Performance, Capability and Costs of Motor-Manual Tree Felling in Hyrcanian Hardwood Forest

Motor-manual tree felling is the most labor-intensive component of all harvesting operations and frequently represents a bottleneck in wood production. The study of motor-manual tree felling was carried out in two compartments in the Namkhaneh district of Kheyrud Forest. The objects of this study were as follows: time study of tree felling operations, estimate of chainsaw productivity and costs, development of a regression model in uneven-aged stand using single-tree selection methods. The factors affecting total felling time regression model (increasing order of importance) were DBH of harvested trees, direction of felling regarding the lay and inter-tree distance. The hourly production of chainsaw felling with and without delay time was 56.4 cubic meters per hour (13 tree/hour) and 80.7 cubic meters per hour (19 tree/hour), respectively. Productivity of chainsaw felling increased in relation to tree DBH as power relation. The cost of chainsaw felling with and without delay time was 0.55 and 0.39 USD/m3, respectively. The cost of felling increased as simple exponential equation when DBH of harvested trees decreased. However, the unit felling cost for chainsaw operation decreased as the tree size increased. Total felling cycle time without delay averaged 3.14 minutes and with delay time it averaged 4.5 minutes. Productivity was more sensitive to DBH than felling direction and inter-tree distance

Mapping Lorey’s height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images

Lorey’s height, representative of mean height in uneven-aged forest stands, is a valuable parameter for forest ecosystem management. While in situ measures provide the most precise information, remote-sensing techniques may provide less expensive but denser and more operational alternative of Lorey’s height estimation over highly mountainous areas. This research aims first to evaluate the performances of two nonparametric data mining methods, random forest (RF) and artificial neural network (ANN), for estimation of Lorey’s height using ice, cloud and land elevation satellite/geoscience laser altimeter system (ICESat/GLAS) in Hyrcanian forests of Iran and then to provide Lorey’s height map using a synergy of ICESat/GLAS and optical images (TM and SPOT). RF and ANN GLAS height models were developed using waveform deterministic metrics, principal components (PCs) from principal component analysis (PCA) and terrain index (TI) extracted from a digital elevation model (DEM). The best result was obtained using an ANN combining first three PCs of PCA and waveform extent ʺWextʺ (RMSE = 3.4 m, RMSE% = 12.4). In order to map Lorey’s height, GLAS-estimated heights were regressed against indices derived from optical images and also topographic information. The best model (RF regression with RMSE = 5.5 m and = 0.59) was applied on the entire study area, and a wall-to-wall height map was generated. This map showed relatively good compatibility with in situ measurements collected in part of the study area