two important factors of precipitation and evaporation. According to the univariate regressions demonstrate a significant relationship between the lake's hydrological regime and precipitation/evaporation rates. The evaporation parameter also showed a logical trend during the statistical years, so that the area and volume of the water zone of the lake has decreased by the increase of evaporation from the free surface of the water. Also, the results of multivariate regression between lake water volume and rainfall and evaporation components showed that the lake volume is more correlated with rainfall. But in contrast, evaporation changes with a greater slope or rate.

Water turbidity is one of the most important parameters of water quality, which represents the transparency of water and is effective in eutrophication. This research was done to estimate the amount of water turbidity using remote sensing data and the random forest technique. For this purpose, the water quality monitoring data of Chitgar Lake in Tehran were used, which is an artificial shallow lake with recreational and urban scenery usage. The Landsat 8 OLI/TIRS and Sentinel 2 MSI satellite images were extracted after matching the date of field observation data and satellite images from 2016 to 2021. Data were divided into calibration and validation datasets. After performing pre-processing processes on satellite images, important bands were recognized using the random forest method. Afterward, appropriate band composition and algorithms were selected and regression models were fitted and validated. The optimum model was able to estimate water turbidity with Adj.R2=0.6, RMSE=1.07 NTU, and NRMSE=12% for Landsat-8 as well as with Adj.R2=0.73, RMSE=1.23 NTU and NRMSE=9% for Sentinel-2 satellite and estimated with a power of 80% for Chitgar Lake. Consequently, the optimal predictive model in Sentinel-2 was chosen with the assistance of the random forest. Moreover, the predictive model was able to estimate the water turbidity in Chitgar Lake with acceptable accuracy.

Drought, as one of the major natural hazards, affects the environment, society, agriculture and economy. Several indices have been developed for drought quantification based on ground data and remote sensing. Traditional drought quantification methods are based on meteorological data and conventional criteria and are usually not available in near real- time. On the other hand, data based on remote sensing are continuously available and can be used to detect several aspects and characteristics of drought. The purpose of this research is to investigate and compare different indices derived from remote sensing and meteorological data for local scale drought monitoring (eastern part of Kurdistan Province).

Seven drought indices were compared, including Vegetation Condition Index (VCI), Vegetation Drought Index (VDI), Vegetation Health Index (VHI), Vegetation Supply Water Index (VSWI), Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Standardized Precipitation Index (SPI). Remote sensing indicators are derived from MODIS data. The meteorological index SPI is obtained by combining the data of rain gauge stations and gridded precipitation data. The digital maps of the seven drought indicators have been prepared for the period of 2002-2021 with the same time interval (16-days). To analyze the characteristics of each drought index, a comparative method including the selection of specific periods of drought and spatial drought identification characteristics has been used. The comparison of drought indicators was done May, which is the growing season. Finally, Pearson's correlation analysis was used to evaluate the behavioral similarity of the indicators.

The spatial comparative analysis between the drought indicators showed that all the indicators had certain adaptations in the distribution of the regional scale of drought, especially those derived from similar data sets. Meanwhile, the difference in local scale distribution was found among different groups of indicators. The results showed that the general trend of the VSWI had a better compliance with the standardized precipitation index. Based on the correlation analysis, it was proved that the VSWI  can be a better reflection of the amount of rainfall and the severity of drought due to the lack of rainfall. In addition, the land surface temperature (LST) contributes more to the VSWI results than the reflectance information. A two-period (32-day) delay of the indicators indicating the state of vegetation is a good indicator of the meteorological drought conditions in the study area. The absence and lack of rainfall in at least five periods (80 days) earlier can had a serious effect on the state of vegetation in the existing conditions. Plain and mountainside areas located in the central, eastern and south-eastern parts of the study area were more sensitive to drought conditions than other parts due to the dominance of grain farming, especially rainfed farming.

While remote sensing drought indicators have many advantages in the analysis of drought in real-time, meteorological drought indicators are still the priority for drought monitoring. This is due to the dependence of hydrological and agricultural systems on meteorological conditions. Mainly, these hydrological and agricultural systems in different regions respond to meteorological fluctuations with different time delays. Understanding these complex relationships between meteorological, hydrological and agricultural systems can be useful in early preparation programs against drought and its management.

Coastal areas worldwide hold significant social and economic importance. These areas, in addition to providing essential ecosystem services, have considerable potential for activities such as tourism, industry, and transportation, contributing significantly to the economic and social development of countries. Given that coastal areas are among the most complex ecosystems, they require monitoring and planning for enhanced protection. Timely and accurate identification of land use changes, which form the basis for better understanding human-nature interactions, provides the necessary groundwork for the efficient management and utilization of coastal resources.

The present study aims to investigate land use and land cover changes in the Chabahar coastal areas for four periods, including the years 1368, 1378, 1388, and 1398, using Landsat satellite time series data. Land cover mapping was performed through preprocessing and processing stages, followed by classification using a combined method. The accuracy of the produced maps was assessed using error matrix, Kappa index, and overall accuracy, which were greater than 80% and 0.8, respectively. Land use changes were analyzed using landscape metrics.

The results indicated that barren land had the highest land use percentage in all studied years. Saline and barren land cover decreased by 398.28 and 75.7 hectares, respectively, from 1368 to 1398, while aquatic land cover increased by 115.3 hectares in 1398 compared to 1368. The analysis of vegetation cover also revealed that this land use, along with human-made structures, consistently allocated the smallest area in all periods, gradually increasing. The accuracy assessment of land cover classification showed high accuracy in the produced maps, with the highest and lowest accuracy rates in 1368 (93.25%) and 1398 (85.6%), respectively. In 1398, the number of patches for aquatic land, vegetation cover, and human-made structures increased (830, 2662, and 4, respectively) compared to 1368 (146, 2386, and 1, respectively), while the number of saline and barren land patches decreased in 1398 (643 and 761, respectively) compared to 1368 (720 and 1060, respectively).

The study results demonstrated considerable changes in the Chabahar coastline over the past 30 years. The findings indicated a significant increase in vegetation cover from 1368 to 1398 (from 116.34 to 202.30 hectares), contributing a total of 85.96 hectares to land use. This increase in vegetation cover can be attributed to agricultural expansion. The detection results of changes in human-made areas also showed substantial growth (133.18, 205.74, 228.29, and 411.42 in 1368, 1378, 1388, and 1398, respectively) during the study period. Overall, it can be concluded that, based on the analysis of landscape metrics, the increase in human-made land uses and environmental changes has continued, and integrated management plans for coastal areas have not been adopted.

Water quality is the process to determine the chemical, physical and biological characteristics of water bodies and identifying the source of any possible pollution or contamination which might cause degradation of the water quality. Due to the rapid growth of industries, the disposal of liquid and solid wastes is increasing, thereby polluting soil and water. If the waste is not disposed of properly, then it percolates into the ground and causes problems like groundwater contamination, degradation of vegetation, soil contamination and modification of soil properties, etc. Nevertheless, traditional methods of water quality monitoring are often expensive and time-consuming. This is especially important for large water bodies such as lakes, dams, and rivers where sampling does not cover the entire body of water. Publicly available RS data are collected at regional scales and temporal resolutions (i.e., repeat collection time) that are much more frequent than field sampling campaigns. The physics and chemical characteristics of water can be determined from spectral signatures. Also, extracting water quality measurements directly from satellite imagery can allow rapid identification of impaired waters, potentially leading to faster responses by water agencies. Remote sensing data is an appropriate alternative to monitoring water resources due to its time and cost-effectiveness in a wide range of temporal and spatial scales. Currently, there are various types of remote sensing data such as hyperspectral and multispectral data that can be used to monitor and evaluate water quality. Geographical information systems (GIS) and remote sensing (RS) have been used extensively to assess the water quality all over the world. The Euphrates River is one of the most important rivers in Iraq, which has hosted various civilizations in the ancient Mesopotamia region since ancient times and is still of great importance to the urban and rural communities of Iraq. The Hillah River is one of the two main branches of the Euphrates River, which flows eastward by branching off from it. This river is the most important river in the Babylon governorate in Iraq, which passes through a wide area and several small streams flow from it to supply water to agricultural lands in other governorates. The Hillah River passes through several cities and is affected by industrial, agricultural, and domestic wastewater, which has received less attention than other areas of the Euphrates River. For this purpose, in this research, a detailed assessment of the quality and pollution of the Hillah River in the Babylon governorate is carried out using different methods of remote sensing, GIS, and field and laboratory operations to determine the quality of this river.the purpose of its performance is to assessment the quality of water and soil for the area of Hillah river in Babylon governorate in Iraq. The method of collecting data and information needed to perform quantitative and qualitative analyzes in research was based on field, laboratory and library operations, and various software tools were used in data processing. In order to determine and collect water and soil quality samples, field operations have been used. For this purpose, the area of Hillah city is considered as the base point and samples have been collected parallel to the river Hillah in the north and south of the city. Accordingly, in terms of number, distribution and accuracy in field sampling, 10 points were collected from the area by using Garmin handheld GPS device, 7 points were taken from water and 3 points were taken from the soil of the area. The field work to determine the sampling locations was based on several reconnaissance trips and as a result, the locations of the main water sampling stations were identified. Then, they visited the desired places twice a month, and each time they visited, relevant samples were taken. The samples were collected in standard plastic bottles with a capacity of 1.5 liters and their lids were tightly closed. Paying attention to the change in composition, soil samples were taken with a wider spatial distribution and from places with far distances from each other in the Hillah river basin, and the volume of each soil sample varied between 1 and 1.5 kg. Two different laboratories in Babylon governorate have been referred to perform quality tests on the collected samples. The laboratory measures have been carried out in two separate stages. In the first step, the measures of preparing the samples and separating them from each other have been carried out, which includes labeling, determining the date of water and soil samples, and classifying the samples for laboratory analysis. In the second stage, laboratory equipment and operations have been used for the qualitative analysis of the samples, and various devices such as CRISON have been used to test the physical and chemical parameters on the samples. Using laboratory tools and facilities, various physical and chemical variables of water quality have been measured based on the collected samples. For this purpose, 13 parameters have been tested on the samples. Electrical conductivity and total dissolved solids were measured using an EC meter and pH using a pH meter according to the relevant methods. The capacity of calcium and magnesium ions in water samples has been measured using the weighing method. Soluble sulfate, phosphate and nitrate were measured by a spectrophotometer. Sodium concentration in water was measured by flame photometer. Chloride in water was estimated from the scaling method using silver nitrate standard solution and using potassium chromate solution as the relevant guide and the results were expressed in ppm. Total hardness was measured as calcium and magnesium in water as milligrams per liter or ppm. Turbidimeteror is used to measure water turbidity. Finally, the iodometric method has been used to measure dissolved oxygen in water. In soil quality measurement, in addition to the parameters of electrical conductivity, pH, calcium, magnesium and sodium, which are also evaluated in the measurement of water samples, other parameters were also measured. including sodium absorption ratio (SAR) and calcium carbonate (CaCo). For the measurement of the mentioned two elements, special laboratory tools have been used like other elements. WQI index has been used to evaluate the water quality at the region of the Hillah river. For this purpose, the data related to the stations sampled from the water level were entered into the calculations of the WQI index, and based on this index, the water quality was evaluated on a monthly basis, and water quality maps were prepared for the region. The WQI index equation creates a range between 1 and 100, where 1 means the poorest and 100 the best water quality, and within this range, five classes are set to classify the water quality as very poor or inadequate, poor, moderately good, good and excellent. For satellite images processing, Landsat satellite imagery data provided by the United States Geological Survey (USGS) database archive has been used. In this regard, the image of Landsat 8 satellite OLI sensor for the date 2021/06/27 of the area has been selected as the main satellite data for processing.The research results can be presented in several sections. In the analysis of water quality in terms of quality parameters, it has been determined that, except for several cases in different months, in most cases, the concentration of chemical parameters of water did not exceed the permissible limit, and the physical parameters were appropriate. However, the results of the drinking water quality index have shown that the water of the Hillah River is at a poor and very poor level in terms of quality according to the location of the samples, and the spatial quality map of the Hillah river has also shown that the central to northern areas are of a more suitable quality than The southern regions have it, the main reason of which is the concentration of agricultural lands and the entry of waste and various effluents into the water in those areas. The results of evaluating the physical and chemical quality of soil in the studied area have also shown that soils with sandy texture are richer than mixed clay soils in terms of parameters such as electrical conductivity, sodium, calcium, magnesium, and SAR, and on the other hand, pH and calcium carbonate of Clay soils were more than sandy soils. The evaluation of the correlation of the parameters between the values of the water and soil samples has been done and the coefficient of the orrelation between them has been obtained, and in some cases, there has been a high correlation between the parameters. Finally, by evaluating the correlation between the quality parameters and the Landsat image bands in terms of combinations and band ratios, it has been determined that there was a direct correlation in a few cases, and on the other hand, the linear relationship also indicated the absence of a relationship between the WQI index and the spectral bands.

As a dynamic system, the river always changes its location and morphological characteristics according to time, geomorphic, geological, hydrological factors and sometimes due to human intervention. Masuleh Rudkhan is one of the most important rivers in Iran that the formation of bed geometry in different periods is very different from each other. This river has a dynamic morphological behavior under the influence of various factors such as the geology of the region, the characteristics of the alluvial structure, the hydrological characteristics of its upstream basin, the structures in it and the hydraulic conditions of the flow. The purpose of this paper was to identify and extract the changes of Masouleh Rudkhan in Guilan province in the period 2000 to 2020 using satellite image processing. The images used were the images of Landsat 5 satellite on 06/06/2000 and Landsat 8 on 13/06/2020. SVM classification method and NDWI, MNDWI, AWEI and WRI indices were used for image processing. Changes in the SVM classification method showed that the river area has decreased by 314.26 hectares from 2000 to 2020. These changes mean an increase in construction on the riverbed and a decrease in the amount of water in the river. Comparing kappa coefficient and overall image processing accuracy, it was observed that AWEI index with kappa coefficient and overall accuracy of 0.93 and 0.95 in 2000 and kappa coefficient and overall accuracy of 0.94 and 0.96 in 2020 had the highest accuracy and Masouleh Rudkhan route in this index to Google earth moved. By examining the river route in a period of 20 years, it was observed that at 5 km from the beginning of the basin, the river route is 100 meters to the south, at 7.5 km, the river route is 50 meters to the south, at 29.3 km, it is 45 meters to On the south side, at km 48, it is 38 meters to the north and at km 50 to 56, it has changed continuously and the reason for this change is the high erosion in this part of Masouleh River.

Updated and correct information is necessary for using and optimized managing of a land. Land cover map is one of the most important information resources in natural resource management. The goal of this research is to provide Katalan land cover map for investigating land use changes during 12 years in this area.

For this purpose, satellite images such as Landsat ETM 2001 and OLI 2013 were used after performing necessary corrections whereas; GPS and topographic maps were implemented for surveying fields and gathering trained samples. Land cover maps were provided using supervised classification method with maximum likelihood algorithm.

The results of this study revealed that the study area comprises six classes viz. irrigated farm land, rainfed farm land, bare land, rock stone, range land and mine class. The overall accuracy and kappa coefficient for 2013 map were estimated 86.11% and 0.82, respectively and theses values for 2001 land use map were 78.26%, and 0.71, respectively.

The results of this research revealed that the class of farm land, bare land and range land were increased 1.84%, 1.29%, and 1.21%from 2001 to 2013, and the class of rock stone and rainfed farmland were decreased 5.09%, and 0 .62%, respectively. Also, there was not mine class in 2001 but this class was 1.36% equivalent to 49.3939 hectare of the whole area in 2013.

Remote Sensing Technology is considered one of the most important sources of spatial and thematic data in the developed world of today. The objective of this work is a comparison of two different methods of change detection in forests using Landsat images. Therefore, sensor Landsat TM images of 1990 and 2011 (ETM+) satellite images have been used.

In the classification of images, the maximum likelihood algorithm, and artificial neural network to multilayer perceptron method were used.

Evaluated results showed that the algorithm approach, the maximum likelihood overall accuracy, and kappa coefficient maps classified in TM image, respectively, are 96.72 and 0.96 percent and image ETM+ 98.02 and 0.97 percent, and the method of artificial neural networks, overall accuracy and kappa coefficient map classified, TM image was 98.22 and 0.97% and ETM+ image was 98.34and 0.97 percent respectively. Following TM and ETM+ classification maps to detect the changes were marked and the map changes obtained.

The results of this study showed that using Landsat data along with data from have inventory capabilities of forest change mapping

The metropolis of Tehran as the largest capital of the Middle East is faced with phenomena such as environmental degradation, land use change and high concentration of agricultural and industrial disasters. Knowing the changes of land use in the past and predicting its future status is necessary in order to carry out a principled, dynamic planning. In this study, the spatio-temporal dynamics of land use changes in the Tehran in a 20-year period and the prediction of future changes in these land uses in the next 40-year were selected as the general objectives of this study.

After forming a database of Landsat 5 and 8 satellite images for three times of 2001, 2011 and 2021, the land use map of this times were prepared. For the validation of the maps Google Earth images, ground points and accuracy and Kappa coefficients were used. The time period from 2021 to 2061 was considered to predict future changes. In order to zoning and predict the future of land use changes, 6 land use change transfer sub-models with artificial neural network, Markov chain, and LCM model were used. Evaluation of the accuracy of the model was obtained from the comparison of the ground map of 2021, the future map of 2061, and the values ​​of Null success, success, Miss and False Alarm were obtained.

The results showed that the period from 2001 to 2021 was associated with the expansion of residential areas, the growth of urban areas and the reduction of green spaces including gardens and parks. The expansion of residential areas has been primarily in poor and barren soils and then in gardens and green spaces. This urban growth was clearly in region 5, 21, 22 and its physical development process was linear. The decrease in the level of gardens and green space is very catastrophic and this decrease is especially evident in the central areas of the city due to the high density of buildings. Urban parks are clearly in a complicated condition in the eastern areas of Tehran. The area of ​​rain fed agriculture has increased and the area of ​​barren soils and poor lands has decreased. Most of the changes in land use related to low capacity lands and agricultural lands have occurred. Studying the maps of the future of land cover showed the continuation of the same trend of the past 20 years. Although the growth of residential areas will be slower than the previous period, but the capacity and dimensions of the city will continue to increase, especially in the western, southern and southwestern regions. The decreasing trend of gardens, parks and urban green space is still observed. This process is more intense in the case of parks and they will be destroyed more quickly. The reason for this is besides drought and withering of trees due to climate change, pollution, and conversion of these green land uses to rain fed agriculture, parks, poor rangelands and urban areas. As for the urban green spaces, the 16 and 4 regions have the worst positions, and the 17, 19. 2, 5 and 22 regions will not be safe from this damage either. The decrease in the area of ​​ rangelands and cities moved to new areas will increase; the cycle of destruction of vegetation will increase from the outskirts of Tehran.

Construction was more in the south of Tehran and the decreasing trend of urban green space will continue to be observed. The central areas of Tehran will be completely devoid of trees due to the predominance of the urban areas, and the point to consider is the destruction of the green belt in the north of Tehran in the future.