On-Going Digital Forestry Projects

The Integrated Digital Forestry Initiative currently has a variety of on-going digital forestry-related research projects. Researchers from agriculture, engineering, computer science, aviation technology, information science and other disciplines are exploring remote sensing, big data and artificial intellegence and methods for applying them to forestry.

These projects are examples of how data science and related technologies are advancing our understanding of complex natural systems and facilitating sustainability of our natural resources at the same time.

Aerial Tree Inventory with LiDAR and UAS Images

AerialTreeInventorDFThumbnail5.jpgResearchers are working on methods to conduct aerial tree inventory using LiDAR and UAS images. AI-assisted automation allows for individual tree recognition and delineation and remote measurement of biometrics (size, biomass) in planted and natural forests. Learn More.

Monitor Stress Epidemiology

MonitorStressDFThumbnail2.jpgResearchers are working to detect and track pest, insect and pathogen incidence with machine learning on multi-temporal data and to monitor drought symptoms with multi-sensor platforms. Learn More.

Automated Tree Inventory with Photogrammetry

PhotogrammetryDFThumbnail.jpgResearchers are working to speed up automated tree measurement, using low-cost Optical Gauging System (LOGS) with stereo cameras and matching learning. Learn More.

Precision Management

PrecisionManagementDFTHumbnail.jpgPrecision managment tools can be utilized not only for forest management, but also during processing of lumber and logs. Researchers are using geo-referenced and image-assisted biometric evaluation for precision tree growth and yield monitoring. Resarch on log and lumber processing is also taking place. Learn More.

Tree Health and Quality Assessment with LiDAR

ForestImageLiDARThumbnail.pngResearchers are using ground-based LiDAR for precision tree structure characterization and analytical framework to assess quality and health of hardwoods using LiDAR data. Learn More.

UAS Disturbance Detection

UASDisturbanceDetectionDFThumbnail.jpgResearchers are using feature-based high-resolution classification on multi-temporal data for planned and unplanned disturbance, including fire, wind-throw, and logging. Learn More.

Terrestrial Mobile LiDAR for High Resolution Forest Inventory

HabibBackpackinMartellThumbnail.jpgResearchers are working to develop a backpack terrestrial system/platform and algorithms for fine-detail, automated measurements and trait characterization for forest plantations. Learn More.

Annotated Bark Image Bank

GazoSpeciesIDThumbnail.jpgResearchers are developing an annotated bark image bank of hardwood trees in the Central Hardwood region, as well as an AI algorithm for bark-based species recognition and a smartphone app for bark-based tree species identification. Learn More.

Urban Forest Inventory Using Artificial Intelligence

urbanforestryndhmthumbnail.pngResearchers are working to identify urban trees and quantify their ecosystem services by developing Artificial Intelligence (AI) algorithms that analyze remotely sensed data for major cities in Indiana. Learn More.

Forestry and Natural Resources, 715 West State Street, West Lafayette, IN 47907-2061 USA, (765) 494-3590

© Purdue University | An equal access/equal opportunity university | Integrity Statement | Copyright Complaints | Maintained by Agricultural Communication

Trouble with this page? Disability-related accessibility issue? Please contact us at agweb@purdue.edu so we can help.

Sign In