İlker Ercanli

Prediction of Some Stand Parameters using Pan-Sharpened IKONOS Satellite Image

Abstract The objective of this study is to evaluate the relationships between stand parameters (stand volume, basal area and dominant height), and band reflectance values and six vegetation indices (VIs) obtained from pan-sharpened, IKONOS satellite image in Artvin-Genya Mountain located in northeastern part of Turkey. Multiple stepwise regression analysis is used to estimate the stand parameters. The results indicated that a linear combination of EVI and DVI for stand volume and basal area (adjusted R2=0.55; a root mean square error (RMSE)=153.53 m3 ha-1 and adjusted R2=0.59; RMSE=12.37 m2 ha-1), respectively, and a linear combination of SAVI, EVI and DVI for dominant height (adjusted R2=0.57; RMSE=3.80 m) were better predictors than a linear combination of IKONOS Band 1and Band 4 for stand volume and basal area, and the IKONOS Band 1 and Band 2 for dominant height (R2=0.41; RMSE=181.01 m3 ha-1, R2=0.43; RMSE=14.84 m2 ha-1 and R2=0.45; RMSE=4.62 m), respectively. This study concludes that the regression models developed with IKONOS VIs were able to predict stand parameters better than do the IKONOS band reflectance values in Artvin-Genya Mountain forest areas.

Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images.

RELATIONSHIPS BETWEEN SITE INDEX AND SOME EDAPHIC AND PHYSOGRAPHIC FACTORS OF PURE ORIENTAL SPRUCE (Picea orientalis Link.) IN ARTVİN GENYA MOUNTAIN

Bu arastirma Artvin Orman Bolge Mudurlugu, Artvin Orman Isletme Mudurlugu, Merkez Isletme Şefligi sinirlari icerisindeki Genya Dagi bolgesinde yayilis gosteren saf Dogu Ladini mescerelerinde bonitet endeksi ile bazi edafik ve fizyografik ozellikler arasindaki iliskilerin saptanabilmesi amaciyla gerceklestirilmistir. Bu amacla Genya Dagi bolgesinde saf olarak yayilis gosteren Dogu Ladini mescerelerinden 50 tane deneme alani secilmis, her bir deneme alanina iliskin fizyografik ve edafik ozellikler belirlenmistir. Topraga iliskin ozelliklerin belirlenebilmesi icin toprak profilleri acilmis ve ornekler alinmistir. Ayrica her bir deneme alaninda mescerelerin bonitet endeksi (100 yasindaki ust boy) belirlenmistir. Mescere bonitet endeksi ile edafik ve fizyografik faktorler arasindaki iliskiler korelasyon analizi ile sorgulanmistir. Bu ekolojik etmenlerden egim, fizyolojik toprak derinligi, mutlak toprak derinligi, Ah ve B horizonundaki kil ve kum miktarilari (%) ile bonitet endeksi arasinda onemli ve anlamli iliskiler bulunmustur. Anahtar Kelimeler: Dogu Ladini, Bonitet Endeksi, Edafik Faktor, Fizyografik Faktor, Artvin.

Positive effect of forest structural diversity on aboveground stand carbon stocks for even-aged Scots pine (Pinus sylvestris L.) stands in the Sarıçiçek Forest, Northern Turkey

ABSTRACT This study examines the relationships between forest structural diversity indices and aboveground stand carbon storage for even-aged and pure Scots pine stands located in the Sarıçiçek Forest, Northern Turkey. In the even-aged Scots pine stands, 293 sample plots were selected to represent various stand conditions such as site quality, age, and stand density. The stand structural diversity, including Shannon’s, improved Shannon, Simpson’s, McIntosh, Margalef, and Berger–Parker indices, was used to correlate the stand carbon storage values. Positive partial correlation coefficients between stand carbon storage and forest structural diversity indices, including the improved Shannon index (r = 0.770), Shannon’s index (r = 0.742), Simpson’s index (r = 0.703), the Berger–Parker index (r = 0.657), the Gini index (r = 0.390), and the Margalef index (r = 0.327), were found at the 0.01 level. These results offer an enhancement of theories concerning positive relationships between stand carbon storage and stand structural diversity for pure and single-species forests. Moreover, regarding biodiversity suitability and stand carbon storage as carbon sinks, the results illustrate that forest stands with higher structural diversity may be preferred when used to mitigate global warming.

Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey

Abstract The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values.

Artificial neural network models predicting the leaf area index: a case study in pure even-aged Crimean pine forests from Turkey

BackgroundLeaf Area Index (LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network (ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.MethodsOne hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.ResultsThe correlation coefficients between LAI and stand parameters (stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters (R2adj. = 0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4–19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI (SSE (12.1040), MSE (0.1223), RMSE (0.3497), AIC (0.1040), BIC (− 77.7310) and R2 (0.6392)) compared to the other studied techniques.ConclusionThe ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Mixed effect models for predicting breast height diameter from stump diameter of Oriental beech in Göldağ

Diameter at breast height (DBH) is the simplest, most common and most important tree dimension in forest inventory and is closely correlated with wood volume, height and biomass. In this study, a number of linear and nonlinear models predicting diameter at breast height from stump diameter were developed and evaluated for Oriental beech (Fagus orientalisLipsky) stands located in the forest region of Ayancik, in the northeast of Turkey. A set of 1,501 pairs of diameter at breast height-stump measurements, originating from 70 sample plots of even-aged Oriental beech stands, were used in this study. About 80 % of the otal data (1,160 trees in 55 sample plots) was used to fit a number of linear and nonlinear model parameters; the remaining 341 trees in 15 sample plots were randomly reserved for model validation and calibration response. The power model data set was found to produce the most satisfactory fits with the Adjusted Coefficient of Determination, R2adj (0.990), Root Mean Square Error, RMSE (1.25), Akaike’s Information Criterion, AIC (3820.5), Schwarz’s Bayesian Information Criterion, BIC (3837.2), and Absolute Bias (1.25). The nonlinear mixed-effect modeling approach for power model with R2adj(0.993), AIC (3598), BIC (3610.1), Absolute Bias (0.73) and RMSE (1.04) provided much better fitting and precise predictions for DBH from stump diameter than the conventional nonlinear fixed effect model structures for this model. The calibration response including tree DBH and stump diameter measurements of the four largest trees in a calibrated sample plot in calibration produced the highest Bias, -5.31 %, and RMSE, -6.30 %, the greatest reduction percentage.

Developing dynamic site index models by using autoregresive modeling approach for scots pine stands in Çankırı forests

In this study, it is aimed that the dynamic site index models for scots pine (Pinus sylvestris L.) stands in Çankırı forests were developed by using Generalized Algebraic Difference Approach, GADA, and Autoregresive Modeling Approach which are up-to-date and complex methods for site index modeling. In this aiming, the model structures of Bertalanffy-Richards, M1, ve Hossfeld, M2-M3, based on Generalized Algebraic Difference Approach were developed and compared with both Nonlinear regression analysis and Autoregresive modeling approach by using 112 stem analysis obtained from studied area. The best predictive model, Hossfeld model, M3, produced the R 2 value of 0.9336 with D.W. of 1.2890 for nonlinear regression analysis and the R 2 value of 0.9449 with D.W. of 1.9903 for Autoregressive modeling, approach, thus this modeling approach has provided a solutions for serial-correlations, autocorrelations, originating from stem analysis data being as time series property. Additionally, the dynamic site index model developed has produced compatible predictions with the expected growth laws, e.g. polymorphism, multiple asymptote and base-age invariable properties in modeling relationships between dominant height and ages.

The construction of diameter-height models based on nonlinear mixed effect regression equations for oriental spruce located in mixed oriental spruce-scotch pine stands at Trabzon Forest District

The goal of this study is to determine the effects of various timber preservers on bending strength (BS), modulus of elasticity (MOE) and internal bond (IB) of particleboard, and whether the levels of those of effecs are importance or not The following material were used for manufacturing of experimental boars; wood chips, urea-formaldehyde and various wood preservers. The urea-formaldehyde were utilized according to the ovendry weight of the chips, the other chemicals were treated as to the ovendry weight of the adhesive. The chips were impregnated with solutions of the preservers in the gluing machine before adhering treatment. The boards were manufactured by pressing at temperature 150°C and pressure 25–28 kp/cm2. As a result; the mechanical properties of the board have increased with increasing of using amounts of impregnating substances. According to the control, the rates of increase have ranged from 2.01% to 34.93% for BS, from 2.67% to 49.18% for MOE, and from 0.50% to 42.13% for IB. For BS and MOE, while this increase is unimportant statistically in the boards impregnated with pine rosin, alcid resin and immersol WR, it is important in the other boards. For IB, the increase has not significance in the boards treated with boric acid/borax, tanalith CBC and tanalith CBC/boric acid/borax, whereas it has significance in the others. Keywords: Impregnation, Industrial wood chips, Particleboard, Mechanical properties.

THE CONSTRUCTION OF VARIABLE DENSITY YIELD TABLE FOR ORIENTAL SPRUCE (Picea orientalis (L.) LINK.) STANDS IN ARTVIN CENTRAL STATE FOREST ENTERPRISE

Bu calismada, Artvin Merkez Isletme Şefligi icerisinde yer alan Dogu Ladini Mescereleri icin Sikliga bagli hasilat tablosu duzenlenmistir. Veriler; sistematik ornekleme yontemiyle secilen 104 adet ornek alandan elde edilmistir. Orneklenen mescerelerin yaslari 30-200, bonitet siniflari I-IV ve Siklik dereceleri ise 0.2-1.2 arasinda degismektedir. Dikili agac hacminin hesaplanabilmesi icin calisma alanindan secilen degisik boyutlardaki 62 adet ornek agac verilerine bagli olarak tek girisli agac hacim tablosu duzenlenmistir. Duzenlenen sikliga bagli hasilat tablosu ile mescere yasi, bonitet endeksi ve siklik derecelerinin fonksiyonu olarak kalan ve ayrilan mescereye iliskin buyume elemanlari tahmin edilebilmektedir. Elde edilen sonuclar; temel buyume yasalari ve literatur bilgileri ile uyumlu bulunmustur. Anahtar Kelimeler: Dogu Ladini, Sikliga bagli hasilat tablosu, Buyume, Siklik