Elmira Asadi-Fard
@modares.ac.ir
EDUCATION
Ph.D. Environmental Science and Engineering (Environmental Science )- Tarbiart Modares unversity
M.Sc. NATURAL RESOURCES ENGINEERING - DESERT REGIONS MANAGEMENT (Department of Natural Resources and Environmental Engineering)-Shiraz university
B.Sc. Natural Resources Engineering-Environmental (Department of Natural Resources and Environmental Engineering)-Shiraz university
RESEARCH, TEACHING, or OTHER INTERESTS
Environmental Science, Pollution
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Elmira Asadi-Fard, Samereh Falahatkar, Mahdi Tanha Ziyarati, and Xiaodong Zhang
Springer Science and Business Media LLC
Elmira Asadi‐Fard, Samereh Falahatkar, Mahdi Tanha Ziarati, and Xiaodong Zhang
American Geophysical Union (AGU)
AbstractGas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2 values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model.
Elmira Asadi-Fard, Samereh Falahatkar, Mahdi Tanha Ziyarati, Xiaodong Zhang, and Mariapia Faruolo
MDPI AG
The environment, the climate and human health are largely exposed to gas flaring (GF) effects, releasing significant dangerous gases into the atmosphere. In the last few decades, remote sensing technology has received great attention in gas flaring investigation. The Pars Special Economic Energy Zone (PSEEZ), located in the south of Iran, hosts many natural oil/gas processing plants and petrochemical industries, making this area one of the most air-polluted zones of Iran. The object of this research is to detect GF-related thermal anomalies in the PSEEZ by applying, for the first time, the Reed-Xiaoli Detector (RXD), distinguished as the benchmark algorithm for spectral anomaly detection. The RXD performances in this research field have been tested and verified using the shortwave infrared (SWIR) bands of OLI-Landsat 8 (L8), acquired in 2018 and 2019 on the study area. Preliminary results of this automatic unsupervised learning algorithm demonstrated an exciting potential of RXD for GF anomaly detection on a monthly scale (75% success rate), with peaks in the months of January and February 2018 (86%) and December 2019 (84%). The lowest detection was recorded in October 2019 (48%). Regarding the spatial distribution of GF anomalies, a qualitatively analysis demonstrated the RXD capability in mapping the areas affected by gas flaring, with some limitations (i.e., false positives) due to possible solar radiation contribution. Further analyses will be dedicated to recalibrate the algorithm to increase its reliability, also coupling L8 and Landsat 9, as well as exploring Sentinel 2 SWIR imagery, to overcome some of the observed RXD drawbacks.
Masoud Masoudi and Elmira Asadifard
Firenze University Press
Agricultural planning is a very complex task, since there are numerous goals, which should be achieved simultaneously, and various components and elements, which must be considered at the same time. The process of agricultural suitability evaluation for crop production requires specialized geo-environmental information and the expertise of a computer scientist to analyze and interpret the information. The main objective of this paper is to test a new model (based on Iranian ecological and FAO models) for ecological capability evaluation with geometric mean evaluation for better planning management of irrigated lands. Next, the proposed method was verified and compared with other well-known methods such as the Iranian ecological model with Boolean logic, arithmetic mean, and WLC. To test the models, we used the normalized difference vegetation index (NDVI). The test results indicated that the method revised by geometric mean evaluation (overall accuracy %=95 and Kappa coefficient =0.91) was the best among the used methods, and the arithmetic mean method (overall accuracy %=46 and Kappa coefficient =0) had the lowest accuracy. Thus, this method (Geometric mean evaluation) has high flexibility in locating agricultural lands. Overall, this study can be used as a basic method to evaluate ecological suitability for other regions with similar conditions owing to its simplicity and high precision.
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Arc gis
SPSS
Statistica
Snap
ENVI
Phast
Terrset
Beam