Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bioinspired Photodetectors: From Nature to Advanced Applications.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Programmable pH-responsive DNA inter-strand matching (PRISM) for precision molecular band-pass actuation.

Theranostics·2026
Same author

Cloning-based analysis of MHC class II DRB variation and Enterocytozoon bieneusi association in the Amur tiger (Panthera tigris altaica).

Molecular and biochemical parasitology·2026
Same author

Molecular detection and zoonotic genotyping of Enterocytozoon bieneusi in Siberian tigers (Panthera tigris altaica), Northeast China.

Parasitology research·2026
Same author

Nanozyme-engineered liners for proactive prevention of wear particle-induced osteolysis.

Nature communications·2026
Same author

Multimodal collaborative UAV framework for single rubber tree parsing.

Plant phenomics (Washington, D.C.)·2026

Related Experiment Video

Updated: Apr 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

915

Species-specific tree structural parameters extraction via UAV RGB-LiDAR data and multimodal instance segmentation.

Jiansen Wang1,2, Huaiqing Zhang1,2, Hanqing Qiu1,2

  • 1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.

Plant Phenomics (Washington, D.C.)
|April 27, 2026
PubMed
Summary

This study introduces a new AI framework, SAMFormer, for precise tree identification and structural analysis using drone data. It reveals that forest competition negatively impacts tree growth and carbon storage, while species mixing increases it.

Keywords:
Carbon stockClimate-adaptive forest managementStructural parametersTree species identificationUAV-Based multimodal remote sensing

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

17.0K

Related Experiment Videos

Last Updated: Apr 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

915
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.0K
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

17.0K

Area of Science:

  • Forestry
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Accurate species-specific tree phenotyping is challenging due to complex forest structures and variations.
  • Existing methods struggle with blurred canopy segmentation and species misclassification in dense forests.

Purpose of the Study:

  • To develop a scalable framework for extracting species-specific tree structural parameters at the individual tree level.
  • To improve fine-grained tree identification in complex forest environments.
  • To map forest carbon stock and analyze ecological relationships.

Main Methods:

  • Utilized ultrahigh-resolution UAV-based RGB and LiDAR data.
  • Developed a self-attention-guided spectral-structural multimodal fusion transformer (SAMFormer) with adaptive feature enhancement and cross-modal fusion modules.
  • Employed K-fold cross-validation for performance assessment.

Main Results:

  • SAMFormer achieved high accuracy (86.3% F1-score, 88.0% mAP@0.5), outperforming single-modal inputs and other models.
  • Generated large-scale species-specific maps of tree structural parameters and carbon stock.
  • Found a significant negative correlation between tree competition and structural parameters/carbon stock (p < 0.001).
  • Observed that species mixing enhances carbon stock compared to monocultures.

Conclusions:

  • The developed framework offers a high-throughput, non-destructive method for forest phenotyping.
  • Forest competition drives adaptive growth strategies that reduce biomass and carbon stock.
  • Mixed forests are more effective carbon sinks than monocultures, supporting climate-adaptive forestry.