Evaluation of the capability of SPOT5-HRG data for predicting tree density in the northern Zagros forests

Quantitative attributes of forest stands are valuable data that are very important for the evaluation of forest resources. Regarding to unique structure of Zagros forests, we tried to predict tree density using SPOT5-HRG satellite data in this study. A systematic random grid consisting of 319 circle plots (0.1 ha) were used to collect field data. Spectral values related to field plots were extracted from original and the artificial bands composed of vegetation indices and principle component analysis. Ancillary data such as slope, aspect and elevation were also used. Multiple regression and stepwise method were used to predict tree density from 4 original spectral bands and 16 artificial bands as independent variables. Ancillary data didn' t improve the results. For considering geographic aspects effects, the study also was done for different aspects, separately. In the general model, predictive variables were PCAC2 (the 2nd component of PCA) and B2 (Red band) with the adjusted coefficient of determination of 0.26%. In the suggested models for the northern, southern, eastern and western forests, independent variables are PCAC2, Ratio, PCAC2, AVI, B1 and PVI, AVI, B3, with the adjusted coefficient of determination of 31%, 34%, 19% and 42%, respectively. The Results of model validation tests showed that all of the presented equations had a reliable validation and are useful for this area, however, for better estimation of tree density, we should find the other approaches

Forest stand volume estimation using IRS_P6 (LISS_IV) data (Case study: Lirehsar, Tonekabon)

Stand volume is an important criterion in forest sciences for monitoring status and function of forests, estimation of productivity, prediction and modeling of forest disturbance, economic and environmental issues and forest planning. The aim of this research is evaluation of the LISS_IV sensor of IRS_P6 satellite data ability for forest timber volume estimation. The study area (1240 ha) is located in watershed No. 35 (Lirehsar) of Mazandaran province. Using systematic random method, 87 circular plots with 0.1 ha area were measured to study the relationship between forest stand volume and satellite data. Correspondent digital data to plots were extracted from spectral and considered as independent variables. Original stand volume data, square root and logarithm of them were considered as dependent variables. Using stepwise regression, the best model (Log V= 8.64–0.19Mb3–0.044Rb3) respect to some criteria including RMSE, bias and correlation coefficient was chosen, while the value of criteria were 32.5%, 12.6% and 0.83%, respectively. Result showed that spectral data of the mentioned sensor have a moderate potential for stand volume estimation

Comparison of Logistic Regression and Discriminate Analysis in Recognition of the Factors Affecting on the Distribution of Quercus Libanii of Armardeh Forests at Baneh, Kurdistan Province

The study site, Armardeh forest, is located in Baneh, Kurdistan Province. The study area covers approximately 17000 ha. In this study, logistic regression and discriminant analysis methods were used and their performances were compared. Two random sampling grids were overlaid on the presence and absence area of Quercus libani type as dependent variables. Physiographic factors including slope, aspect, elevation and distance from drains were extracted for each sampling site and considered as independent variables. The results showed that the area under the ROC curve (0.746) for logistic regression method is higher than the discriminant analysis with 0.502. Furthermore, the overall accuracy of the classification of the logistic regression was higher than discriminate analysis. The results of this study can be used in management and restoration of these forests

Extraction of forest roads network map by Fuzzy theory and mathematical morphology

Road is one of the most important and obvious extractable feature in satellite imagery. Automatic road extraction from satellite imagery has many advantages such as updating data bases by spending less time and cost. The aim of present research is the automatic extraction of forest roads map using Liss_IV sensor imagery of IRS_P6 satellite. Because of frequent irregular objects in forest, roads are very complicated for extracting automatically. Therefore, the designed methodology for this research was in a way that can deal with this problem. For this aim, image of the study area was classified into two road and non road areas by a fuzzy logic. Then, morphological mathematic algorithm was used to extract the existed roads. By this method, forest roads map was extracted automatically with 88% overall accuracy. Also, morphological mathematic algorithm showed a great ability for recovering road line that was hidden or was cut off under forest canopy