جستجوی مقالات مرتبط با کلیدواژه « Satellite Imagery » در نشریات گروه « منابع طبیعی »

Plant residues can contribute to nutrient fertility in soil, increase soil organic matter concentration, maintain water in soil, reduce evaporation, stimulate microbial activity, increase grain size, decrease temperature fluctuations, improve the characteristics Physical, chemical and biological properties and improve soil plowing power. Crop residue burning is the factors affecting the emission of greenhouse gases and air pollutants. It is one of the fast and low-cost land preparation methods. This action causes environmental issues, as well as leads to changes in vegetation and soil. Hence, attention to this issue can be important in raising awareness and preventing the increase of its disadvantages. This research was carried out in order to estimate the area of agricultural land that post-harvest crop residue was burned, in the province of Kermanshah, Sarapul-Zahab

Field sampling operations were carried out on burned and unburned fields and their geographic coordinates were recorded. As a result, 255 burned fields and 163 non- burned fields were recorded. Considering the size of the grazing area of the study area, which is often smaller than 5 hectares, in this research, the images of LANDSAT 8, OIL / TIRS sensor with 30 m spatial resolution were obtained from the studied area. After atmospheric correction by FLAASH method, for selecting the best contrast, the contrast enhancement methods of optimum index factor (OIF), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) were used. The classification of images was done using the maximum likelihood method.

The results showed that the minimum values of the bands 1 and 2 were negative, so the data were normalized and the negative values of the bands was corrected. Based on the results obtained from the optimum index factor (OIF), the combination of bands 7, 5, 1 for all the examined dates, was chosen as the best band combination. After band combination based on the OIF index the burnt lands were separated by dark gray color and the vegetation of the cultivated lands were also separated by green color. The city and water could also be recognized in this image. The results of PCA and MNF Contrast enhancement methods also showed that the use of these methods, like the OIF band combination method, leads to a good enhancement of ground features in the images. After applying the maximum likelihood method to objects classification, The accuracy of the classification was evaluated by checking the correlation between the observed values (burnt and unburnt fields) and the classified values (burnt and unburnt places resulting from the image classification process). The methods of OIF and MNF (coefficients of Phi= 0.80, Cramer's V= 0.80 and Contingency Coefficient = 0.62) had significant correlations and more accurate than the PCA method (coefficients of Phi= 0.68, Cramer's V= 0.68 and Contingency Coefficient = 0.56). Accordingly, the area of irrigated agricultural field which burned after harvest was estimated at 7380 hectares, which included 56 percent of the County’s total irrigated field.

Based on the obtained results, the use of LANDSAT 8 satellite images can be used with high accuracy to identify burnt lands using enhancement methods OIF and MNF. A high percentage of irrigated lands in Sarpol Zahab city were burned in late spring and early summer for the next cultivation land preparation. In the long term, this can endanger the stability of production in agricultural ecosystems and also cause the release of greenhouse gases and various pollutants into the atmosphere, which in turn can cause air pollution and increase health problems for the inhabitants, which requires more attention of managers in this regard.

Background and Objective:

 Due to the increasing degradation at the level of the natural ecosystem, the amount and location of land use changes and predicting its future growth trend, I can provide the information I need to planners and managers. In this study, in order to change the current changes and predict the future in the Siahkal range, forecasting and changing the nose were done with Landsat images. There are various methods for predicting land use change. Processes for predicting and modelling land use change, such as urban growth and development, deforestation, etc., are considered powerful tools in managing natural resources and changing the state of the environment. This change reflects how humans interact with their environment, and its modelling has had an impact on settlement and macro-planning. In this research, due to the high capabilities of remote sensing and modelling tools and predicting changes in change using automatic-Markov cells in forests in northern Iran.

Materials and Methods:

 In this research, Landsat 5 images, 2000 TM sensor, Landsat 7 ETM+ sensor 2010 and Landsat 8 OLI sensor 2018 are used. In the preprocessing stage, errors in raw data such as radiometric, atmospheric, geometric, etc. errors are corrected. Was significant but had a radiometric error. 84 points are used for forest use, 76 points for thin forest water, 31 points for consumption and 2 required sensitivities to indicate a specific level of land cover. Land cover is defined into five classes: dense forest, semi-dense forest, sparse forest, urban area and agricultural area. The ENVI Remote sensing Software defines four types of kernels for the support vector machine in the SVM classification section: Polynomial, Sigmoid torsion, and FBCTION (RBF). According to the best kernel studies for land use classification, the radial kernel (RBF) has been proposed. In the present study, this kernel was used for classification. The classification of the appropriate band composition that you want to separate these classes for visual interpretation was selected by the spectral mean plot. This is done by the complex OIF index. After the extraction of land uses by the method, the results were evaluated accurately. Maps are prepared by land use, then with the GPS position of the earth, the map of the situation in the visible area and using the formed error matrix of kappa weakness and its overall accuracy obtained for this work, 200 points are randomly created on the images. The use of these points was determined by field visits and topographic maps of the surveying organization. Land use classification models are prepared, for modelling and land use changes are entered into office software to design land use changes in the required years. Degree of land use change modelling The LCM model was used in the Idrisi software environment. The Markov-CA model is a combination of automated cells, Markov chains, and multi-purpose land allocation. The Markov model also shows each user by generating a set of status probability images from the transfer probability matrix. In the last step of the structural model, using the transfer area matrix in the CA Markov model, a simulated simulation of future land use can be obtained. In this research, the land use map of 2010 and 2018 was used to predict the 2028 map. And in order to accurately review the forecast by CA Markov using the user map for 2000 and 2010, the map for 2018 has been predicted and increased by the map obtained from the classified level for this year.

Results and Discussion:

 The classification accuracy test was obtained using the Kappa coefficient index and overall accuracy. Kappa coefficient and overall accuracy were 0.88 and 0.89 for the image of 2000, 0.91 and 0.92 for the image of 2010, and 0.93 and 0.95 for the image of 2018, respectively. The images are categorized as entered into the software and processed by changing the LCM. Changes in the LCM model showed that during the years 2000 to 2018, more changes were related to the conversion of semi-dense forest land with an area of 42104.27 hectares. Urban land use change has also increased in the years of many studies and amounted to 148.14 hectares. The table of the probability of land use changes in the Markov production model and with the production map at this stage, for the years of Markov forecast studies for 2018 and 2028 showed that in 2028 the urban class area increased to 21293.1 hectares and the valuable land use area of dense forest to 2189.97 hectares will be reduced.

Conclusion:

 In order to prevent the uncontrolled expansion of cities, residential areas and the destruction of forest areas and vegetation, management measures should be taken and management decisions should be made. The level of dense and semi-dense forests in areas with high slopes will decrease further by 2028. Urban land use changes have also increased in the study years and amounted to 148.14 hectares. The results of surveying the area of forecasting classes showed that in 2028, the area of urban classrooms will increase to 21293.1 hectares and the valuable land use area of dense forests will decrease to 2189.97. The ability of the vector machine model in determining land cover/land use, vegetation and forest cover in different regions of Iran has been proven by other researchers. Remote sensing tools can be an important arm in information production in natural resource management.

The seas and oceans play an important role in climate conditions as well as climate change. In addition, physical and biological phenomena are among the most important factors affecting the chemistry and environment of the sea. Therefore, it is important to know the physical processes that govern the seas and oceans, as well as the correlation between these properties and biological properties. Remote sensing algorithms use a close range of blue, green, yellow, red, and infrared, so monitoring of chlorophyll-A, the phytoplankton pigment of oceanic and coastal waters, can be measured and evaluated using state-of-the-art remote sensing technology.

In this study, the capability of remote sensing methods has been used to investigate the status of coastal water quality characteristics of Sistan and Baluchestan provinces. For this purpose, the status of chlorophyll-A has been used using OC3 bio-optical algorithms in ENVI as well as the predecessors of the Google Earth Engine platform. Google Earth Engine is an open-source spatial analysis platform that enables users to visualize and analyze planetary satellite images. Using this system, various spectral processes can be performed on different surface phenomena with different satellite data. It is also possible to perform calculations on large volumes of data without the need for high-power systems. The salinity parameter of MIRAS's SMOS satellite was used in SNAP software to investigate the parameters of chlorophyll, temperature, and organic carbon using Terra's MODIS satellite images. The time to be studied in the images used and field sampling is May 2020. In order to extract the concentration of chlorophyll-A, bio-optical algorithms based on blue and green bands (OC3) were used in ENVI software. Bio-optical models combine optical measurements of reflection or radiation with biological parameters such as chlorophyll concentration, water quality, and more. Water temperature is one of the most important factors in the life of the sea, so those marine animals can survive and reproduce only in a certain range of water temperatures. Therefore, phytoplankton is very sensitive to changes in water temperature and react to temperature. Water level can determine their frequency and distribution. In this study, the product MIR_OSUDP2 of the SMOS satellite of MIRAS on 3rd of May 2020, for the study area from  https://smos-diss.eo.esa.int/ was used.

The results showed that the amount of chlorophyll-A is higher along the shores and the stations near Joud and the estuary has a higher concentration of chlorophyll-A. The results showed the outputs of two different methods for estimating chlorophyll-A in the study area are similar. Also, according to the results, it is clear that the amount of chlorophyll-A has increased in Chabahar, Konarak, Jude, and Goater stations in recent years. In Chabahar and Konarak regions, this increase has been significant for ten years, and the sudden increase in chlorophyll in recent years in field stations requires more studies to identify the causes and should be considered. The chart below shows the rate of change in chlorophyll-A from 2019 to 2020. According to the results, the amount of organic carbon follows the amount of chlorophyll-A and in areas such as Chabahar and Konarak we see higher levels of organic carbon. Also, the highest increase in temperature in all three periods studied was in Chabahar and Konarak ports, of which human activities are one of the main factors. Also, by examining the ten-year trend, increasing temperature changes can be seen in the ports of Maidan and Jude. The general trend of temperature is decreasing to the east as expected because it is closer to open waters. Seasons when water temperatures are lower, chlorophyll-A levels are higher. Chlorophyll-A map output results by ENVI software and Google Earth Engine platform, chlorophyll-A concentrations were higher in autumn and winter than in spring and summer, high chlorophyll-A-concentrations are common in cold tropical and subtropical seasons. Also, the concentration of chlorophyll-A in the study areas along the coast is higher than the offshore areas, which is related to the chlorophyll-A harvesting algorithm in type 1 waters; In other words, coastal areas have more value than offshore areas due to shallow depth, high turbidity and suspended sediments. Because there is no river discharge in this area, these areas are mostly affected by hydrodynamic processes such as wind direction and sea currents. The lowest chlorophyll-A concentrations were observed in the region from May to September, which was contrary to fluctuations in water surface temperature, which could be due to rising currents. The amount of organic carbon is one of the most important factors for evaluating the performance of aquatic ecosystems, which determines the potential of ecosystems for fishery products; The results of the study of organic carbon showed that the amount of organic carbon as chlorophyll-A in the two seasons of autumn and winter was higher than spring and summer so that the trend of changes in organic carbon also followed the trend of changes in chlorophyll-A. There is a correlation between temperature fluctuations and chlorophyll-A, this correlation indicates the importance of water surface temperature in changes in the growth rate of phytoplankton as one of the climatic factors and has made the most important parameter affecting chlorophyll-A, water surface temperature. According to the obtained results, the trend of temperature changes in the last ten years is increasing and the hottest stations are Chabahar and Konarak stations. In terms of salinity, areas with lower salinity had higher chlorophyll-A levels. Comparison of the data obtained from this study with the above indicates that the range of recorded fluctuations of the quality parameters studied in the natural waters of the region and is consistent with similar studies in the study area by other experts.

The results of this study show the acceptable accuracy of the results compared to the data of similar researchers in addition to the speed and ease of the method. Therefore, with the help of remote sensing science, timely monitoring of the quality parameters of water areas can prevent major crises and save time and money, problems that may be irreversible if they occur.

Mapping soil salinity changes on a large scale is very costly and time consuming. One of the most cost-effective ways to map soil salinity is to use satellite imagery. Investigation of spectral characteristics of soils with saline surfaces and determining the extent of areas affected by this phenomenon, by using new images with relatively high spatial and spectral resolution can be used in preparing soil salinity maps. In recent years, many indicators have been developed to detect and extract the characteristics of saline surfaces from satellite images, most of which have been studied based on the spectral reflection of these surfaces in different bands of satellite images or intermediate ratios. The purpose of this study is to estimate the salinity changes from 2001 to 2016 in Miandoab region located in West Azerbaijan province. For this purpose, 42 samples with appropriate distribution were taken from the area. The samples were then transferred to the laboratory to measure soil salinity parameters. To evaluate soil salinity, in addition to sampling of topsoil, the NDSI, IBI, NDVI, SI7 and NIR, RED, SWIR2 bands of Landsat 7 for 2001 and Landsat 8 for 2016 were used. The regression equation for determining soil salinity for 2001 showed that soil salinity has a positive and significant correlation with NDSI index at the level of 5%, While for 2016, the highest significant correlation at the 5% level was with the RED and NIR bands of the Landsat 8 image. In 2001, the highest and lowest lands in the region were non-saline lands (39%) and very saline lands (12%), respectively; While in 2016, the highest and lowest lands in the region are relatively saline lands (35%) and non-saline lands (18%). The results of the present study showed that the salinity of the region can be easily calculated using Remote sensing indices. Due to its proximity to Lake Urmia, the study area faces soil salinity problems, so the identification and classification of saline areas is necessary for land management.