AI-Based Tree Seedling Detection and Biodiversity Monitoring in Reforested Areas

Revolutionary AI algorithms combined with high-resolution drone remote sensing technology for next-generation forest ecosystem protection and sustainable forest management solutions.

📅
Multi-year Project (Granted)
💰
Environmental Protection
🤖
AI-Based Automation
🌱
Forest Protection
🔬
Smart Forest Management

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Expected Outcomes & Impact

Revolutionary improvements in forest management efficiency and ecosystem protection

90%+
AI Detection Accuracy
Tree seedling identification
75%
Cost Reduction
vs Traditional monitoring
Real-time
Data Processing
Automated analysis
Non-invasive
Wildlife Monitoring
Thermal imaging technology
Multi-scale
Area Coverage
From plot to landscape
AI-driven
Decision Support
Evidence-based management

Key Benefits

Enhanced Efficiency

Automated monitoring reduces manual labor and increases survey accuracy

Ecosystem Protection

Non-invasive wildlife monitoring preserves natural behaviors

Data-Driven Decisions

Evidence-based management strategies for optimal outcomes

Research Team

Interdisciplinary collaboration between leading institutions

Research Team Photo - Seoul National University & Inha University Collaboration

Multi-Institutional Research Consortium

Joint research initiative combining Seoul National University's forestry expertise with Inha University's AI and remote sensing capabilities, creating a synergistic approach to forest monitoring innovation.

🌲
Seoul National University
Forest and Life Science
• Remote sensing data collection
• Multi-sensor drone operations
• Forest management strategy development
• Field validation and ground-truth data
🔬
Incheon National University
Drone-based Wildlife Monitoring
• Thermal camera drone operations
• Wildlife behavior analysis
• Biodiversity assessment protocols
• Ecological impact evaluation
🤖
Inha University
AI Automation Development
• AI algorithm development and optimization
• Deep learning model training
• Image classification and object detection
• Automated analysis pipeline development

Research Excellence & Innovation

15+
Research Publications
10+
PhD Researchers
5+
Years Experience
3
Partner Institutions

Contributing to Sustainable Forest Management

Advancing Korea's forest industry through innovative AI technology and comprehensive ecosystem monitoring for a sustainable future.

🌍 Environmental Protection
🔬 Scientific Innovation
📈 Economic Impact

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Research Overview

Korea's forest resources have reached a mature stage with 77% of forests being over 31 years old (4th age class or higher). For forestry's new leap forward, a circular forest economy approach is needed that creates virtuous cycles in the value chain from forest resource formation and cultivation to timber production, distribution, processing, and consumption.

Precise and cost-effective monitoring of reforestation sites after harvesting is essential, and innovative technology development for successful establishment of reforested areas is urgently needed.

This research aims to develop an innovative system that automatically monitors tree seedling detection and biodiversity in reforested areas by integrating high-resolution drone imagery with AI technology, contributing to forest ecosystem protection and sustainable forest management.

Drone-based Reforestation Monitoring System
Drone-based Reforestation Monitoring System
Integrated system utilizing drones equipped with high-resolution RGB, LiDAR, and multispectral sensors to precisely monitor the overall condition of reforested areas. Real-time data collection and AI-based analysis automatically evaluate tree seedling survival rates and health status.
AI-based Automatic Tree Seedling Detection
AI-based Automatic Tree Seedling Detection Algorithm
Automatic tree seedling identification system using Convolutional Neural Networks (CNN) and deep learning technology. By learning characteristics of tree seedlings according to various environmental conditions and species, it detects tree seedlings and quantifies them with over 90% accuracy.
Thermal Camera-based Wildlife Monitoring
Thermal Camera-based Wildlife Monitoring
Non-invasive investigation of medium and large mammal habitat status and behavioral patterns in reforested areas using drones equipped with thermal infrared cameras. AI image recognition technology enables automatic species identification and density estimation.
Integrated Data Analysis and Decision Support
Integrated Data Analysis and Decision Support System
A decision support system that integrates multi-sensor data and AI analysis results to comprehensively evaluate the health status of reforested areas and provides optimal planting strategies and damage reduction management plans considering topographic and environmental factors.

Research Goals

Integrated Forest Monitoring Technology Development through Three Sub-projects

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Remote Monitoring System for Reforested Areas
• Multi-sensor (RGB, LiDAR, multispectral) drone data collection
• Analysis of factors affecting tree seedling survival rates and landslide occurrence
• Optimal planting strategies and damage reduction management strategies
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Biodiversity Monitoring System for Reforested Areas
• Medium and large mammal surveys using thermal camera drones
• Environmental animal identification big data construction
• Establishment of healthy forest ecosystem creation and management systems
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AI Algorithm Development for Reforestation Monitoring
• AI image classification algorithm optimization
• Advanced AI image identification technology
• Automated precision monitoring system construction

Research Methodology

Advanced AI-driven approach combining multi-modal sensing and deep learning

🛸 High-Resolution Drone Data Acquisition
Deploy multi-sensor drones equipped with RGB, LiDAR, and multispectral sensors to collect comprehensive reforestation site data. Systematic data collection across different seasons, species, and topographic conditions to build robust training datasets for AI algorithms.
🧠 Deep Learning Algorithm Development
Develop optimized Convolutional Neural Networks (CNN) for automatic tree seedling detection and wildlife identification. Implement advanced image classification and object detection algorithms with self-training capabilities for continuous improvement.
🌡️ Thermal Imaging Wildlife Monitoring
Utilize thermal infrared cameras mounted on drones for non-invasive wildlife monitoring. Implement AI-based animal species recognition and behavioral pattern analysis to assess biodiversity and ecosystem health in reforested areas.
📊 Integrated Data Analysis Platform
Develop a comprehensive platform that integrates multi-sensor data, AI analysis results, and environmental factors to provide actionable insights for forest management decisions and biodiversity conservation strategies.