Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.

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Two heuristic techniques, the genetic algorithm (GA) and Tabu search (TS), both with an embedded linear programming routine for earthwork allocation, were compared to a manually designed forest road profile. The manually designed road length was 345.7 m and its average gradient was 14.1%. The best costs of the profiles designed by GA and TS, without changing the placement of control points, were less than that designed manually. The best cost found by GA was almost the same as the global optimum solution. While TS could not find a better solution than GA, it usually found a good solution in less time. It was not possible to search all alternatives by changing the placement of control points and find the global optimum solution within a reasonable time. However, it can be concluded from the results that both GA and TS found good solutions within a reasonable time. Since it is not possible to manually evaluate many alternatives, road designers should find heuristic techniques helpful for design of the road profile. Moreover, the effect of the number of control points on construction costs was examined. The results indicated that increasing the number of control points reduces the construction costs. However, driving safety and comfort might be decreased.

Designing forest road networks in a large forest land is a challenging task because many feasible alternatives exist and need to be analyzed. To provide field managers with an analytical tool that can create and analyze alternative road networks, we have developed a road network optimization model. The model formulates a large network problem in which links represent two timber transportation options from evenly distributed timber locations: on-road transportation via new roads and off-road transportation using skidders. A heuristic network algorithm is employed to solve the network problem and identify cost-efficient road networks for timber harvesting under given cost parameters. To demonstrate our model, we applied it to a 4760 ha forest in the upper part of the Mica Creek watershed in Idaho owned by Potlatch Forest Holdings, Inc. The sensitivity analyses were conducted to verify the model’s performance under various cost and volume settings. The model-generated road network was compared with a road network proposed by experienced forest engineers in Potlatch. The sensitivity analyses confirm that the model appropriately responds to changes in input parameters. Comparisons between the model output and the manually designed road network indicate that the model tends to develop a tree-shape road network to evenly cover the entire management area.