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ABSTRACT:One of the cost factors in forest management is the development of road infrastructure. The objective of study was to develop a method using GIS and Multi-criteria Evaluation (MCE) to design a forest road network with the lowest construction cost while maintaining other technical requirements. Six road alternatives meeting technical requirements were developed using PEGGER. Then MCE was used to evaluate the construction costs of the candidate networks. The decision making group identified six factors as being relevant to the costs of forest roads. Then factors were compared in a pair-wise comparison, in the context of the Analytic Hierarchy Process to develop weights of map layers. Then weights and factors were entered into the MCE module to create a final suitability map. The total cost of each alternative was extracted from the suitability map and the unit cost of each alternative was calculated. The results showed that alternatives one and two had the highest and lowest unit costs, respectively. The results illustrated the utility of using GIS and MCE to improve the planning process.

This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI datasets, respectively. Moreover, this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.

Spatio‐temporal patterns of throughfall (TF) have often been studied under forest canopies. Few reports, however, have been made on small‐scale TF variability in deciduous forest stands. In the present research, the spatial heterogeneity and temporal stability of TF under five individual persian oak trees (Quercus brantii var. Persica) was quantified. The research site was in the Zagros forests in western Iran, where mean annual precipitation and temperature are equal to 587.2 mm and 16.9 °C, respectively. Data from 23 rainfall events were aggregated to assess the spatial correlation of TF. Variograms for TF beneath two of the five trees reached a stable sill at the range of 5–6 m. The redistribution of TF within the canopy was highly variable in time, attributable to seasonal variation in canopy foliation and meteorological factors. As the length of the sampling period increased, the spatial variability of TF decreased and the temporal stability of the TF pattern increased. Time stability plots of TF normalized with respect to mean and variance showed a moderate general persistence for all individual trees. We conclude that single trees modify the spatial distribution of TF reaching the forest floors. Copyright © 2012 John Wiley & Sons, Ltd.