Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.     

Validity and consistency assessment of accident analysis methods in the petroleum industry

Background. Accident analysis is the main aspect of accident investigation. It includes the method of connecting different causes in a procedural way. Therefore, it is important to use valid and reliable methods for the investigation of different causal factors of accidents, especially the noteworthy ones. Objective. This study aimed to prominently assess the accuracy (sensitivity index [SI]) and consistency of the six most commonly used accident analysis methods in the petroleum industry. Methods. In order to evaluate the methods of accident analysis, two real case studies (process safety and personal accident) from the petroleum industry were analyzed by 10 assessors. The accuracy and consistency of these methods were then evaluated. The assessors were trained in the workshop of accident analysis methods. Results. The systematic cause analysis technique and bowtie methods gained the greatest SI scores for both personal and process safety accidents, respectively. The best average results of the consistency in a single method (based on 10 independent assessors) were in the region of 70%. Conclusion. This study confirmed that the application of methods with pre-defined causes and a logic tree could enhance the sensitivity and consistency of accident analysis.     

Understanding the pro-environmental behavior among green poultry farmers: Application of behavioral theories

Environment, Development and Sustainability - Despite widespread economic growth and increasing quality of life in the last century, there are concerns that human activities have had a significant...     

Evaluation of spatial and temporal variation in Karun River water quality during five decades (1968–2014)

In the present study, the multivariate statistical technique cluster analysis (CA) is used to evaluate the spatial and temporal variations in the water quality data for the Karun River, which was gathered during 46 years of monitoring (from 1968 to 2014). The data recorded electrical conductivity (EC) in the river at six water monitoring stations along its course. The mean of EC was 972.05 ± 365.466 micromhos per centimeter (μmhos/cm) at the most upstream station and showed an increase to 1458.41 ± 675.048 μmhos/cm at the most downstream of the stations. By using hierarchical CA, the six sampling stations were grouped into three clusters of similar characteristics, which may be a result of different land uses in proximity to the stations. Furthermore, two‐way analysis of variance showed that EC had a significant correlation (p < 0.001) with the season, and the mean of the pollution depended on the level of the cluster and the season (spring, summer, autumn, and winter).     

Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Level

Groundwater and water resources management plays a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying management tools which can reveal the critical and hot conditions seems necessary due to some limitations such as labor and funding. In this study, spatial and temporal analysis of monthly groundwater level fluctuations of 39 piezometric wells monitored during 12 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial and temporal structure of groundwater level fluctuation. Results showed that a strong spatial and temporal structure existed for groundwater level fluctuations due to very low nugget effects. Spatial analysis showed a strong structure of groundwater level drop across the study area and temporal analysis showed that groundwater level fluctuations have temporal structure. On average, the range of variograms for spatial and temporal analysis was about 9.7 km and 7.2 months, respectively. Ordinary and universal kriging methods with cross-validation were applied to assess the accuracy of the chosen variograms in estimation of the groundwater level drop and groundwater level fluctuations for spatial and temporal scales, respectively. Results of ordinary and universal krigings revealed that groundwater level drop and groundwater level fluctuations were underestimated by 3% and 6% for spatial and temporal analysis, respectively, which are very low and acceptable errors and support the unbiasedness hypothesis of kriging. Although, our results demonstrated that spatial structure was a little bit stronger than temporal structure, however, estimation of groundwater level drop and groundwater level fluctuations could be performed with low uncertainty in both space and time scales. Moreover, the results showed that kriging is a beneficial and capable tool for detecting those critical regions where need more attentions for sustainable use of groundwater. Regions in which were detected as critical areas need to be much more managed for using the current water resources efficiently. Conducting water harvesting systems especially in critical and hot areas in order to recharge the groundwater, and altering the current cropping pattern to another one that need less water requirement and applying modern irrigation techniques are highly recommended; otherwise, it is most likely that in a few years no more crop would be cultivated.     

Relationships between habitat quality and ecological properties across Ziarat Basin in northern Iran

Environment, Development and Sustainability - This study aimed to examine the relationships between habitat quality and ecological properties including land use, elevation, slope, and soil...     

The effect of air pollution on drivers’ safety performance

Environmental Science and Pollution Research - Numerous studies have determined the adverse effects of air pollution on the health, welfare, and comfort of humans. More recently, the effects of air...     

Groundwater Vulnerability Assessment Using Fuzzy Logic: A Case Study in the Zayandehrood Aquifers, Iran

Groundwater is an important source of water, especially in arid and semi-arid regions where surface water is scarce. Groundwater pollution in these regions is consequently a major concern, especially as pollution control and removal in these resources are not only expensive but at times impossible. It is, therefore, essential to prevent their contamination in the first place by properly identifying vulnerable zones. One method most commonly used for evaluating groundwater pollution is the DRASTIC method, in which the Boolean logic is used to rank and classify the parameters involved. Problems arise, however, in the application of the Boolean logic. In this paper, the fuzzy logic has been used to avoid the problems. For this purpose, three critical cases of minimum, maximum, and mean values have been considered for the net recharge parameter. The process has been performed on the Zayandehrood river basin aquifers. The fuzzy-DRASTIC vulnerability map thus obtained indicates that the western areas of the basin generally have the maximum pollution potential followed by the areas located in the east. The central parts of the study area are found to have a low pollution potential. Finally, two sensitivity analyses are performed to show the significance of each value of the net recharge parameter in the calculation of vulnerability index.     

Acid-treated Fe-doped TiO2 as a high performance photocatalyst used for degradation of phenol under visible light irradiation

The photocatalytic activity of Fe-doped TiO2 nanoparticles is significantly increased by an acid-treatment process. The photocatalyst nanoparticles were prepared using sol–gel method with 0.5 mol% ratio of Fe:Ti in acidic pH of 3. The nanoparticles were structurally characterized by means of X-ray diffraction(XRD), high-resolution transmission electron microscope(HRTEM), energy-dispersive X-ray spectroscopy(EDX), X-ray photoelectron spectroscopy(XPS) and diffuse reflectance spectroscopy(DRS). It was observed that the photocatalytic activity suffered from an iron oxide contaminating layer deposited on the surface of the nanoparticles. This contamination layer was removed using an HCl acidtreatment process. The photocatalytic activity using 500 mg/L of Fe0.5-TiO2 in a 10 mg/L of phenol solution increased significantly from 33% to 57%(about 73% increase in the performance), within 90 min of reaction time under visible light irradiation. This significant improvement was achieved by removing the iron oxide contamination layer from the surface of the nanoparticles and adjusting p H to mild acidic and basic pHs.