Estimation of fresh sprout biomass based on tree variables of pollarding Turkey oak (Quercus cerris L.).

Pollarding of oak trees for livestock and animal feeding is a traditional application, and it has been used for centuries from generation to generation in southern and southeastern Turkey. Estimation of the fresh sprout biomass (FSB) potential of pollarded oak forests in high accuracy is important for sustainable forest management. In the present study, 40 trees were sampled from Turkey oak (Quercus cerris L.) stands that have been irregularly pollarded for animal husbandry in Adiyaman, southeastern Turkey. In order to estimate FSB, a multiple logarithmic linear model was developed with explanatory variables such as tree diameter at breast height (DBH), total tree height (H), mean sprout length (SL), and mean sprout age (SA), which are in a significant relationship with FSB. Stepwise multiple regression analysis was used to fit this multiple logarithmic linear model and to determine the best independent variable set. As a result of stepwise regression analysis, three models were obtained in which SL, DBH, and SA are independent variables. Model 1 estimates the FSB by taking only SL, Model 2 uses SL and DBH, and Model 3 uses SL, DBH, and SA as independent variables. All models were significant at p = 0.001 level. Model 1 explained the variation in FSB by 65%, Model 2 by 81%, and Model 3 by 86%. Inclusion of DBH in the model (Model 2) decreased the mean absolute error (MAE) of FSB by 26% and the inclusion of SA (Model 3) decreased MAE by 43%.

Biomass estimation of aboveground tree components for Turkey oak (Quercus cerris L.) in south-eastern Turkey.

Biomass equations were developed for different components of oak trees (Quercus cerris L.), which have been managed in coppices at different development stages-small-diameter forest (SDF) and medium-diameter forest (MDF). In this context, four biomass regression models-two based on diameter at breast height (DBH) alone and two based on DBH and total tree height (H)-were developed for each of the crown, stem, and total aboveground biomass components. Akaike's information criterion (AIC), root mean square error percentage (RMSE (%)), mean absolute error percentage (MAE (%)), adjusted coefficient of determination (Adj.R2), and bias values were used to evaluate and compare the suitability of a total of 12 regression models developed for biomass components. As a result, in the estimation of crown biomass, only DBH-based models provided higher estimation accuracy than DBH-H-based models. For the most suitable model, estimated values were Adj.R2 = 0.60, bias = - 0.009, RMSE = 66%, and MAE = 41%. In models developed to estimate stem biomass, the estimation accuracy of DBH-H-based models was higher. In the goodness-of-fit statistics calculated for the most suitable model, Adj.R2, bias, RMSE, and MAE were 0.89, 0.010, 38%, and 23%, respectively. The models developed to estimate the total aboveground biomass were all close in terms of estimation accuracy. The biomass components (crown and stem) in the total aboveground biomass were proportionally as follows: crown at 38% and stem at 62% in the SDF stage, and crown at 35% and stem at 65% in the MDF stage, indicating lower crown and higher stem partitioning as the development stage increased.

Assessment of visual landscape quality using IKONOS imagery.

The assessment of visual landscape quality is of importance to the management of urban woodlands. Satellite remote sensing may be used for this purpose as a substitute for traditional survey techniques that are both labour-intensive and time-consuming. This study examines the association between the quality of the perceived visual landscape in urban woodlands and texture measures extracted from IKONOS satellite data, which features 4-m spatial resolution and four spectral bands. The study was conducted in the woodlands of Istanbul (the most important element of urban mosaic) lying along both shores of the Bosporus Strait. The visual quality assessment applied in this study is based on the perceptual approach and was performed via a survey of expressed preferences. For this purpose, representative photographs of real scenery were used to elicit observers' preferences. A slide show comprising 33 images was presented to a group of 153 volunteers (all undergraduate students), and they were asked to rate the visual quality of each on a 10-point scale (1 for very low visual quality, 10 for very high). Average visual quality scores were calculated for landscape. Texture measures were acquired using the two methods: pixel-based and object-based. Pixel-based texture measures were extracted from the first principle component (PC1) image. Object-based texture measures were extracted by using the original four bands. The association between image texture measures and perceived visual landscape quality was tested via Pearson's correlation coefficient. The analysis found a strong linear association between image texture measures and visual quality. The highest correlation coefficient was calculated between standard deviation of gray levels (SDGL) (one of the pixel-based texture measures) and visual quality (r = 0.82, P < 0.05). The results showed that perceived visual quality of urban woodland landscapes can be estimated by using texture measures extracted from satellite data in combination with appropriate modelling techniques.

Estimation of Tree Size Diversity Using Object Oriented Texture Analysis and Aster Imagery.

This study investigates the potential of object-based texture parameters extracted from 15m spatial resolution ASTER imagery for estimating tree size diversity in a Mediterranean forested landscape in Turkey. Tree size diversity based on tree basal area was determined using the Shannon index and Gini Coefficient at the sampling plot level. Image texture parameters were calculated based on the grey level co-occurrence matrix (GLCM) for various image segmentation levels. Analyses of relationships between tree size diversity and texture parameters found that relationships between the Gini Coefficient and the GLCM values were the most statistically significant, with the highest correlation (r=0.69) being with GLCM Homogeneity values. In contrast, Shannon Index values were weakly correlated with image derived texture parameters. The results suggest that 15m resolution Aster imagery has considerable potential in estimating tree size diversity based on the Gini Coefficient for heterogeneous Mediterranean forests.