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Forest Resource Management Lab
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A lab that measures forest resources with airborne laser and drone data and explores how to manage them.
AboutAbout the lab
Measuring forest resources from the sky
The Forest Resource Management Lab aims at the sustainable use of forest resources, and conducts research on developing forest measurement technologies as well as on theory and planning for forest resource management.
The lab analyzes data obtained from airborne laser surveying and drone images to estimate information on individual trees in forests.
It is also developing methods that handle forest information from images and point cloud data using AI techniques such as machine learning and deep learning.
Key terms
- Point cloud data
- A collection of positional information for a large number of points, obtained from laser surveying or images. It represents the three-dimensional shape of a forest
- UAV
- Short for unmanned aerial vehicle (small), commonly known as a drone. It is used to photograph forests from above
ResearchResearch themes
Aiming at the sustainable use of forest resources, the lab conducts research on developing advanced forest measurement technologies, building theory related to forest resource management, and developing methods for forest planning and its evaluation. It also works on improving forest planning methods by modeling forest and tree growth, examining forest resource management methods using geographic information systems (GIS), and wide-area environmental analysis using remote sensing.
*Illustrative imageDevelopment of Point Cloud Analysis Systems
The lab analyzes point cloud data obtained from airborne laser surveying and drone (UAV) images to estimate the position and height of individual trees in a forest. It is also developing systems that estimate stand-level values, such as the average height and brightness of a forest.
*Illustrative imageAI-Based Methods for Analyzing Forest Information
The lab develops methods that handle forest information from images and point cloud data using AI techniques such as machine learning and deep learning. It works on automatic detection of planted trees and tree species identification from aerial images, and on tree species identification from airborne laser point cloud data.
MembersFaculty
The members of the Forest Resource Management Lab consist of faculty, doctoral students, master's students, and undergraduate students.As of 2026
- ProfessorKazukiyo Yamamoto
- Associate ProfessorYusuke Yamada
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Aerial view of Sahasradhara Waterfall and forest canopy, Sitakunda Eco Park” Sultan Sunnny / CC BY-SA 4.0 (cropped)
- Development of Point Cloud Analysis Systems: “"Punktewolke" Berlin Vertikale Vermessung des Hauptbahnhofs ( )” DLR German Aerospace Center / CC BY 2.0 (cropped)
- AI-Based Methods for Analyzing Forest Information: “Simplified neural network example” Mikael Häggström, M.D. Author info - Reusing images- Conflicts of interest: None Mikael Häggström, M.D. / CC0 (cropped)
- Related lab (木材物理学): “Timber Wood - geograph.org.uk - 187429” Derek Harper / CC BY-SA 2.0 (cropped)
- Related lab (森林社会共生学): “Temuan headman in his community forest garden” Pakbelang / CC0 (cropped)
- Related lab (生物システム工学): “JPL Engineers Work on Carbon Mapper Imaging Spectrometer (PIA25869)” NASA Jet Propulsion Laboratory / Carbon Mapper/JPL-Caltech / Public domain (cropped)
- Related lab (森林化学): “Cross section of Lilac wood” Park Hotel-1 / CC0 (cropped)
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