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| 2. |
- Ahlmér, Anna Klara, et al.
(författare)
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Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure
- 2018
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Ingår i: Environmental Earth Sciences. - : Springer. - 1866-6280 .- 1866-6299. ; 77:14
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Tidskriftsartikel (refereegranskat)abstract
- The expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods by a case study of two areas in Sweden, Vastra Gotaland and Varmland, which was affected by severe flooding in August 2014. Soil moisture data are derived from remote-sensing techniques, with a focus on the soil moisture-specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are analyzed and the result shows that larger slopes and drainage density, in general, mean a higher risk of flooding. The precipitation is the same; however, it can be concluded that more precipitation in most cases gives higher soil moisture values. The lack, or the dimensioning, of road drainage structures seems to have a large impact on the flood risk as more sediment and water can be accumulated at the road-stream intersection. The results show that the method implementing soil moisture satellite data is promising for improving the reliability of flooding.
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| 3. |
- Aliabad, Fahime Arabi, et al.
(författare)
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Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2
- 2022
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13
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Tidskriftsartikel (refereegranskat)abstract
- One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.
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| 4. |
- Aliabad, Fahime Arabi, et al.
(författare)
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Use of Landsat 8 and UAV Images to Assess Changes in Temperature and Evapotranspiration by Economic Trees following Foliar Spraying with Light-Reflecting Compounds
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:23
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Tidskriftsartikel (refereegranskat)abstract
- Pistachio is an important economic crop in arid and semi-arid regions of Iran. A major problem leading to a reduction in crop quality and reduced marketability is extreme air temperature in summer, which causes sunburn of pistachio leaves and fruit. A solution proposed to deal with the negative effects of high temperatures and increase water consumption efficiency in pistachio orchards is use of light-reflecting compounds. This study investigated the effect of foliar application of gypsum, sulfur, and NAX-95 (calcium-based suspension coating) to trees in a pistachio orchard (150 ha) in central Iran. The effect of these foliar products is assessed at plot scale, using control plots sprayed with calcium sulfate, based on temperature and evapotranspiration changes analyzed through remote sensing. Landsat 8 sensor images and RGB images collected by UAVs (spatial resolution of 30 m and 20 cm, respectively), on the same dates, before and after foliar spray application, were merged using the PCA method and bilinear interpolation re-sampling. Land surface temperature (LST) was then estimated using the split-window algorithm, and daily evapotranspiration using the surface energy balance algorithm for land (SEBAL) algorithm. A land use map was prepared and used to isolate pistachio trees in the field and assess weed cover, whose effect was not accounted. The results showed that temperature remained constant in the control plot between the spraying dates, indicating no environmental changes. In the main plots, gypsum had the greatest effect in reducing the temperature of pistachio trees. The plots with foliar spraying with gypsum displayed a mean tree temperature (47–48 °C) decrease of 3.3 °C in comparison with the control plots (>49 °C), leading to an average decline in evapotranspiration of 0.18 mm/day. NAX-95 and sulfur reduced tree temperature by on average 1.3 °C and 0.6 °C, respectively. Thus, gypsum is the most suitable foliar-spraying compound to lower the temperature of pistachio trees, reduce the water requirement, and increase crop productivity.
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| 5. |
- Andersson, Erik, et al.
(författare)
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Ambio fit for the 2020s
- 2022
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Ingår i: Ambio. - : Springer Nature. - 0044-7447 .- 1654-7209. ; 51:5, s. 1091-1093
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Tidskriftsartikel (refereegranskat)
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| 6. |
- Arabameri, Alireza, et al.
(författare)
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A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran
- 2019
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 660, s. 443-458
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Tidskriftsartikel (refereegranskat)abstract
- In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.
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| 7. |
- Ardakani, Amir Hossien Hatefi, et al.
(författare)
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Selecting potential locations for groundwater recharge by means of remote sensing and GIS and weighting based on Boolean logic and analytic hierarchy process
- 2022
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Ingår i: Environmental Earth Sciences. - : Springer Nature. - 1866-6280 .- 1866-6299. ; 81:1
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Tidskriftsartikel (refereegranskat)abstract
- Growing demand for water, as a consequence of population growth, farmland irrigation, and industrial expansion, results in groundwater resources exploitation. This, in combination with droughts induced by climate change, has caused a sharp drop in groundwater levels throughout arid and semiarid countries. In Iran, all these factors are resulting in alarming water scarcity. Appropriate management and control of existing water resources can overcome water shortages, with healthy and sustainable management of groundwater as one of the most efficient tools. Artificial recharge of aquifers can be used to replenish water supplies and restore the water resources in Iran and other semiarid and arid countries, but selection of the right location for runoff collection is essential to achieve success. Precipitation, slope, geology, lineament density, drainage density, aquifer water quality, groundwater level, vegetation, and land use were selected in this study as key factors in locating suitable sites for artificial recharge. The weight of each, in terms of importance and impact on aquifer recharge, was determined using remote sensing techniques to prepare layers and analytic hierarchy process (AHP) and Boolean logic to identify the optimal weight for each factor. Geographic information system (GIS) was used for modeling, applying the weight of each criterion, and producing a final map. The results showed better performance of AHP than Boolean logic. For artificial recharge, 9.9% of the total study area (Mahdishahr in northern Iran) was found to be in a very good position and 22.6% in a good position. On filtering the privacy layer of fountains and aqueducts, the very good and good area declined to 8.4% and 14.7% of the total area, respectively, and mainly comprised alluvial valleys and coarse alluvial sediments with low slope and drainage density.
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| 8. |
- Arian, Fatemeh, et al.
(författare)
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Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms
- 2022
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Ingår i: Journal of digital imaging. - : Springer Nature. - 0897-1889 .- 1618-727X. ; 35:6, s. 1708-1718
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Tidskriftsartikel (refereegranskat)abstract
- The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)–penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53–0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64–0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50–0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.
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| 9. |
- Behboudian, Massoud, et al.
(författare)
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Comparison of three group decision-making frameworks for evaluating resilience time series of water resources systems under uncertainty
- 2024
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Ingår i: Ecological Indicators. - : Elsevier BV. - 1470-160X .- 1872-7034. ; 158
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Tidskriftsartikel (refereegranskat)abstract
- This study compared three uncertainty-based decision-making frameworks (considering/not considering the hierarchical structure of stakeholders) using resilience-based indices for evaluating different water resources management (WRM) scenarios under the impacts of climate change. The first step involved identifying significant stakeholders in the study area and establishing their relative weights. In the next step, stakeholders were asked to evaluate the management scenarios in the three different decision-making frameworks based on their decision criteria (nine resilience-based indices, implementation cost, and employment). Different types of weights (explicit and interval) were assigned to each stakeholder and their decision criteria, to account for the uncertainty associated with estimating their respective weights. This methodology was applied to the case of the Zarrinehrud River basin in northwest Iran. The best management scenario identified (MSC1346) was able increase lake elevation by 2.6 m (from 1271.3 m to 1273.9 m), improve the resilience of the system by 25 %, and enhance provisioning ecosystem services such as water and food supply and regulating services such as air quality. Comparing the results of the three decision-making frameworks revealed that the two which considered the hierarchical structure of stakeholders were more effective in determining the best scenario. The best scenario selected in the framework that ignored the hierarchical structure of stakeholders (MSC13567) had USD 202 million higher overall implementation and construction costs and gave a negligible difference in resilience value (0.04 difference) compared with scenario MSC1346.
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| 10. |
- Bemmoussat, A., et al.
(författare)
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Contribution of Satellite-Based Precipitation in Hydrological Rainfall–Runoff Modeling : Case Study of the Hammam Boughrara Region in Algeria
- 2021
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Ingår i: Earth Systems and Environment. - : Springer Nature. - 2509-9426 .- 2509-9434. ; 5:4, s. 873-881
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Tidskriftsartikel (refereegranskat)abstract
- Hydrological models are viewed as powerful tools that have a major importance for managing water resources and predicting flows. It should be specified that the meteorological parameter rainfall is the main input in these models. In the current study, data from only one rainfall station are available over the analysis domain, which cannot represent the entire Hammam Boughrara watershed of Algeria. The precipitation data remotely detected by the tropical rainfall measuring mission (TRMM) provide good spatial coverage in the watershed and can be used to fill in the missing data. The use of raw TRMM data gives poor results from the simulated flow rates with a Nash–Sutcliffe efficiency NSE equal to 0.34 for the validation period that ranges from year 2000 to 2005; this is mainly due to uncertainties in the TRMM data. For this reason, it was deemed necessary to carry out a performance test of the model. The results obtained give an unsatisfactory percent bias (PBIAS) of − 46.24%, which suggests the need to perform a correction to decrease the PBIAS of satellite precipitation. For this, two methods were used: the linear regression method and the multiplicative method. These two techniques can only be applied if there is at least one rainfall measurement station available in the watershed. The obtained results are very satisfactory since the PBIAS reaches − 0.62% for the linear regression method and − 11.58% for the multiplicative method. In addition, the use of corrected TRMMs gives also very good results with a Nash–Sutcliffe efficiency that ranges from 0.74 to 0.88 for both validation and calibration periods. Overall, the current study is supportive to estimate the satellite-based rainfall, one of the very sensitive to measure the meteorological parameter, in northwestern Algeria.
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