Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images

Nhu, Viet-Ha; Mohammadi, Ayub; Shahabi, Himan; Shirzadi, Ataollah; Al‐Ansari, Nadhir; Ahmad, Baharin Bin; Chen, Wei; Khodadadi, Masood; Ahmadi, Mehdi; Khosravi, Khabat; Jaafari, Abolfazl; Nguyen, Hoang

doi:10.3390/ijerph17124210

Cited by 43 publications

(16 citation statements)

References 62 publications

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“…Landslide Inventory. Accurate knowledge of problem influencing variables and corresponding data is required for dealing with practical engineering problems [52][53][54][55]. Identification and collection of landslide events and their past and current locations using time series Google Earth and satellite images [56] in conjunction with field surveys and reports are required to develop a landslide inventory map [14].…”

Section: Data Usedmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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Section: Data Usedmentioning

confidence: 99%

“…Geomorphology of the area describes the relief features of the Earth and can significantly control the landslide occurrences [54,73,74]. is factor map was extracted from the GSI report, checked from the DEM and Google Earth images, and classified into twelve geomorphological units (Figure 3).…”

Section: Geomorphologymentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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“…This lake has been experiencing a rapid decrease in water level and becoming more saline affecting 204,000 ha of agricultural land. It will then considerably affect agricultural production, and food security, which is one of the biggest long-term challenges in Iran and the world (Nhu et al 2020). In this condition, the farmers use the wastewater as irrigation water due to scarcity of freshwater and easy access .…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Phragmites australis under different times of wastewater irrigation in the soil–plant–water system

Khalilzadeh

Пирзад

Sepehr

et al. 2021

Int. J. Environ. Sci. Technol.

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This study investigates the impact of wastewater irrigation on soil-plant-water system at the vegetative, flowering, and reproductive stages of Phragmites australis for two and four days (in each stage) in the coastal saline area of Urmia Lake. The concentrations of potentially toxic elements were detected in irrigated water, soil, and plant (aboveground and belowground) tissues, and transfer factor and bioaccumulation factor were calculated. The shoot biomass was significantly increased with increasing wastewater exposure, up to four days after the flowering stage. All potentially toxic elements concentrations in belowground (roots and rhizomes) tissues were higher than aboveground (leaves and stems) tissues. Fe showed the maximum concentrations in all organs at different growth stages due to the higher level contents in soil and water. Fe was found to be the least mobile at the reproductive stage and presented in the following order: roots > rhizomes > leaves ≥ stems. By contrast, Zn and Cd metals which are quickly transported in plants exhibited the same trend. Pb was accumulated at the flowering stage as follows: rhizomes > roots > stems > leaves. The toxic threshold was exceeded by Zn, Fe, Cu, and Cd in roots; Fe in rhizomes, and leaves; Cd in rhizomes; and Ni in roots. There was more efficiency in the removal of chemical oxygen demand (61.09%), biochemical oxygen demand (73%), total suspended solids (50%), and ammoniacal nitrogen (59.5%) at the reproductive stage. Therefore, P. australis could be an efficient and valuable tool for the phytoextraction and phytostabilization of metals if harvested at the proper time of growth in high potentially toxic elements polluted coastal ecosystems.

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“…In order to record all changes, selecting the proper temporal baseline of RS data is very important [10]. The Landsat satellite imagery (medium spatial resolution RS data) has widely been utilized to quantitate urbanization across the world [2,11].…”

Section: Introductionmentioning

confidence: 99%

“…Built-up areas are very important for many studies, including those considering buildings' age, seismic hazard assessment, and prediction, so as to enable the optimal updating of built-up areas. Maximum likelihood, as one of the best methods for classification [11,13], was employed for extracting land cover using four cloud-free Landsat data. Accordingly, building inventory and urban sprawl information are important factors for damage estimation [7,14,15].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

Mohammadi

Karimzadeh

Kamran

et al. 2020

Sensors

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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.

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Monitoring and Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental Consequences Using Multitemporal Landsat 7 ETM+ Images

Cited by 43 publications

References 62 publications

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Efficiency of Phragmites australis under different times of wastewater irrigation in the soil–plant–water system

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

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