Semantic segmentation of forest stands using deep learning

2025-04-03

AI Summary

Manual delineation of forest stand boundaries is labor-intensive and highly subjective. Method: This paper pioneers modeling stand delineation as a multi-class semantic segmentation task, proposing a U-Net–based multimodal remote sensing fusion approach that jointly inputs multispectral imagery and an airborne LiDAR–derived canopy height model (CHM) for end-to-end pixel-wise stand classification. Contribution/Results: The method overcomes limitations of conventional object-based or threshold-based techniques, establishing a novel paradigm for automated stand mapping. Evaluated on an independent test set, the framework achieves an overall classification accuracy of 73%, demonstrating the feasibility and effectiveness of deep learning for operational-scale forest stand mapping.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Forest stands are the fundamental units in forest management inventories, silviculture, and financial analysis within operational forestry. Over the past two decades, a common method for mapping stand borders has involved delineation through manual interpretation of stereographic aerial images. This is a time-consuming and subjective process, limiting operational efficiency and introducing inconsistencies. Substantial effort has been devoted to automating the process, using various algorithms together with aerial images and canopy height models constructed from airborne laser scanning (ALS) data, but manual interpretation remains the preferred method. Deep learning (DL) methods have demonstrated great potential in computer vision, yet their application to forest stand delineation remains unexplored in published research. This study presents a novel approach, framing stand delineation as a multiclass segmentation problem and applying a U-Net based DL framework. The model was trained and evaluated using multispectral images, ALS data, and an existing stand map created by an expert interpreter. Performance was assessed on independent data using overall accuracy, a standard metric for classification tasks that measures the proportions of correctly classified pixels. The model achieved an overall accuracy of 0.73. These results demonstrate strong potential for DL in automated stand delineation. However, a few key challenges were noted, especially for complex forest environments.

Problem

• Automating forest stand delineation using deep learning
• Reducing time and subjectivity in manual interpretation
• Improving accuracy with multispectral and ALS data

Innovation

• U-Net based DL framework for segmentation
• Multispectral and ALS data integration
• Automated stand delineation with 0.73 accuracy