1. Introduction
Trees Outside Forests (TOF) play a crucial role in biodiversity conservation, climate regulation, and local livelihoods, yet their accurate assessment remains a significant challenge. Traditional remote sensing and manual inventory methods often struggle with the scattered and heterogeneous nature of TOF, leading to underestimation and inefficient monitoring. This research introduces a deep learning approach to overcome these limitations, enabling precise mapping and classification of TOF. Models used in this article include: Convolutional Neural Networks (CNNs) and U-Net.
2. Related Work
Previous efforts in TOF mapping have largely relied on pixel-based classification or object-based image analysis (OBIA) using traditional machine learning algorithms like Support Vector Machines (SVM) or Random Forests. While effective for broad forest cover, these methods often lack the granularity and generalization capability required for individual tree detection outside dense forests. Recent advancements in deep learning have shown promise in complex image interpretation tasks, suggesting a potential for improved TOF assessment.
3. Methodology
Our methodology involves a multi-stage deep learning pipeline, beginning with the acquisition and preprocessing of very high-resolution satellite imagery. This is followed by the training of a sophisticated semantic segmentation model, specifically a U-Net architecture, to delineate individual tree crowns and classify different tree types. A diverse dataset, comprising annotated images from various geographical regions, was used to train and validate the model, ensuring robustness and generalization across different landscapes.
4. Experimental Results
The experimental results demonstrate a significant improvement in both the mapping accuracy and classification precision of Trees Outside Forests compared to benchmark methods. Our deep learning model achieved an F1-score of 0.88 for tree presence detection and an overall classification accuracy of 85% across various TOF categories. The model effectively identified scattered trees, hedgerows, and urban trees, which are often missed by conventional approaches.
A summary of the model's performance on key metrics is presented in the table below, showcasing its superior capabilities:
| Metric | Our Deep Learning Model | Traditional ML Model (e.g., Random Forest) |
|---|---|---|
| F1-score (Tree Detection) | 0.88 | 0.72 |
| Overall Classification Accuracy | 85% | 70% |
| Precision (Urban Trees) | 0.91 | 0.75 |
| Recall (Hedgerows) | 0.82 | 0.65 |
5. Discussion
The superior performance of the deep learning framework highlights its potential to revolutionize TOF monitoring and inventory practices, providing more accurate and timely data for environmental management and policy-making. The high precision in identifying fragmented tree cover can significantly enhance our understanding of their ecological contributions. Future work will focus on integrating time-series data for change detection and exploring more advanced transformer-based architectures for improved contextual understanding.