深度网络耦合遥感的重大铁路工程施工期生态环境质量评价

姬翠翠; 万胜; 黄早阳; 张晓超; 罗璟; 杨恒聪; 裴向军

doi:10.3799/dqkx.2025.296

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深度网络耦合遥感的重大铁路工程施工期生态环境质量评价

更新时间：2026-10-15

- 深度网络耦合遥感的重大铁路工程施工期生态环境质量评价
- Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing
- 地球科学 2026年51卷第9期页码：3405-3421
- 作者机构：
  
  1.重庆交通大学智慧城市学院，重庆 400074
  2.成都理工大学生态环境学院，四川成都 610059
  3.成都理工大学地质灾害防治与地质环境保护全国重点实验室，四川成都 610059
  4.成都理工大学国家环境保护水土污染协同控制与联合修复重点实验室，四川成都 610059
  5.天府永兴实验室，四川成都 610213
- 作者简介：
  
  姬翠翠（1984-），女，副教授，博士生导师，研究方向为植被参量定量反演、森林火灾风险评估和区域生态环境遥感监测.ORCID：0000⁃0001⁃7764⁃6422. E⁃mail：cuicuiji@whu.edu.cn
  杨恒聪，E⁃mail：yanghc@mails.cqjtu.edu.cn
  裴向军，E⁃mail：pxj@cdut.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2023YFC3007103);西藏自治区重点研发项目(XZ202401ZY0091);天府永兴实验室有组织的科研项目(2023KJGG05);国家自然科学青年基金项目(42301459);地质灾害防治与地质环境保护国家重点实验室开放基金资助项目(SKLGP2022K028);国家环境保护水土污染协同控制与联合修复重点实验室开放基金项目(GHBK⁃2023⁃04)
- DOI：10.3799/dqkx.2025.296
  中图分类号： P642.5
- 收稿：2025-09-01，
  
  纸质出版：2026-09-30
- 稿件说明：
移动端阅览
姬翠翠，万胜，黄早阳，张晓超，罗璟，杨恒聪，裴向军，2026.深度网络耦合遥感的重大铁路工程施工期生态环境质量评价.地球科学，51（9）：3405-3421.

Ji Cuicui，Wan Sheng，Huang Zaoyang，Zhang Xiaochao，Luo Jing，Yang Hengcong，Pei Xiangjun，2026.Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing.Earth Science，51（9）：3405-3421.
姬翠翠，万胜，黄早阳，张晓超，罗璟，杨恒聪，裴向军，2026.深度网络耦合遥感的重大铁路工程施工期生态环境质量评价.地球科学，51（9）：3405-3421. DOI： 10.3799/dqkx.2025.296.

Ji Cuicui，Wan Sheng，Huang Zaoyang，Zhang Xiaochao，Luo Jing，Yang Hengcong，Pei Xiangjun，2026.Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing.Earth Science，51（9）：3405-3421. DOI： 10.3799/dqkx.2025.296.

摘要

针对西南山区重大铁路工程施工期生态环境扰动监测空间精度低的问题，本研究提出运用“深度网络耦合遥感光谱特征”框架进行生态环境质量动态评价.利用2019-2024年Sentinel-2的10 m分辨率时序影像，构建由影像光谱波段、光谱指数和纹理特征组成的输入特征集，采用U-Net语义分割模型，动态评价多年西南山区重大铁路工程施工期生态环境质量，并与全卷积网络（FCN）和LinkNet模型评价成效进行对比.结果表明：U-Net测试集精度达91.65%，显著优于FCN（82.95%）和LinkNet（90.23%），该网络模型结合光谱波段和植被指数特征更鲁棒可靠.其次，发现2019-2024年优等区面积锐减28.5%，精准识别出2021-2022年铁路主体施工期为生态退化关键阶段，生态质量指数下降0.18.再者，结合变化分析揭示了“一般→较差”与“良→一般”为主的生态退化路径，并量化了扰动时空异质性，发现以弃渣场、交通便道为主体的持续退化热点区域.总体区域上生态质量呈“东西高、中间低”格局，优等区分布在林地为主区域，差等区集中在稀疏灌草的生态退化区域.研究证明，深度学习网络耦合遥感光谱特征方法可成为铁路工程施工期生态环境质量评价的新范式，能够实现复杂山区交通廊道生态质量的高精度、快速监测，为重大铁路工程生态风险精准防控与恢复策略制定提供科学依据.

Abstract

To address the challenges of low spatial accuracy and poor spatio-temporal continuity in monitoring ecological disturbances in the construction of major railway projects in mountainous regions of Southwest China

this study proposes a dynamic evaluation framework that integrates deep networks with remote sensing spectral features. Using Sentinel-2 10 m resolution time-series imagery from 2019 to 2024

we developed input feature datasets comprising bands of remote sensing images

spectral indices and textural features. A U-Net-based semantic segmentation model was developed for assessing ecological quality

with comparative analyses conducted against FCN and LinkNet. This approach enabled dynamic evaluation of ecological quality during the construction period of the railway in the mountainous areas of Southwest China in years. The results show that the U-Net model achieved a test accuracy of 91.65%

significantly outperforming FCN (82.95%) and LinkNet (90.23%)

demonstrating its robustness when coupled with spectral features. From 2019 to 2024

the area of high-quality zones decreased sharply by 28.5%

with the main construction period of 2021-2022 identified as the most critical phase of ecological degradation

during which the ecological quality index dropped by 0.18. Change trajectory analysis further revealed dominant degradation pathways

such as “moderate→poor” and “good→moderate”

and quantified the spatio-temporal heterogeneity of disturbances. Persistent degradation hotspots were primarily associated with waste dump sites and construction access roads. Overall

the region exhibited a spatial pattern of “high quality in the east and west

low in the center” in which high-quality zones are distributed in areas dominated by forest land

while the low-quality zones are concentrated in ecologically degraded areas with sparse shrubs and grasses. This study demonstrates that integrating deep learning with remote sensing spectral features provides a novel paradigm for ecological quality assessment during railway construction. It enables rapid high-precision monitoring of ecological quality in complex mountainous corridors

offering a scientific basis for precise ecological risk management and restoration strategies in major railway projects.

关键词

Keywords

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