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  • 1
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    GIS-based comparative characterization of groundwater quality of Tabas basin using multivariate statistical techniques and computational intelligence (2018)
    Springer
    Details
    Publication Date: 2018-10-03
    Print ISSN: 1735-1472
    Electronic ISSN: 1735-2630
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Springer
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  • 2
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    Identification and abundance of fungal flora in egg, and larvae of Acipenser persicus in Shahid Beheshti Sturgeon Rearing Center (2007) (2018)
    Details
    Publication Date: 2021-05-19
    Description: Fungal flora in egg, yolk sac larvae and larvae of Acipenser persicus were identified and studied. Totally, 270 specimens from Shahid Beheshti Sturgeon Rearing Center were examined. A heterogeneous solution from samples was prepared and inoculated on culture media SDA+C and CMA+C in lines under sterile conditions. Wet mounts were prepared for the identification of Saprolegnia sp. and the inoculants were cultured on culture media GP containing gentamycin and chloramphenicol. We found Penicillium, Cladosporium, Fusarium, Yeast, Mucor sp., Aspergillus niger and Paecilomuces on egg samples in order of frequency and in water samples we observed Penicillium, Cladosporium, Fusarium, Yeast, Mucor sp., Aspergillus niger, and Paecilomyces. Fungal species identified in yolk sac larvae included Penicilliium sp., Cladosporium sp., Fusarium sp., Alternaria sp., Yeast and in water samples we found Penicillium, Cladosporium, Fusarium, and Yeast, while in larvae we saw Cladosporium sp., Penicilliium sp., Fusarium sp., Alternaria sp., Aspergilus fumigatus, Yeast and Mucor spp. In water samples containing larvae we were able to identify Cladosporium sp., Penicilliium sp., Fusarium sp., Yeast and Aspergilus niger. Fungal species such as Cladosporium sp., Penicillium, Fusarium, Yeast and Saprolegnia were detected in all four sampling mediums. T-test indicated no significant differences in total counts (colonies/2 plates in all samples) in eggs (15.08 plus or minus 3.51 colony forming unit; CFU) and in water (15.91c 2.63) samples. However, t-test indicated significant differences in total counts in yolk sac larvae (5.33 plus or minus 1.05) and in water (11.77 plus or minus 2.39) samples. T-test showed no significant differences in total counts of larvae (32 plus or minus 12.46) and water (31.11 plus or minus 12.79) samples.
    Description: Published
    Keywords: Fungi ; Brackish ; Larvae ; Identification ; Eggs
    Repository Name: AquaDocs
    Type: Journal Contribution , Refereed
    Format: pp.35-42
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  • 3
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    Identification and abundance of fungal flora in egg, and larvae of Acipenser persicus in Shahid Beheshti Sturgeon Rearing Center (2007) (2021)
    In:  http://aquaticcommons.org/id/eprint/23798 | 18721 | 2018-07-27 14:59:33 | 23798 | Iranian Fisheries Science Research Institute
    Details
    Publication Date: 2021-07-15
    Description: Fungal flora in egg, yolk sac larvae and larvae of Acipenser persicus were identified and studied. Totally, 270 specimens from Shahid Beheshti Sturgeon Rearing Center were examined. A heterogeneous solution from samples was prepared and inoculated on culture media SDA+C and CMA+C in lines under sterile conditions. Wet mounts were prepared for the identification of Saprolegnia sp. and the inoculants were cultured on culture media GP containing gentamycin and chloramphenicol. We found Penicillium, Cladosporium, Fusarium, Yeast, Mucor sp., Aspergillus niger and Paecilomuces on egg samples in order of frequency and in water samples we observed Penicillium, Cladosporium, Fusarium, Yeast, Mucor sp., Aspergillus niger, and Paecilomyces. Fungal species identified in yolk sac larvae included Penicilliium sp., Cladosporium sp., Fusarium sp., Alternaria sp., Yeast and in water samples we found Penicillium, Cladosporium, Fusarium, and Yeast, while in larvae we saw Cladosporium sp., Penicilliium sp., Fusarium sp., Alternaria sp., Aspergilus fumigatus, Yeast and Mucor spp. In water samples containing larvae we were able to identify Cladosporium sp., Penicilliium sp., Fusarium sp., Yeast and Aspergilus niger. Fungal species such as Cladosporium sp., Penicillium, Fusarium, Yeast and Saprolegnia were detected in all four sampling mediums. T-test indicated no significant differences in total counts (colonies/2 plates in all samples) in eggs (15.08 ±3.51 colony forming unit; CFU) and in water (15.91± 2.63) samples. However, t-test indicated significant differences in total counts in yolk sac larvae (5.33 ±1.05) and in water (11.77 ±2.39) samples. T-test showed no significant differences in total counts of larvae (32 ±12.46) and water (31.11 ±12.79) samples.
    Keywords: Biology ; Fungal ; Acipenser persicus ; Egg ; Larvae ; Caspian Sea ; Iran
    Repository Name: AquaDocs
    Type: article , TRUE
    Format: application/pdf
    Format: application/pdf
    Format: 35-42
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  • 4
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    Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump (2021)
    In:  Remote Sensing
    Details
    Publication Date: 2021-12-22
    Description: Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 5
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    Determination of Chromium Concentration and Spatial Distribution in a Copper Mine using Reflectance Spectroscopy and Remote Sensing. (2022)
    Details
    Publication Date: 2022-08-19
    Type: info:eu-repo/semantics/lecture
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  • 6
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    Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters (2023)
    In:  Ecological Informatics
    Details
    Publication Date: 2023-05-09
    Description: Our study investigates the capability of the environmental mapping and analysis program (EnMAP) scenes simulated using the EnMAP end-to-end simulator software (EeteS) based on the AISA Eagle airborne data to predict chlorophyll-a (Chl-a) and total suspended solids (TSS) as two of the most crucial water quality indicators. Three machine learning (ML) approaches (principal component regression(PCR), partial least square regression (PLSR) and random forest (RF)) were employed to establish links between the simulated image spectra and the above-mentioned water attributes of the samples collected from several inland water reservoirs within the southern part of the Czech Republic. Airborne hyperspectral images were also used to develop a model to compare its performance with models developed based on the simulated EnMAP data. Adequate prediction accuracy was obtained for both Chl-a (R2 = 0.89, RMSE = 43.06 g/L, and Lin’s concordance correlation coefficient (LCCC) = 0.91) and TSS (R2 = 0.91, RMSE = 17.53 mg/L, and LCCC = 0.94), which were close enough to those obtained from the airborne hyperspectral images. Chl-a and TSS correlated with the wavelengths around 550 nm and 700 to 750 nm of the red and near-infrared (NIR) regions. In addition, the spatial distribution maps derived from the simulated EnMAP were comparable to those obtained from the AISA Eagle airborne data. Overall, it can be concluded that the simulated EnMAP image successfully and reliably predicted and spatially mapped the selected biophysical properties of the small inland water bodies.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 7
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    Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface? (2023)
    In:  Science of the Total Environment
    Details
    Publication Date: 2023-06-20
    Description: Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible–near-infrared–shortwave infrared (VNIR–SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR–SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg−1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg−1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR–SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.
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  • 8
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    Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effect of fusion techniques on model performance (2022)
    In:  Environmental Pollution
    Details
    Publication Date: 2024-04-22
    Description: Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value, Brovey, principal component analysis, Gram-Schmidt, wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR–SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements’ prediction models.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 9
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    On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains (2024)
    In:  Soil and Tillage Research
    Details
    Publication Date: 2024-05-14
    Description: Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt and sand), which makes SOC prediction complicated, especially in samples with considerable pedological variability. Hence, understanding the relationship between SOC and soil texture is important within the context of SOC prediction using remote sensing data. The main objective of this study was to find the impact of soil texture on the performance of local SOC prediction models that were developed on Sentinel-2 (S2) multispectral and CASI/SASI (CS) hyperspectral airborne data as the main predictor variables. One approach to that objective was to lowering the texture variance by stratification of the samples. Therefore, soil samples collected from four agricultural sites in the Czech Republic were segregated based on the i) site-based and ii) texture-based stratification strategies. Random forest (RF) models were then developed on all stratified classes with and without considering the soil texture parameters as predictor variables and results were compared with those obtained by the RF models developed on the non-stratified (NS) samples. Both stratification strategies provided more homogeneous classes, which enhanced the accuracy of SOC prediction, compared to using the NS samples. In addition, the texture-based RF models yielded higher accuracy predictions than the site-based ones. Except sand, adding texture parameters to the main predictors improved accuracy of the models, so that the highest prediction performance was obtained by a texture-based model developed on clay added CS data. Overall, texture-based stratification could significantly enhance the SOC prediction, when the texture parameters were added to the S2 and CS data as the main predictor variables.
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