Forest plantations in Japan cover an area of 102,000 km² and 70% are composed of Japanese cedar and cypress trees. Half of the trees in these planted forests are overdue harvesting; therefore, it is necessary to realize the forest cycle by actively harvesting these trees and using them as domestic timber. Related to harvesting, the rate of fatalities and injuries per 1,000 in the forestry industry in 2019 was 2–10 times higher than in other industries; thus, the implementation of new technologies to improve productivity and safety is required in the industry. In this study, we focused on a labor saving technique for investigating and classifying trees in forests using tree-bark images taken by mobile devices or monocular cameras. Previously, tree classification using such images had not been considered; however, if tree species can be classified using a lightweight monocular camera, it will be possible to create a tree species distribution map at low cost, even in wide areas of mountain forest, by installing lightweight cameras on drones. Here, we were able to verify the classification of Japanese cedar and cypress trees by integrating existing technologies. Specifically, we developed a classification method for tree-bark images using two types of image data (cedar and cypress) acquired with a monocular camera. By preparing image data appropriately, this method achieved a classification rate >80% with minimum labor requirements; therefore, the developed classifier allowed us to successfully identify the trees in images. Our method may also facilitate information sharing and centralized database management, contributing to the adoption of IoT (Internet of Things) in the forestry industry.

The present study proposes a selection method for logging systems suitable for a specific terrain that uses conventional factors such as average slope and relief as well as a contour roundabout factor, which indicates the degree of complexity of the terrain surface. This selection method was applied to six watersheds in Kochi Prefecture, Japan. The aim was to assess the feasibility of the method and predict forestry infrastructure in the area by comparing the results with actual forest road networks and existing forest machinery in the watersheds. Topographic data were expressed through digital elevation model with a mesh resolution of 10 m. Topographic factors for evaluation were calculated using an open-source geographic information systems software, which also selected appropriate logging systems for each mesh. The results that were obtained almost mimicked the actual situations, particularly in the middle and eastern watersheds. However, the findings for the two western watersheds exhibited lower rates of medium- and small-sized machines, which are suitable for winching and/or small-sized cable systems, than those actually present. These findings indicate that although the dominant terrain in Kochi Prefecture would favor middle- and large sized cable systems such as tower yarders, actual operation systems may differ from the inferred results owing to the recently completed forest road networks and existing forest machinery in the corresponding areas.

In this study, an information sharing system adapted for Japanese wood transactions between all concerned parties (from the upper to lower levels of the wood distribution process) was developed. This system aimed to achieve effective use of a harvester via automatic judgment of log qualities during processing. To this end, overseas advanced trends and domestic conditions of wood transactions were investigated. StanForD (Standard for Forest machine Data and communication) 2010 was adopted as the base system, referring to progress in Swedish forestry since the introduction of ICT (Information and Communication Technology). Data items suitable for Japanese wood transactions, e.g., warp, Youngʼs modulus, wood density, butt end diameter, etc., were included using an expandable area in StanForD 2010. Moreover, output/input, map interface, and data conversion tools related to wood operating were added to the associated cloud server.

The authors estimated the expected value of the number of forest road collapses in a 1-km road segment caused by a rainfall event with various intensities. Radar/Raingauge-Analyzed Precipitation data, basic data of forest roads in private forest areas in the Toyama prefecture, and data about forest road collapses that occurred in the areas in the past 21 years were used to estimate the expected value. The estimated expected value was 10^-3 orders of magnitude with a rainfall event where the maximum rainfall per 24 hours was 100 mm or more, and less than 150 mm, while the value was 10^-1 orders of magnitude with a rainfall event of 300 mm or more, and less than 350 mm. This model enables us to predict the number of forest road collapses in a given area over a certain period, taking into account the increase in density of forest roads and the frequency of intense rainfall events due to climate change.

Localization in the forest is an important technology that can be used in a wide range of fields from forest management to cutting. However, it is difficult to obtain accurate location information in real-time using the global navigation satellite system. Real-time location information is indispensable for the autonomous driving of forestry machinery. In this study, we investigated the accuracy of simultaneous localization and mapping (SLAM) using a laser range finder (LRF) and RGBD camera for localization in the forest. We used LOAM and hdl graph slam as LRF SLAM. ORB-SLAM2 and RTAB-Map were also used as Visual SLAM. All SLAM algorithm was implemented on the robot operating system (ROS). The horizontal root-mean-square error was found to be 0.526 m and 0.619 m for LOAM and RTAB-Map, respectively. It was suggested that SLAM using LRF and RGBD cameras could prove to be adequate localization methods in the forest if integrated with other sensors.