This page has only limited features, please log in for full access.
Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs’ performance, such as 90-meters’ TanDEM-X and 30-meters’ SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).
Ayub Mohammadi; Sadra Karimzadeh; Shazad Jamal Jalal; Khalil Valizadeh Kamran; Himan Shahabi; Saeid Homayouni; Nadhir Al-Ansari. A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models. Sensors 2020, 20, 7214 .
AMA StyleAyub Mohammadi, Sadra Karimzadeh, Shazad Jamal Jalal, Khalil Valizadeh Kamran, Himan Shahabi, Saeid Homayouni, Nadhir Al-Ansari. A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models. Sensors. 2020; 20 (24):7214.
Chicago/Turabian StyleAyub Mohammadi; Sadra Karimzadeh; Shazad Jamal Jalal; Khalil Valizadeh Kamran; Himan Shahabi; Saeid Homayouni; Nadhir Al-Ansari. 2020. "A Multi-Sensor Comparative Analysis on the Suitability of Generated DEM from Sentinel-1 SAR Interferometry Using Statistical and Hydrological Models." Sensors 20, no. 24: 7214.
Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively.
Ayub Mohammadi; Sadra Karimzadeh; Khalil Valizadeh Kamran; Masashi Matsuoka. Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran. Sensors 2020, 20, 7010 .
AMA StyleAyub Mohammadi, Sadra Karimzadeh, Khalil Valizadeh Kamran, Masashi Matsuoka. Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran. Sensors. 2020; 20 (24):7010.
Chicago/Turabian StyleAyub Mohammadi; Sadra Karimzadeh; Khalil Valizadeh Kamran; Masashi Matsuoka. 2020. "Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran." Sensors 20, no. 24: 7010.
Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.
Ayub Mohammadi; Khalil Valizadeh Kamran; Sadra Karimzadeh; Himan Shahabi; Nadhir Al-Ansari. Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models. Complexity 2020, 2020, 1 -21.
AMA StyleAyub Mohammadi, Khalil Valizadeh Kamran, Sadra Karimzadeh, Himan Shahabi, Nadhir Al-Ansari. Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models. Complexity. 2020; 2020 ():1-21.
Chicago/Turabian StyleAyub Mohammadi; Khalil Valizadeh Kamran; Sadra Karimzadeh; Himan Shahabi; Nadhir Al-Ansari. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models." Complexity 2020, no. : 1-21.
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.
Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; Marten Geertsema; Victoria R. R. Kress; Sadra Karimzadeh; Khalil valizadeh Kamran; Wei Chen; Hoang Nguyen. Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms. Forests 2020, 11, 830 .
AMA StyleViet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, Marten Geertsema, Victoria R. R. Kress, Sadra Karimzadeh, Khalil valizadeh Kamran, Wei Chen, Hoang Nguyen. Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms. Forests. 2020; 11 (8):830.
Chicago/Turabian StyleViet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; Marten Geertsema; Victoria R. R. Kress; Sadra Karimzadeh; Khalil valizadeh Kamran; Wei Chen; Hoang Nguyen. 2020. "Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms." Forests 11, no. 8: 830.
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.
Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; John J. Clague; Abolfazl Jaafari; Wei Chen; Hoang Nguyen. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health 2020, 17, 4933 .
AMA StyleViet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, John J. Clague, Abolfazl Jaafari, Wei Chen, Hoang Nguyen. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health. 2020; 17 (14):4933.
Chicago/Turabian StyleViet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; John J. Clague; Abolfazl Jaafari; Wei Chen; Hoang Nguyen. 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment." International Journal of Environmental Research and Public Health 17, no. 14: 4933.
The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, multispectral Landsat 7 ETM+ images for the years 2000, 2010, and 2017 were acquired. In addition, precipitation and temperature data for 31 years between 1986 and 2017 were collected for further analysis. Results indicate that the increased temperature (by 19%), decreased rainfall of about 62%, and excessive damming in the Urmia Basin along with mismanagement of water resources are the key factors in the declining water level of Lake Urmia. Furthermore, the current research predicts the potential environmental crisis as the result of the lake shrinking and suggests a few possible alternatives. The insights provided by this study can be beneficial for environmentalists and related organizations working on this and similar topics.
Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Ataollah Shirzadi; Nadhir Al-Ansari; Baharin Bin Ahmad; Wei Chen; masood Khodadadi; Mehdi Ahmadi; Khabat Khosravi; Abolfazl Jaafari; Hoang Nguyen. Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images. International Journal of Environmental Research and Public Health 2020, 17, 4210 .
AMA StyleViet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Ataollah Shirzadi, Nadhir Al-Ansari, Baharin Bin Ahmad, Wei Chen, masood Khodadadi, Mehdi Ahmadi, Khabat Khosravi, Abolfazl Jaafari, Hoang Nguyen. Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images. International Journal of Environmental Research and Public Health. 2020; 17 (12):4210.
Chicago/Turabian StyleViet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Ataollah Shirzadi; Nadhir Al-Ansari; Baharin Bin Ahmad; Wei Chen; masood Khodadadi; Mehdi Ahmadi; Khabat Khosravi; Abolfazl Jaafari; Hoang Nguyen. 2020. "Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images." International Journal of Environmental Research and Public Health 17, no. 12: 4210.
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing 2020, 12, 266 .
AMA StyleHiman Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad, Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing. 2020; 12 (2):266.
Chicago/Turabian StyleHiman Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier." Remote Sensing 12, no. 2: 266.
Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.
Dieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Mohsen Alizadeh; Wei Chen; Ayub Mohammadi; Baharin Bin Ahmad; Mahdi Panahi; Haoyuan Hong; Yingying Tian. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia. Remote Sensing 2018, 10, 1527 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Mohsen Alizadeh, Wei Chen, Ayub Mohammadi, Baharin Bin Ahmad, Mahdi Panahi, Haoyuan Hong, Yingying Tian. Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia. Remote Sensing. 2018; 10 (10):1527.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ataollah Shirzadi; Kamran Chapi; Mohsen Alizadeh; Wei Chen; Ayub Mohammadi; Baharin Bin Ahmad; Mahdi Panahi; Haoyuan Hong; Yingying Tian. 2018. "Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia." Remote Sensing 10, no. 10: 1527.
Himan Shahabi; Mamand Salari; Baharin Bin Ahmad; Ayub Mohammadi. Soil Erosion Hazard Mapping in Central Zab Basin Using Epm Model in GIS Environment. International Journal of Geography and Geology 2016, 5, 224 -235.
AMA StyleHiman Shahabi, Mamand Salari, Baharin Bin Ahmad, Ayub Mohammadi. Soil Erosion Hazard Mapping in Central Zab Basin Using Epm Model in GIS Environment. International Journal of Geography and Geology. 2016; 5 (11):224-235.
Chicago/Turabian StyleHiman Shahabi; Mamand Salari; Baharin Bin Ahmad; Ayub Mohammadi. 2016. "Soil Erosion Hazard Mapping in Central Zab Basin Using Epm Model in GIS Environment." International Journal of Geography and Geology 5, no. 11: 224-235.