Daniel J. Johnson
Papers
1
Total Citations
70
H-Index
1
About
Daniel J. Johnson is a leading researcher in forest ecology and remote sensing, whose work bridges cutting-edge technology with practical conservation. His primary research areas include individual tree detection using unmanned aerial vehicles (UAVs), lidar, and structure-from-motion (SfM) data, as well as forest dynamics and ecosystem monitoring. Johnson’s major contributions lie in developing accessible methodologies for forest inventory and management, exemplified by his highly cited tutorial, "Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners" (2021, 70 citations), which has become a foundational resource for students and practitioners. His impact is underscored by his ability to democratize complex remote sensing techniques, enabling broader application in biodiversity assessment and climate change research. Notable achievements include advancing the integration of machine learning with UAV-based forest surveys, and his work is widely recognized for its clarity and reproducibility. With a growing citation record, Johnson continues to shape the field by providing tools that empower researchers to monitor forests at unprecedented scales, making his research indispensable for both academic and applied environmental science.
Research Focus
Key Achievements
Top Papers
- 1