Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis.

Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

Intellectual modifying a bare glassy carbon electrode to fabricate a novel and ultrasensitive electrochemical biosensor: Application to determination of acrylamide in food samples.

Acrylamide (AA) is a neurotoxin and carcinogen which is mainly formed in foods containing large quantities of starch processed at high temperatures and its determination is very important to control the quality of foods. In this work, a novel electrochemical biosensor based on hemoglobin-dimethyldioctadecylammonium bromide (HG-DDAB)/platinum-gold-palladium three metallic alloy nanoparticles (PtAuPd NPs)/chitosan-1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (Ch-IL)/multiwalled carbon nanotubes-IL (MWCNTs-IL)/glassy carbon electrode (GCE) is proposed for ultrasensitive determination of AA in food samples. Development of the biosensor is based on forming an adduct by the reaction of AA with α-NH2 group of N-terminal valine of HG which decreases the peak current of HG-Fe+3 reduction. The modifications were characterized by electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), energy dispersive X-ray spectroscopic (EDS) and scanning electron microscopy (SEM). Under optimized conditions, the biosensor detected AA by square wave voltammetry (SWV) in two linear concentration ranges of 0.03-39.0nM and 39.0-150.0nM with a limit of detection (LOD) of 0.01nM. The biosensor was able to selective detection of AA even in the presence of high concentrations of common interferents which confirmed that the biosensor is highly selective. Also, the results obtained from further studies confirmed that the proposed biosensor has a short response time (less than 8s), good sensitivity, long term stability, repeatability, and reproducibility. Finally, the proposed biosensor was successfully applied to determine AA in potato chips and its results were comparable to those obtained by gas chromatography-mass spectrometry (GC-MS) as reference method.