2024-07-14T03:18:56+03:30
http://jgit.kntu.ac.ir/browse.php?mag_id=45&slc_lang=fa&sid=1
45-849
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
Evaluation of SEBAL and METRIC algorithms to estimate evaporation over fresh and saline water bodies (case study: Urmia Lake, Dez reservoir, and Karkhe reservoir)
Ramin
Ghahreman
ghahremaniramin1995@gmail.com
Majid
Rahimzadegan
rahimzadegan@kntu.ac.ir
Evaporation over the surface of water bodies is regarded as one of the main sources of water loss. Various model based on satellite imagery have been introduced to estimate evaporation. Accurate realization of performance of each model to estimate evaporation over water bodies is one of the critical issues in the water balance and water resource management. Hence, the purpose of this study was implementation and evaluation of initial version of Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC) algorithms over Urmia Lake, Karkhe reservoir, and Dez reservoir. It was performed using 25 Moderate Resolution Imaging Spectroradiometer (MODIS) images in 2020. Regarding restriction on accessibility to the required meteorological data to run the relevant models in Iran, the consequences of using various synoptic station data, which located at the lake neighborhood was investigated. The obtained results of monthly mean evaporation showed that METRIC model over saline water has good efficiency against the measurement value with coefficient of determination (R2)=0.98 and Root Mean Square Error (RMSE) of 0.13(mm), and over the freshwater with R2=0.89 and RMSE=0.7(mm). The difference was that the bias of METRIC model on saline water is higher than freshwater. Nevertheless, due to inaccessibility to the daily solar net radiation and according to the results, initial version of SEBAL model over saline and fresh water have resulted in more bias and unacceptable result against in situ measurement. The analysis of the results showed that because of the Dez and Karkhe reservoir have same climate condition and their geographical location is close to each other, the obtained result of evaporation value in both of dam were close to each other.
Energy balance
Urmia lake
reference evapotranspiration
Evaporation
Remote sensing
2024
2
01
1
20
http://jgit.kntu.ac.ir/article-1-849-en.pdf
10.61186/jgit.11.4.1
45-893
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
Pre-analysis of GNSS water vapor tomography based on analysis of the models space resolution matrix with various combinations of GNSS satellites observations
Elaheh
Sadeghi
sadeghi.elaheh@ut.ac.ir
Masoud
Mashhadi Hossainali
hossainali@kntu.ac.ir
Abdolreza
Safari
asafari@ut.ac.ir
Water vapor has an obvious role in the hydrological cycle and plays a key role in energy transport. Therefore, monitoring and determining its changes and distribution is demanding. In voxel-based tropospheric tomography, water vapor is computed for a set of voxels, each covering a specific part of the troposphere. One of the fundamental flaws in GPS tomography is the absence or insufficiency of signals in some voxels. To evaluate the performance of tomography models, it is necessary to analysis the validity of the proposed model before implementing it. In this study, the efficacy of two elements has been investigated in tomography models: using the observed various GNSS combined strategies and changing the horizontal resolution of the tomography model had been investigated to improve the validation of tomography model. In this research, we use the resolution matrix and the spread of the resolution matrix to provide a quality measure without using the reference field as radiosonde data. The results show that all of GNSS observations can increase the quality of efficiency tomographic model but not as well as was expected. To give an instance, the design matrix rank deficiency of tomography model with 40 km horizontal resolution, has been improved just 7% on the study day, while the amount of observations 65% increased when we used all active satellites. Against, increasing the horizontal resolution caused to increase the quality of tomography models. So that, the values of spread decrease from 0.2 to 0.04, when just GPS observations were used. Therefore, increasing observations when using available techniques such as optimal horizontal resolution can be important for the tomography.
GNSS tomography
design matrix
resolution matrix
spread function
2024
2
01
21
36
http://jgit.kntu.ac.ir/article-1-893-en.pdf
10.61186/jgit.11.4.21
45-920
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
Widespread Dilute Smoke Correction in the Multispectral Images through Iteratively Applying the Regression Estimated Residuals to Spectral Bands
Reza
Qalavand
reza.reza.1371.6@gmail.com
Alireza
Safdarinezhad
safdarinezhad@tafreshu.ac.ir
Behzad
Behnabian
behnabian@tafreshu.ac.ir
Widespread wildfires and the resulting smoke are a common occurrence in various regions of the earth’s surface. Identification of the contaminated areas and crisis management requires the classification of areas affected by smoke. On the other hand, visible spectral bands in satellite images are affected by such phenomena, which blur the clarity of these images. To address this issue, a repetitive regression-based method is proposed in order to reduce the impact of dilute smoke on multispectral images. The occurrence of large residuals when the affected bands are estimated with the non-affected bands is the motivation for designing this method. The method involves iteratively applying residuals from regression models to probable smoke areas after refinement and localization, which corrects spectral observations in affected bands. This solution maintains radiometric content in clean image areas and significantly improves image clarity in smoke-contaminated areas. The results show quantitative improvement in the correlation between corrected images and smoke-free images in the most affected spectral bands, averaging 14.2 percent. This method can only be used for dilute smoke with a visible background.
Linear Regression
Dilute smokes
Multispectral Images
Scattering of light
Atmosphere
Residuals Vector
2024
2
01
37
53
http://jgit.kntu.ac.ir/article-1-920-en.pdf
10.61186/jgit.11.4.37
45-928
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
A Neural Network-Based Approach for Real-Time Measurement of the Concentration of Gaseous Pollutants in Tehran Using MODIS
Mina
Saleh
saleh.mina.sha@gmail.com
Reza
Shah-Hosseini
rshahosseini@ut.ac.ir
Zahra
Bahramian
zbahramianut@yahoo.com
Sara
Khanbani
sara.khanbani@ut.ac.ir
Nowadays, gas pollutants are considered as an important challenge in big cities. Due to the fact that gaseous pollutants have negative effects on human health and destroy the environment, there are several methods for predicting the concentration of gaseous pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2) and sulfur dioxide (SO2). The aim of the present research is to calculate the concentration of gaseous pollutants in real time using MODIS sensor data, including night and day surface temperature, aerosol light depth, vegetation index and the data from the ground stations monitoring the concentration of the pollutants using multi-layer perceptron neural network. The perceptron neural network had the best performance with 8 neurons, 4 of which in the input layer, 3 in the middle layer, and one in the output layer. 80% of the data were considered as the training data and 20% as the test data; and 15% of the training data were considered for Validation of the network. Using the aforementioned training and experimental data, the parameters of the number of periods and the learning rate were subjected to sensitivity analysis and the most suitable parameters were selected. In the next step, the random forest regression method was used to compare the results. The results showed that the multilayer perceptron neural network performed better than the random forest regression. In this research, the qualitative analysis of the pollutant concentration map and the pollutants’ relationship with the land use and the existing roads around each of the air quality control monitoring stations was done. The data of Tehran city were used as a 6-year time series from 1393 to 1399. The accuracy evaluation of the proposed method using the experimental data shows 86% accuracy for measuring carbon monoxide (CO) and nitrogen dioxide (NO2) pollutants and 92% accuracy for sulfur dioxide (SO2) one.
Air Pollution
Perceptron Neural Network
Gaseous Pollutants
GIS
MODIS
2024
2
01
55
81
http://jgit.kntu.ac.ir/article-1-928-en.pdf
10.61186/jgit.11.4.55
45-901
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
Matching improvement of satellite images using geometric relationships
Ali
Jafari
iustuser@mut.ac.ir
Elham
Pour Yaghoubi
Matching remote sensing images is a challenging issue in computer vision applications. Due to the very large dimensions, local destructions, radiometric distortions, and geometric changes in the input images, the existing matching algorithms such as Scale Invariant Feature Transform (SIFT) produce a large number of false matches. Moreover, due to the high dimensional images a big number of keypoints are extracted in large-scale satellite images. A very large number of keypoints increases the computational, memory and time complexity in the stages of feature description and matching the keypoints. In this paper, the geometric relationships between the key points extracted from the input images, are used to improve the detection process of false corresponding points and also to increase the speed of the SIFT algorithm. The proposed false correspondence removal algorithm uses the histogram of the scale difference values and the two image rotation angle. In the following, two new algorithms which are based on the hierarchical strategy are proposed to increase the speed of the SIFT algorithm. The first proposed algorithm is based on finding the optimal octaves in the scale space of the SIFT algorithm and selecting their compared keypoints. In the second method, the parameters of the affine transformation which are between the two images are calculated by performing an initial matching, and then this transformation is used to reduce the search space in the final matching stage of the keypoints. Finally, to check the performance and accuracy of each of the proposed methods, a variety of simulated and real images have been used. Moreover, for the final evaluation of the proposed algorithms, the obtained results are compared with SIFT, SR-SIFT and SIFT-GSI methods. The experimental results confirm the accuracy, stability and high speed of the proposed methods in matching satellite images.
Image Matching
Affine Transformation
Reduce Search Space
SIFT Algorithm
2024
2
01
83
101
http://jgit.kntu.ac.ir/article-1-901-en.pdf
10.61186/jgit.11.4.83
45-930
2024-07-14
10.1002
Engineering Journal of Geospatial Information Technology
jgit
2008-9635
10.61186/jgit
2024
11
4
A new fractal relationship for measuring surface roughness to be used in physical models of radar backscattering
Mohammad
Maleki
mo.maleki158@gmail.com
The sensitivity of the microwave waves to the physical and geometrical parameters of the soil has caused the radar remote sensing to have a wide range of applications in various fields. In agricultural and environmental issues, the soil parameters, including its moisture and roughness, have been among the most important issues to the experts, the first of which is related to its physical characteristic and the second one is related to geometrical characteristic. The roughness parameter plays an important role in the soil erosion. And in order to estimate the soil moisture, the roughness must also be studied. The role of each of them in the backscattering of radar waves must be analyzed as well. There are various models to estimate these two parameters from the radar images, among which the most important ones are the IEM and SPM models. As the evaluation of the accuracy of these models in estimating the surface roughness is based on the real ground data, the calculation of the ground roughness is important. In order to measure the ground roughness, there are two methods: Euclidean Geometry and Fractal Geometry. There are many studies that have demonstrated the higher accuracy of the fractal methods in estimating the surface roughness. In order to measure the surface roughness to enter the physical models or to measure the accuracy of the roughness using the inversion of the physical models, various fractal methods have been proposed, which are based on the profile length, while In this study, a new equation for measuring the roughness by fractal method is proposed, which is based on the sampling intervals. This equation has been obtained based on the simulation of different fractal surfaces in a wide range of dimensions. The accuracy of the obtained model is RMSE =0.12 and R2 =0.9988. The evaluation results of this equation for the ranges outside the simulation have shown that it is a reliable method with high accuracy for measuring the surface roughness using the fractal method.
Roughness
Fractal Geometry
Euclidean Geometry.fbm
2024
2
01
103
117
http://jgit.kntu.ac.ir/article-1-930-en.pdf
10.61186/jgit.11.4.103