What question did this study set out to answer?

The aim is to create a detailed mapping of eucalyptus plantations to evaluate ecological risks at a municipal level.

April 11, 2026Open Access

High-Resolution Mapping of Eucalyptus Plantations for Municipal Forest Governance: A Task-Specific Deep Learning Approach in Nanning, China

BZBoyuan ZhuangSun Yat-sen University QZQingling ZhangSun Yat-sen University

Key Points

The aim is to create a detailed mapping of eucalyptus plantations to evaluate ecological risks at a municipal level.
Developed the Eucalyptus Semantic Segmentation Dataset for precise modeling.
Introduced SDCNet, a specialized deep learning model for eucalyptus mapping.
Implemented a redesigned SD-ASPP module and Coordinated Self-Attention Mechanism to improve accuracy.
Conducted ablation studies to validate model components.
Ran benchmark tests comparing SDCNet to seven existing segmentation models.
Achieved a per-class IoU of 88.83% and an F1-score of 93.81% for eucalyptus species.
Improved IoU by +2.24% and F1-score by +1.71% over the strongest baseline model.
Produced the first 0.3 m resolution eucalyptus distribution map for Nanning.
Revealed that eucalyptus plantations cover over 50% of the forested area around Nanning's main drinking water source.

Abstract

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity of fragmented stands, and (2) the difficulty in achieving precise boundary delineation due to shadowed and complex canopy edges. To address these, this study makes two primary contributions. First, we present the Eucalyptus Semantic Segmentation Dataset (ESSD)—a high-quality, pixel-level annotated dataset that includes geographic coordinates to support reproducible research. Second, we propose SDCNet, a task-specific deep learning network optimized for eucalyptus mapping. SDCNet incorporates a redesigned SD-ASPP module that leverages Deep Over-parameterized Convolution (DO-Conv) to capture multi-scale features, alongside a novel Coordinated Self-Attention Mechanism (CSAM) to enhance the accuracy of canopy boundary detection. Ablation studies confirm the effectiveness of each component. In benchmark tests against seven state-of-the-art semantic segmentation models, SDCNet achieves superior performance, obtaining a per-class Intersection over Union (IoU) of 88.83% and an F1-score of 93.81% for eucalyptus—an improvement of +2.24% in IoU and +1.71% in F1-score over the strongest baseline. Applied to Nanning City, SDCNet produces the first 0.3 m resolution eucalyptus distribution map for the region. This map reveals a critical finding: within the watershed of the Xiyunjiang Reservoir—Nanning’s primary drinking water source—eucalyptus plantations cover more than 50% of the forested area. This result provides the first quantitative, high-resolution evidence of potential hydrological risk at a municipal scale. Our work establishes an integrated framework that bridges advanced remote sensing with actionable forest governance, offering scientifically grounded support for ecological risk assessment and sustainable land-use policy.

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