جستجوی مقالات مرتبط با کلیدواژه « remote sensing » در نشریات گروه « کشاورزی »

The relationship between intensity-duration-frequency of rainfall is a significant tool for estimating flood discharge. According to the sparsely available rain gauge stations and the development of technology, it is possible to use satellite rainfall data with different temporal and spatial resolutions. PERSIANN rainfall data with a time resolution of 1 and 6 hours were used in this research. Also, the spatial resolution of these data is 0.04 x 0.04 degrees. Rainfall data from synoptic stations around the Kan basin were also used. Three common continuous probability distributions of Gamble, Pearson type 3, and Log Pearson type 3 with return periods of 2, 5, 10, 25, 50, and 100 years were investigated to calculate and check the IDF curve. In general, the precipitation intensity obtained from Gumble's method was more than Pearson Type 3's method. Log Pearson type 3 distribution did not provide acceptable results in this research. The two interpolation methods of inverse distance weighting and empirical Bayesian kriging were used to generalize the frequency intensity curves to the entire Kan basin. The results showed little difference between these two methods, except for Pearson type 3 probability distribution.

One of the common methods to estimate the yield of crops before their harvest is the use of satellite images and remote sensing technology. One of the efficient methods extracted from remote sensing data to monitor crop yield is the use of spectral indices. This study was carried out to estimate maize yield before its harvest using Landsat 8 satellite images in a part of the lands of Shahid Beheshti agro-industry, Dezful. Eight spectral indices including NDVI, TNDVI, GNDVI, SAVI, OSAVI, NDWI, MNDWI, and NDMI were used in this study. A correlation relationship was formed between the indices and yield with 70% of the data. Then the relationships were evaluated with 30% of the data. MNDWI index had the highest correlation with the field data in December and January, respectively, with a coefficient of determination of 30% and 13%. R2 and RMSE between the observed and predicted yield values by this index for the mentioned months were 72%, 1/71 tons per hectare, and 48%, 1/54 tons per hectare, respectively. Multivariable equations produced for both dates in January and December were obtained with R2 and RMSE, 31%, 0/06 tons per hectare, and 55%, 1/19 tons per hectare, respectively. According to the results, by using the average MNDWI ، SAVI, and NDVI indices in the produced relationships, the errors caused by the spatial changes of yield at the farm level are minimized, and it is possible to estimate the yield before harvest.

The effect of drought as a complex phenomenon on the economy and environment can be destructive. Identifying the drought-prone areas is essential for planning and adopting mitigation measures. This study is focused on introducing a multi-scale method based on satellite datasets to assess drought vulnerability in areas without data. In this regard, two steps were considered and drought monitoring was carried out for the northwest part of Iran as the study area. In the first step, vulnerable areas were identified using multi-criteria kernel-based techniques that integrated 17 geo-environmental parameters extracted from ground-based and satellite datasets to develop a drought vulnerability map. The considered models were evaluated with different criteria and the most important variables were determined using sensitivity analysis. Results showed that the southern and central parts of the selected region are more prone to severe droughts. Among the considered variables, soil moisture, precipitation, air humidity, and vegetation condition were the most effective parameters. In the second step, due to the limitation of ground-based data, drought-prone areas were identified only by using the variables of precipitation, vegetation condition, and ground surface temperature extracted from satellites. For this aim, a new multiscale method was developed based on wavelet transform (WT), variational mode Decomposition (VMD), and kernel-based methods. The obtained results show the appropriate efficiency of the proposed multi-scale method in identifying areas prone to drought with different intensities.

Given that conventional ground-based methods require significant costs, time, and manpower, there is an increasing inclination towards using indirect methods such as remote sensing. Therefore, the aim of this research was to calculate dry biomass, harvest index, and grain yield of irrigated wheat using remote sensing in the Urmia Lake Basin. For this purpose, the Urmia Lake Basin was divided into six sub-basins, and the Radiation Use Efficiency (RUE) model was utilized. In this study, 1115 irrigated wheat fields were used. Monitoring results of the fields and the NDVI index indicated that irrigated wheat germination in the basin began on average from November 11th, and harvesting of this crop continued until July 21th. The values of correlation coefficient (R), Nash-Sutcliffe coefficient (NSE), normalized root mean square error (nRMSE), and coefficient of residual mass (CRM) in the calibration step were 0.90, 0.76, 15%, and 6.38%, respectively, and in the validation step, they were 0.92, 0.72, 14%, and 7.21%, respectively. Examination of the harvest index (HI) indicated that the average of this index in different sub-basins ranged from 0.40% to 0.45%, with an overall average of 0.42% for the Urmia Lake Basin. The results of biomass and grain yield assessment revealed significant differences in their values among different sub-basins. The highest biomass and grain yield were observed in the Zarrineh-Simineh sub-basin with averages of 12098 and 5261 kilograms per hectare, respectively, while the lowest values were observed in the West-Lake sub-basin with averages of 8651 and 3723 kilograms per hectare, respectively.

There are two approaches in dealing with environmental changes. The first approach is based on ignoring these changes and continuing the current situation, which will result in further environmental destruction. The second approach is based on identifying these changes from the past to the present and formulating an environmental management plan to control these changes and plan for improving the environmental situation. Wetlands are one of the most important natural habitats in the world and have many values and benefits, especially in energy circulation in the environment. In the last 20 years, the area of many wetlands has decreased due to the increase in industrial and agricultural development and the lack of water in arid and semi-arid regions (Cui et al., 2009). One of the effective tools in the field of wetland area changes is the use of remote sensing technology and satellite data. The use of satellite images, due to their wide spatial coverage, high resolution, low cost, temporal archive, free access to satellite images, and the availability of practical software and spectral indices, has become very important in estimating the area of aquatic zones. Monitoring and assessing the water level of wetlands using remote sensing to protect this natural heritage has become a focus for many countries in recent years, and this is of particular importance as the first step in identifying problems and attempting to solve them. Heshilan wetland plays a significant role in nourishing and increasing the stability of underground waters, preventing floods, and promoting agriculture in the region, as well as providing a unique habitat for native and migratory birds to easily pass winter. The aim of this research is to monitor changes in the water level of Heshilan wetland over a 22-year period using remote sensing and geographic information systems.

Landsat satellite images were downloaded from the United States Geological Survey website. After downloading the Landsat satellite images, the Heshilan wetland area was delineated, and the images were radiometrically and atmospherically corrected. To enhance the spatial resolution of the study area, the 30-meter pixel size image was converted to a 15-meter pixel size image for better spatial resolution (the 15-meter resolution provides more accurate information than approximate surface area estimates of wetlands), then we normalized the surface reflectance values. The Target Detection Wizard tool in ENVI 5.6 software was used to determine the aquatic zone area of the wetland in Murch of each year during (2000-2022), and the output file was imported into ArcMap 10.8.2 to calculate the aquatic zone area of Heshilan wetland in square kilometers on the date of image acquisition.

The results of interpreting satellite images over the years under study indicate that in 2018, the wetland had its lowest surface area in two decades, with an area of 0.34 square kilometers in that year. Additionally, the highest area of the aquatic zone of the wetland occurred in 2005, with an area of 3.83 square kilometers. The results show that over the past 22 years, the wetland has faced a decrease in water level by 0.79 square kilometers for the specific month.

The research results indicate that analyzing satellite images using remote sensing techniques can effectively show changes in wetland areas over time intervals. The results of interpreting satellite images over the years under study show a reduction in the extent of Heshilan wetland, especially in the western and southwestern regions, which can be attributed to natural phenomena such as drying up some springs due to droughts and periods of low water levels, as well as undesirable effects of human activities such as increased human access to wetlands through access roads in this area, land grabbing by villagers converting them into agricultural lands, deep wells around the wetlands, and utilizing wetland water through agricultural canals without considering proper water inflow. In these conditions, the wetland ecosystem has completely changed, and other aquatic birds no longer live in this area, with many migratory birds moving to tropical regions, depending on their habitats along the migration route. Given the unfavorable trends in Heshilan wetland developments, continuing the current conditions could jeopardize the existence and integrity of Heshilan wetland irreversibly. Ecosystem management of wetlands by developing a management plan to preserve and revitalize the structure and functions of this ecosystem with long-term stability can be a solution.

Fresh water availability and its shortage is one of the most important issues in the world today that some countries are facing. In the last two decades, Kabul province, Afghanistan, has seen a decrease in water resources under the influence of natural and human factors. The purpose of this research is to use satellite data and remote sensing techniques to investigate changes in surface and underground water resources in Kabul province. For this purpose, the satellite data and products available in the Google Earth Engine in the period 2000 to 2022 and the climate data of ground stations in the period 2006 to 2021 have been used, which include: evaporation data- Transpiration, Enhanced Vegetation Index (EVI), Global Product of Surface Water Areas (JRC), GRACE data, OLS Satellite Night Images, Sentinel 2 Satellite Image, Landsat 7 Satellite Image, Temperature, Humidity and Rainfall Data. The change process of the used data was analyzed through the Mann-Kendall test and the significance level of these data was checked. The supervised classification method was used on the two mentioned images to calculate the area of vegetation, water area, residential areas, and barren lands. The obtained results show the reduction of underground water resources (significant reduction trend of GRACE satellite data) and the reduction of the surface water area changes in Kabul province, under the influence of natural and human factors, which among these factors can reduce He pointed out the amount of rainfall, increase in temperature, increase in evaporation-transpiration, increase in the level of vegetation and physical development of Kabul city and increase in the population using water resources.

This study identified and described drought using multiple MODIS-based indicators, so the temporal-spatial characteristics of the intensity and frequency of drought in the entire country in the period from 2001 to 2021 using the standardized precipitation index in the form of one month SPI-1. Three-month SPI-3 and one-year SPI-12, based on CHIRPS precipitation data set with spatial resolution of 5 km, vegetation condition index (VCI), temperature condition index (TCI) and vegetation health index (VHI) were investigated. the distribution of rainfall in the southeastern and central regions of the country is less than 200 mm per year. Examining the ratio of drought classes based on the TCI index shows that the area ratio of the areas that are in the severe drought class in 2020 and 2021 is 36.7% and 43.2%, respectively, which has increased by about 7%. The comparison of the area of the drought class of the TCI and VCI indices also shows that the VCI index has overestimated the areas in the severe drought class by 3.7% and 5.1% in 2020 and 2021. Also, the VHI index shows that 6 provinces in the southern region of the country have experienced long-term drought between 2009 and 2021.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level (Sanchez et al., 2008). The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods (Alizade and Kamali, 2007). Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations (Chihda et al., 2010). Examples of such algorithms include SEBAL (Bastiaanssen et al., 1998 Bastiaanssen, 2000;), METRIC (Allen et al., 2007), SEBS (Su, 2002). Among the above mentioned algorithms, energy billing algorithms have been used (Bagheriharooni et al., 2013; Teixeira et al., 2009). Among the factors of superiority of the SEBAL algorithm, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models (Bastiaanssen et al., 2002).

In this research, the water quality of Sefidroud River during the years 2013-2018 using Landsat 8 satellite images as well as 10 qualitative chemical and physical parameters including Ca2+, Na+,Mg2+, (Cl-,SO4-2, HCO3-, TDS, EC, TH and pH were studied in three hydrometric stations. Drinking water quality index (WQI) was calculated and its relationship with satellite bands and band ratios (28 parameters) was analyzed using univariate and multivariate regression models. The results of the univariate regression model showed that the WQI index with band 5 and the ratio of band B4/B3 had a linear and power correlation at a significance level of 1% with coefficient of determination (R2) of 0.55 and 0.51, respectively. The implementation of the stepwise linear multivariate regression model of WQI with all the studied bands and ratios showed that the three band 5 variables and band ratios B4/B3 and B6/B5 were correlated with WQI, with an R2 of about 0.80 at a 5% significance level. After preparing the spatial changes map of WQI using multivariate linear regression model, the results indicated that the water quality in the head branches of Sefidroud, that is, the Qezaluzen and Shahroud rivers, was lower compared to the lower areas and Manjil Dam Lake, although the water quality of the Qezeluzen and Shahroud rivers was lower. Shahrood was weak, but after entering Manjil Dam, it became a good water class. However, the WQI of the water coming out of the dam had gradually increased by passing through the agricultural lands, residential and industrial areas along the river until it reached the Caspian Sea, and it had weakened water class. In general, the results of the research showed that the use of Landsat 8 satellite images and multivariable regression model has a high power for water quality monitoring.

Determining the amount of soil erosion and its spatial distribution can effectively provide appropriate management practices to reduce erosion problems. Direct measurement of soil erosion takes a lot of time and money, and results are often regional and limited. This issue has led to the development of various models for estimating soil erosion and sediment production. Since most of the watersheds in Iran lack sufficient and accurate information, models that require relatively few inputs make more sense than models with high inputs. Along with empirical and process-based models, a number of erosion estimation models are known as hybrid models. One of the hybrid models is the Thornes model, which requires relatively little input data, and there has been no research related to this model in Iran. Therefore, this study was conducted to determine spatial variation the soil erosion rate and sediment yield in a part of the Sanandaj Gheshlagh Dam watershed using the Thornes model utilizing GIS and remote sensing (RS).

Two hundred soil samples were collected using a stratified random sampling method and the parameters of soil texture, particle size distribution, organic carbon, and soil CaCO3 equivalent in the laboratory were measured by conventional methods. In order to determine the input parameters of the studied model, laboratory results, Landsat 8 satellite images during a 6-year period, and some monthly meteorological data including precipitation, precipitation days number and potential evapotranspiration were analyzed. Finally, using ArcMap, Saga GIS, and ENVI software, the required parameters, and maps of the models were prepared, and the studied models were run. Sediment delivery ratios (SDRs) are assumed to be a function of the travel time of surface runoff from catchment cells to the nearest downstream channel.

The results showed that the estimated average erosion of the Thornes model was 0.76 mm per year or 10.24 tons per hectare per year (assuming a bulk density of 1.4 gr/cm3), and according to the calculated SDR, the estimated sediment was calculated by the model as 4.34 tons per hectare per year. The results of the Thornes model showed that the sensitivity of this model to some parameters, including potential evapotranspiration and potential water storage capacity, was very high. Small changes in these parameters caused a significant difference in the results, which would reduce the efficiency of the model.

According to the field observations and the investigation of erosion and sedimentation situations in the studied watershed, as well as long-term information about sediment discharged from the hydrometric station (3.10 tons per hectare per year), it seems that the Thornes model has been a relatively reasonable estimate of erosion in many parts of the studied watershed. Although, the type of model used to estimate the sediment delivery ratio and then the sediment yield can have a significant effect on the model's efficiency. Considering that this model requires relatively few data, it may be possible to use it to predict erosion in watersheds with no information or with poor information.

Evapotranspiration govern the water and energy balance of the soil, primarily used in general circulation models and hydrological modeling of weather conditions. Consequently, predicting river flow, forecasting crop performance, water management systems, and the quality of rivers or lakes all depend on evapotranspiration levels. Therefore, accurate estimation of water bilan is essential. Improved and precise estimations of evapotranspiration enable effective irrigation planning and optimal water usage for other agricultural purposes. The goal of this research is to evaluate and validate the surface energy balance model, SEBS, in determining evaporation-transpiration in the Sohrin-Qareh Chiran Plain, located 30 kilometers northwest of Zanjan city.

In this study, the actual evapotranspiration were determined using the SEBS energy balance model and Landsat imagery in the Sohrin-Qareh Chiran Plain. A total of 17 Landsat-8 images, along with weather data from the Zanjan airport station, were examined. Additionally, with the help of the SEBS model, the evapotranspiration of a five-hectare wheat field was calculated and validated for the time period from September 22, 2020, to September 22, 2021. The required inputs for the SEBS model, including albedo, land surface temperature, emissivity, NDVI, vegetation cover index, leaf area index, vegetation height, and canopy density, were prepared in the ENVI software environment. The data on precipitation and water consumption volume were also determined using the soil water balance model.

The study found that the evapotranspiration estimated by the soil water balance model was 24115 m3, while the evapotranspiration estimated by the SEBS model was 28750 m3, with a 16.12% error. Additionally, the SEBS model was calibrated using four indices - R2, RMSE, MAE, and MBE - with values of 0.844, 1.06, 1.12, and 0.25, respectively. These results indicate that the SEBS algorithm is accurate enough to estimate ETa in the study area.

Conclusion and Suggestions:

Therefore, based on the results of this research, it can be said that the use of satellite images in estimating actual evapotranspiration (ET) is reliable compared to field measurement methods. Considering the limitations of traditional moisture balance methods (such as being point-specific, time-consuming, costly, requiring precise instruments, and almost impractical with the use of lysimeters) and the advantages of utilizing satellite images (covering a wide area of farms or orchards, being cost-effective and fast), acceptable results can be achieved in estimating evapotranspiration while accepting a low margin of error.

Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.

The different land uses in the irrigation water area of the eleven streams of Khansar city during 1969, 1995, 2014, and 2019 have been identified and their area has been determined by analysis of the aerial photos as well as the satellite images of QuickBird, and Landsat in the Google Earth Engine (GEE) environment. Then, the net and gross areas of land under irrigation water, area of non-agricultural land uses, location and area of agricultural land uses under irrigation of the streams are separated according to the type of agricultural activity (orchard or farmland) for each stream. Aerial photos of the study area dated 1969 are the basis for the assessment of agricultural conditions before the law of Fair Water Allocation. The results showed that non-agricultural and particularly urban and residential land uses have increased since 1969. In other words, land use of part of the agricultural lands has been changed to residential and urban land uses. Despite the decreasing trend of agricultural land uses in the last 50 years, these changes have not been the same between the farm and orchard land uses and the area under orchard plantation showed an increasing trend. These changes have dramatically influenced on water demand of the streams. Land use has not significantly changed from 2014 to 2019 and no noticeable change was observed in the area of the agricultural and green agricultural lands as well as the percentage of the orchard and farming lands during these years. The results of this study confirmed the significant changes in agricultural land use and consequently water consumption in the district of the eleven streams of Khansar in recent decades. This study also highlighted the high efficiency of the combined use of aerial photos, spectral satellite images with medium spatial resolution, and visible spectral satellite data with high spectral resolution, as well as using cloud system capabilities of the Google Earth Engine to study changes in agricultural land uses during last decades.

Determining the organic carbon content of rangeland vegetation is essential for monitoring rangeland condition and facilitating reclamation efforts. Satellite data provides a valuable tool for conducting extensive vegetation studies. This research aimed to estimate the organic carbon content of vegetation using field assessments and remote sensing indices in the rangelands of Lashgardar protected area, Malayer. Leveraging Landsat time series images, this study utilized Landsat 8 data from the Operational Land Imager (OLI) sensor.

Field sampling was conducted in the rangelands of Lashgardar protected area on May 28, 2016. The dominant growth form in these rangelands is herbaceous-shrub, with Asteraceae family species being the most abundant. Forty points were randomly selected as the centers of sampling plots for plant biomass. To account for GPS accuracy error, a factor of twice the pixel size was applied, resulting in the selection of 40 plots measuring 30×30 m2 for field sampling. Subplots measuring 1×1 m2 were utilized to collect composite aboveground biomass samples from the central point and the four corners of each main plot. Samples were processed in the Rangeland Science Laboratory at Malayer University following coding for laboratory procedures. The organic carbon content of vegetation was determined using the loss on ignition (LOI) method after air-drying. Vegetation indices were extracted from Landsat 8 satellite images captured by OLI sensors, including digital bands 1 to 7 with a spatial resolution of 30 meters. Various vegetation indices such as Greenness, RVI, NDVI, IPVI, DVI, WDVI, ARVI, SAVI, TSAVI, BI, OSAVI, GEMI, EVI, LAI, and GARI were derived from Landsat images.

Comparative analysis of estimated organic carbon data with measured organic carbon content revealed that only the Green Atmospherically Resistant Vegetation Index (GARI) could effectively estimate the organic carbon content of vegetation. The best model was achieved using the GARI index for organic carbon estimation, represented as OC = 5.4 + 1.38 GARI, with an explanatory coefficient (R2) of 0.13 and Root Mean Square Error (RMSE) of 0.7. These findings suggest that remote sensing indices can serve as complementary methods in vegetation studies.

The GARI index demonstrated promising results for estimating organic carbon content in vegetation within the study area and is recommended as a suitable indicator for similar areas. However, the efficacy of each index may vary depending on specific area characteristics and vegetation types. It is advisable to conduct time-series studies with larger sample sizes tailored to the unique conditions of the study area to identify the most appropriate indices.