جستجوی مقالات مرتبط با کلیدواژه "تصاویر لندست" در نشریات گروه "آب و خاک"

Soil salinization is a global environmental problem with serious economic, social and economic consequences. Measuring soil salinity includes the concentration of all salts dissolved in the soil, generally expressed in units of electrical conductivity (EC). Determining where, when, and how soil salinity occurs is essential to determining the sustainability of land use and development systems. Due to the ability to repeat and capture a wide range of remote sensing images, this technology will be useful for detecting changes even in short periods of time, and as an essential tool in monitoring soil salinity, it provides very valuable information on the size of the captured pixels. Flooding from this rainy season can have a large impact on soil salinity. The purpose of this research is to extract soil surface salinity with high spatial resolution and the effect of heavy rains and spatial analysis of the resulting anomalies in Fars Province using satellite image processing.

Fars Province is located in the south of the central region of Iran with an area of 122.799 square kilometers. The topography of the province consists of mountains and plains. In this province, eight million ha of land are suitable for agriculture and gardens, although only 1.6 million hectares have been used. The agricultural sector in Fars Province, which accounts for a major share of the national gross product, plays one of the most important roles in Iran's production, employment, and food security, so many of the province's agricultural products, such as cereals and citrus fruits, rank first to third in the country. Since our study was carried out in a wide area of the country, it was decided to use Google Earth Engine (GEE) as an open-source platform. Also, the Generalized Difference Vegetation Index (GDVI) prepared by Wu (2014) was used to analyze soil salinity. In order to evaluate the efficiency of the obtained model, R2 and RMSE indices were used. In order to verify the output of the field data collected from the Agricultural and Natural Resources Research Center of Fars Province, which was used as a ground sample for the evaluation By using spatial analysis in the form of geostatistics, spatial structures can be identified and spatial planning can be done.

Results and Discussiom:

For the studied area, soil salinity was between 7.01 and 53.63 decisiemens/meter. The difference between the highest and the lowest soil salinity in the study area is approximately seven times, the highest value being in the east and south of the province in the cities of Niriz, Larestan, Lamard and Zarindasht. A significant point is the sharp increase in soil salinity in the bed of rivers leading to Bakhtegan and Tashk lakes. According to the available ground data, the accuracy of the map was checked, and the square root of the error and the correlation coefficient were calculated as 0.33 and 0.59, respectively. Then, the soil salinity map was extracted using the same algorithm in the period of Farudin 2018 due to the heavy rains that were associated with the arrival of numerous rain systems in Iran. Soil salinity was obtained between 6.35 and 47.9 and was classified into five classes. The results showed that changes have been made in the minimum and maximum values ​​of salinity and soil salinity levels and soil salinity has decreased especially in the south of the province.Then soil salinity anomaly was obtained and spatially analyzed. The term soil salinity anomaly means deviation from the reference value or long-term average. The results showed that the amount of abnormality increased and decreased between 0.8 and -0.9 in the province. Areas with lower salinity have experienced a greater share of positive anomalies. The positive anomaly was mostly around Darab, Zanian and Babamonir in the northeast of Jahrom. The southern and eastern parts, including Lar, Ozer, Rastaq, Ahl and Lamard, which were in the medium salinity class, have suffered less salinity anomalies. In order to understand the cluster or scatter pattern of soil salinity changes, Moran's spatial autocorrelation coefficient was investigated. The results showed that the anomaly of salinity distribution in the rainy year has a cluster pattern. By examining the available maps, it can be said that the clusters of soil salinity anomalies are mostly located in the north of the province, Baba Monir and Zarian at higher altitudes of the province and to some extent in the south of the province around Darab and Jahrom. Also, a little clustering has occurred in terms of anomalies in the plains of the province; That is, the rains could not cause major changes in the soil salinity of the plains.

In this research, the soil salinity map using GDVI in two time periods before and after the heavy rains of the water year 1398-1397, using the open source platform Google Earth Engine, extracting and changing the soil salinity classes and converting the salinity classes to each other, as well as the method of spatial clustering. The salinity anomaly was investigated. Soil salinity for the studied area was calculated between 7.01 and 53.63 decisiemens/meter with square error and correlation coefficient of 0.331 and 0.59, respectively. Soil salinity has changed between 6.35 and 47.9 after heavy rains. The most changes due to heavy rains are related to the low salinity layer with 19% and the least changes are related to the very saline layer with 0.3%. The amount of anomaly between 0.8 and 0.9 decisiemens per meter was increasing in the center of the province around Bakhtegan and Tashk lakes and the western highlands of the province and decreasing in the south and east of the province. Areas with lower salinity contribute more positive anomaly. The southern and eastern parts, which have high and very high salinity, undergo less changes. The results of this study showed that the use of remote sensing and satellite data in the Google Earth Engine cloud system and spatial analysis to prepare soil salinity maps in areas that have a large area and are affected by salinity changes, has great financial and time savings, and in Areas where sampling is not done or is associated with issues can be very efficient. Such researches can easily and quickly identify areas that are most exposed to increasing or decreasing soil salinity and can be used in environmental planning to implement preventive measures.

Potential evapotranspiration is one of the most important components in water balance equation in a watershed or a plain. ETP measurement is a costly and time-consuming process. Therefore, remote sensing allowed us to estimate the surface energy considering the energy balance in a small area in order to calculate evapotranspiration (ET). Thus in this study, the Priestly-Taylor equation associated with Landsat 7 and 8 were taken into account for crop ETP calculation. The remotely sensed ETP algorithm was evaluated against four different ETP calculation approaches including radiation approach (Priestly-Taylor), aerodynamic approach, combination approach (Penman), and temperature approach (Hargreaves). These approaches were conducted using meteorological data obtained from seven stations around the Qazvin plain. Results showed that this algorithm could properly estimate ETP, and had the best relationship with ETP calculated from the Priestly-Taylor equation with R2 of 0.95 and RMSE of 0.6. Also, this algorithm could detect the crop on the ground and presented more actual values of ETP compared with ETPs calculated from meteorological data. Therefore, this algorithm could estimate ETP more accurately by distinguishing the dense of vegetation on the ground.

Primary and secondary soil salinity is one of the main reasons of land degradation and desertification in arid and semi-arid zones. Change detection of salt-affected soils, aides finding salt distribution and salinization trends. Present study was arranged for monitoring soil salinity status of agricultural lands located on southern part of Bakhtegan Lake in Fars province, Iran using remote sensing techniques in last two decades.
To fulfill the goal, Electrical conductivity data from technical reports on the years 1995 and 2007 was obtained and soil sampling was done on 2014. Composite and randomly 0-30 cm soil samples were collected. Electrical conductivity of 1:2.5 soil: water extracts were measured. Landsat 5 data scanned in 1995 and 2007 and Landsat 8 data scanned in 2014 were used. Different indices and false color composite images were calculated with ILWIS 3.3 software. For year 2014, principal component analysis was done, too. Before correlation and regression analysis, digital number of satellite bands transformed to atmospheric reflectance. 80 percent of data was used for classification of imagery data and 20 percent was used for validation the images for all procedures. Salinity maps was calculated, compared and the best model was chosen.
Results showed that the minimum Mahalanobis distance method (with overall accuracy of 62.5%) and maximum likelihood method (with overall accuracy of 44.8%) were the best approaches for mapping soil salinity for years 1995 and 2014, respectively, while the linear regression model (with overall accuracy of 62.5%) increased the accuracy of image classification compare to other methods. Statistical description of soil EC showed that in 1995 more than 50 percent of studied area has Electrical Conductivity less than 2 dSm-1, while in 2014 more than 50 percent of the soils have EC more than 4 dSm-1. Results also showed that during 20 years, salinity expansion was from soils around lake and center part of basin to the Neyriz city.
the present study showed that, during 20 years, the area of soils with salinity less than 4 dsm-1 reduced and the area of saline soils increased and salinity expansion was from Bakhtaran lake to Neyriz city. According to soil salinization, it seems, on the case of continuation of the present land management in studied area, in a short time, the existing orchards would be out of economic cycle. Therefore, to prevent the soil salinization and the loss of farm and orchards in the region, serious efforts of authorities requires.

One of the major challenges in water resources management is the operation of trans boundary watershed. This has been experienced in case of Helmand River between Iran and Afghanistan since the last century. For such a situation, application of a conceptual rainfall-runoff models that can simulate management scenarios is a relevant tool. The SWAT model can be a relevant option in this regard. However, the required hydro-climatic data for them is a serious obstacle. Especially, this problem gets exacerbated in the case of Afghanistan with poor infrastructures. So, application of this type of model would be more problematic. This paper aims to investigate capabilities of SWAT for the simulation of rainfall-runoff processes in such a data-scarce region and the upper catchment of Helmand River is used as the case study. For this purpose, discharge data of Dehraut station from 1969 to 1979 along with some metrological data were prepared and used to calibrate and validate the simulations. The results were acceptable and the coefficients of determinations (R2) during calibration and validation periods were 0.76 and 0.70, respectively. Notably, with respect to snowy condition of the basin, the elevation band option of the snow module of model had a significant effect on the results, especially in the base flows. Moreover, two Landsat satellite images during February 1973 and 1977 when the basin was partly covered with snow was prepared and compared with the SWAT outputs. Similarly, the results showed good performance of the model such that R2 were 0.87 and 0.82, respectively.