Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.     

Detection of Apricot latent virus and Plum bark necrosis stem pitting-associated virus by RT-PCR in Eastern Anatolia (Turkey)

Field surveys were carried out to determine presence and incidence of Apricot latent virus (ApLV) and Plum bark necrosis stem pitting-associated virus (PBNSPaV) in the main stone fruit growing areas of Eastern Anatolia. RT-PCR and nested-RT-PCR techniques were used to detect ApLV and PBNSPaV, respectively. Three apricot samples out of 224 tested positive for ApLV, although infected trees showed no apparent disease symptoms. Of 45 sweet cherry and plum trees tested for PBNSPaV, 35 tested positive. Stem pitting symptoms were observed on the trunks of PBNSPaV-infected sweet cherries. Trunk bark was spongy and thick; pits and grooves were observed on the woody cylinder. The overall incidence of ApLV was 1.3% and of PBNSPaV was 77%. This is the first report of ApLV and PBNSPaV in Eastern Anatolia, Turkey.     

The Effects of Fabric and Conductive Wire Properties on Electromagnetic Shielding Effectiveness and Surface Resistivity of Interlock Knitted Fabrics

Our aim in this study was to investigate the effects of course density, yarn linear density and thickness and type of conductive wire on electromagnetic shielding effectiveness. Metal/cotton conductive composite yarns were produced by the core-spun technique on the ring spinning machine, involving stainless steel, copper and silver coated copper wires with 40 μm, 50 μm, 60 μm thicknesses and Ne10/1 and Ne20/1 count yarns. The interlock fabrics were knitted on a 7G flat knitting machine with the three different machine settings. The EMSE and the surface resistivity of knitted fabrics were measured by the co-axial transmission line method according to the ASTM-D4935-10 standard in the frequency range from 15 to 3000 MHz and by the ASTM D257-07 standard, respectively. It was observed that all fabrics shielded around 95 % of electromagnetic waves at low frequencies, 80 % at medium frequencies and 70 % at high frequencies. Increasing the course density and thickness of conductive wire in interlock knitted fabrics increased the EMSE correspondingly. The knitted fabrics that had been produced with high yarn count showed greater EMSE because there was less isolation. The effect of the metal wire type was highly significant between 15 and 600 MHz.     

Quantifying the effect of pine mistletoe on the growth of Scots pine

Mistletoe infection results in substantial growth losses in mistletoe‐infected forests. This study reports and evaluates the results of retrospective analyses of radial growth of Scots pine (Pinus sylvestris) in relation to the level of infection of pine mistletoe (Viscum album ssp. austriacum). A total of 43 Scots pine trees were destructively sampled from different sites. Of these trees, 14 were uninfected and 29 were infected. Infection classes were determined using six‐class dwarf mistletoe rating system (DMRS). All needle and mistletoe biomass were removed completely and weighed for each sampled tree. Subsamples from needles and all mistletoe biomass were taken to the laboratory for oven‐dried weight determinations. Five‐cm‐thick wood discs were cut from the stem at the breast height (1.3 m) to determine annual basal area increment for the last 25 years. In addition to DMRS, new infection classes were created using mistletoe‐to‐needle biomass (MB/NB) ratio. The results showed that the radial growth losses could be as much as 41% to 64% at different infection levels. The rate of growth loss in relation to DMRS and MB/NB ratio was similar, but with a larger variability in DMRS values. The results showed that both DMRS rating and MB/NB ratio seem to be important for quantifying growth loss on Scots pine trees infected with mistletoe. The results of this study can also be invaluable in modelling the effects of mistletoe on the growth of Scots pine trees.     

Comparison of the bordered pits of two species of spruce (Pinaceae) in a green and kiln-dried condition and their effects on fluid flow in the stem wood in relation to wood preservation

The relationship between bordered pit aspiration, pit sizesand permeability measured as preservative uptake and expressedas porosity was examined in two species of spruce, Sitka spruce(Picea sitchensis (Bong.) Carr.) grown in the UK and Easternspruce (Picea orientalis (L.) Link.) grown in Turkey, before(in the freshly felled green condition) and after kiln drying.A 2.5 per cent chromated copper arsenate preservative solutionwas allowed to flow in either the longitudinal or tangentialdirection of the stem wood and the uptake was assessed. Borderedpit anatomy was examined by light and scanning electron microscopyand image analysis was used to characterize the samples. Thepermeability of the wood declined following drying but lessso in the Sitka spruce. From an analysis of measurements madeon the wood features it appeared that basic density, latewoodpercentage and degree of pit aspiration were the most importantfeatures explaining these results. The structure of the borderedpits varied between the two species and the relative size ofthe aperture in comparison with the pit chambers was greaterin the Eastern spruce. The effects of these and other variablesincluding differences in conventional drying systems and naturaltree responses to environmental conditions on pit behaviouraffecting permeability are discussed.     

Modeling surface fuels moisture content in Pinus brutia stands

Fuel moisture content is an important variable for forest fires because it affects fuel ignition and fire behavior. In order to accurately predict fuel ignition potential, fuel moisture content must be assessed by evaluating fire spread, fireline intensity and fuel consumption.Our objective here is to model moisture content of surface fuels in normally stocked Calabrian pine(Pinus brutia Ten.) stands in relation to weather conditions, namely temperature, relative humidity, and wind speed in the Mugla province of Turkey. All surface fuels were categorized according to diameter classes and fuel types. Six fuel categories were defined: these were 0–0.3, 0.3–0.6, and0.6–1 cm diameter classes, and cone, surface litter, and duff. Plastic containers 15 9 20 cm in size with 1 9 1 mm mesh size were used. Samples were taken from 09:00 to19:00 h and weighed every 2 h with 0.01 g precision for10 days in August. At the end of the study, samples were taken to the laboratory, oven-dried at 105 °C for 24 h and weighed to obtain fuel-moisture contents. Weather measurements were taken from a fully automated weather station set up at the study site prior to the study. Correlation and regression analyses were carried out and models were developed to predict fuel moisture contents for desorption and adsorption phase for each fuel type categories. Practical fuel moisture prediction models were developed for dry period. Models were developed that performed well with reasonable accuracy, explaining up to 92 and 95.6%of the variability in fuel-moisture contents for desorption and adsorption phases, respectively. Validation of the models were conducted using an independent data set and known fuel moisture prediction models. The predictive power of the models was satisfactory with mean absolute error values being 1.48 and 1.02 for desorption and adsorption as compared to the 2.05 and 1.60 values for the Van Wagner's hourly litter moisture content prediction model. Results obtained in this study will be invaluable for fire management planning and modeling.