Elham Goumehei - Academia.edu (original) (raw)
Papers by Elham Goumehei
2016 International Electronics Symposium (IES)
Water is one of the most important earth resources which is essential to human health, society an... more Water is one of the most important earth resources which is essential to human health, society and environment. Studies on water extraction and changes have been subjects of academic studies for many years. Remote sensing as an efficient and reliable tool has been used in recent years and Landsat satellite imagery were one of the most common data due to their advantages in spatial resolution and cost. Improvement of new Landsat 8, the Operational Land Imager (OLI) data attracted more attentions recently. This study uses the Landsat 8 OLI imagery data source for water information extraction based on the Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI) and Automated Water Extraction Index (AWEI) to compare the effect of using The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) in mountainous area. The study area is Kermanshah, in west of Iran, a mountainous area which has difficulties for water extraction due to shadows and dark objects. Due to small area of water bodies in study area user's accuracy were used for evaluation of results. User accuracy for water class gives results of 23.68%, 24.34% and 22.57% for NDWI, MNDWI and AWEI, respectively. In other words, around 77% of pixels which classified as water are not water and are misclassified pixels. Applying DEM data improves results to 27.44%, 29.1% and 27.22% for NDWI, MNDWI and AWEI, respectively which shows slight increase of 3.76%, 4.88% and 4.65%.
Water Resources Research, 2019
Detection of surface water from satellite images is important for water management purposes like ... more Detection of surface water from satellite images is important for water management purposes like for mapping flood extents, inundation dynamics, and water resources distributions. In this research, we introduce a supervised contextual classification model to detect surface water bodies from polarimetric Synthetic Aperture Radar (SAR) data. A complex Wishart Markov Random Field (WMRF) combines Markov Random Fields with the complex Wishart distribution. It is applied on Single Look Complex Sentinel 1 data. Using Markov Random Fields, we utilize the geometry of surface water to remove speckle from SAR images. Results were compared with the Wishart Maximum Likelihood Classification (WMLC), the Gaussian Maximum Likelihood Classification, and a median filter followed by thresholding. Experiments demonstrate that the statistical representation of data using the Wishart distribution improves the F-score to 0.95 for WMRF, while it is 0.67, 0.88, and 0.91 for Gaussian Maximum Likelihood Classification, WMLC, and thresholding, respectively. The main improvement in the precision increases from 0.80 and 0.86 for WMLC and thresholding to 0.96 for WMRF. The WMRF model accurately distinguishes classes that have a similar backscatter, like water and bare soil. Hence, the high accuracy of the proposed WMRF model is a result of its robustness for water detection from Single Look Complex data. We conclude that the proposed model is a great improvement on existing methods for the detection of calm surface water bodies. Remote sensing technology provides advanced means for detecting, characterizing, and monitoring water bodies. It overcomes shortcomings of traditional ground-based surveys, such as being expensive, timeconsuming, and influenced by other unknown factors in the field (Wang et al., 2011). Synthetic Aperture Radar (SAR) data have many advantages over optical images as they are independent of cloud cover; the sensors are able to operate day and night and are not subject to sun glint (Kutser et al., 2009). Applicability of SAR data for surface water detection has been demonstrated in the past (Henry et al., 2006; Hoque et al., 2011; Mertes, 2002; Tholey et al., 1997). Thresholding methods have been extensively used based upon the assumption of a strong contrast between the low backscatter of water and the higher backscatter of main land cover classes in the intensity images (
International Journal of Environment and Geoinformatics, 2016
Groundwater is considered as an important source of water supply in our world. Its contamination ... more Groundwater is considered as an important source of water supply in our world. Its contamination is of particular concern as it is a vital source of water for irrigation, drinking and industrial activities. To control and manage groundwater contamination DRASTIC model is a popular approach. This study applied an integrated DRASTIC model using Geographic Information Science (GIS) tool to evaluate groundwater vulnerability of Kermanshah plain, Iran considering water table fluctuation. High fluctuation of water table depth due to wet and dry season in arid and semi-arid areas is notable. The study area is affected by this problem, thus this research investigated the effect of minimum depth water during one year respect to average water depth which is common for this model. Results represent considerable differences for two types of produced maps; map using mean of water table for 5 year and map of minimum water table of one year. Vulnerability maps of mean data classified 40% of the study area as no risk of pollution while this is around 25% for vulnerability maps of minimum depth. In spite, minimum depth vulnerability maps classified around 12% of the study area as moderate risk which is 6% greater than mean depth vulnerability maps. In case of accuracy, results show more correlation between Nitrate data (NO 3 −) and vulnerability maps of minimum water table.
This document describes work undertaken as part of a programme of study at the Faculty of Geo-inf... more This document describes work undertaken as part of a programme of study at the Faculty of Geo-information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the University.
2016 International Electronics Symposium (IES)
Water is one of the most important earth resources which is essential to human health, society an... more Water is one of the most important earth resources which is essential to human health, society and environment. Studies on water extraction and changes have been subjects of academic studies for many years. Remote sensing as an efficient and reliable tool has been used in recent years and Landsat satellite imagery were one of the most common data due to their advantages in spatial resolution and cost. Improvement of new Landsat 8, the Operational Land Imager (OLI) data attracted more attentions recently. This study uses the Landsat 8 OLI imagery data source for water information extraction based on the Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI) and Automated Water Extraction Index (AWEI) to compare the effect of using The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) in mountainous area. The study area is Kermanshah, in west of Iran, a mountainous area which has difficulties for water extraction due to shadows and dark objects. Due to small area of water bodies in study area user's accuracy were used for evaluation of results. User accuracy for water class gives results of 23.68%, 24.34% and 22.57% for NDWI, MNDWI and AWEI, respectively. In other words, around 77% of pixels which classified as water are not water and are misclassified pixels. Applying DEM data improves results to 27.44%, 29.1% and 27.22% for NDWI, MNDWI and AWEI, respectively which shows slight increase of 3.76%, 4.88% and 4.65%.
Water Resources Research, 2019
Detection of surface water from satellite images is important for water management purposes like ... more Detection of surface water from satellite images is important for water management purposes like for mapping flood extents, inundation dynamics, and water resources distributions. In this research, we introduce a supervised contextual classification model to detect surface water bodies from polarimetric Synthetic Aperture Radar (SAR) data. A complex Wishart Markov Random Field (WMRF) combines Markov Random Fields with the complex Wishart distribution. It is applied on Single Look Complex Sentinel 1 data. Using Markov Random Fields, we utilize the geometry of surface water to remove speckle from SAR images. Results were compared with the Wishart Maximum Likelihood Classification (WMLC), the Gaussian Maximum Likelihood Classification, and a median filter followed by thresholding. Experiments demonstrate that the statistical representation of data using the Wishart distribution improves the F-score to 0.95 for WMRF, while it is 0.67, 0.88, and 0.91 for Gaussian Maximum Likelihood Classification, WMLC, and thresholding, respectively. The main improvement in the precision increases from 0.80 and 0.86 for WMLC and thresholding to 0.96 for WMRF. The WMRF model accurately distinguishes classes that have a similar backscatter, like water and bare soil. Hence, the high accuracy of the proposed WMRF model is a result of its robustness for water detection from Single Look Complex data. We conclude that the proposed model is a great improvement on existing methods for the detection of calm surface water bodies. Remote sensing technology provides advanced means for detecting, characterizing, and monitoring water bodies. It overcomes shortcomings of traditional ground-based surveys, such as being expensive, timeconsuming, and influenced by other unknown factors in the field (Wang et al., 2011). Synthetic Aperture Radar (SAR) data have many advantages over optical images as they are independent of cloud cover; the sensors are able to operate day and night and are not subject to sun glint (Kutser et al., 2009). Applicability of SAR data for surface water detection has been demonstrated in the past (Henry et al., 2006; Hoque et al., 2011; Mertes, 2002; Tholey et al., 1997). Thresholding methods have been extensively used based upon the assumption of a strong contrast between the low backscatter of water and the higher backscatter of main land cover classes in the intensity images (
International Journal of Environment and Geoinformatics, 2016
Groundwater is considered as an important source of water supply in our world. Its contamination ... more Groundwater is considered as an important source of water supply in our world. Its contamination is of particular concern as it is a vital source of water for irrigation, drinking and industrial activities. To control and manage groundwater contamination DRASTIC model is a popular approach. This study applied an integrated DRASTIC model using Geographic Information Science (GIS) tool to evaluate groundwater vulnerability of Kermanshah plain, Iran considering water table fluctuation. High fluctuation of water table depth due to wet and dry season in arid and semi-arid areas is notable. The study area is affected by this problem, thus this research investigated the effect of minimum depth water during one year respect to average water depth which is common for this model. Results represent considerable differences for two types of produced maps; map using mean of water table for 5 year and map of minimum water table of one year. Vulnerability maps of mean data classified 40% of the study area as no risk of pollution while this is around 25% for vulnerability maps of minimum depth. In spite, minimum depth vulnerability maps classified around 12% of the study area as moderate risk which is 6% greater than mean depth vulnerability maps. In case of accuracy, results show more correlation between Nitrate data (NO 3 −) and vulnerability maps of minimum water table.
This document describes work undertaken as part of a programme of study at the Faculty of Geo-inf... more This document describes work undertaken as part of a programme of study at the Faculty of Geo-information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the University.