Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing

Ji Cuicui; Wan Sheng; Huang Zaoyang; Zhang Xiaochao; Luo Jing; Yang Hengcong; Pei Xiangjun

doi:10.3799/dqkx.2025.296

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Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing

更新时间：2026-10-15

- Eco⁃Environmental Quality Assessment of Major Railway Project Construction Period by Deep Learning Network Coupled with Remote Sensing
- Earth Science Vol. 51, Issue 9, Pages: 3405-3421(2026)
- 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：
- 基金信息：
- DOI：10.3799/dqkx.2025.296
  CLC： P642.5
- Received：01 September 2025，
  
  Published：30 September 2026
- 稿件说明：
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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.

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references

Akhtar, A. M., Qazi, W. A., Ahmad, S. R., et al., 2020. Integration of High⁃Resolution Optical and SAR Satellite Remote Sensing Datasets for Aboveground Biomass Estimation in Subtropical Pine Forest, Pakistan. Environmental Monitoring and Assessment , 192 (9): 584. https://doi.org/10.1007/s10661⁃020⁃08546⁃1

Arshad, F., Iqbal, M., Riaz, A., et al., 2024. Road Corridors Vegetation in the Semi⁃Arid Region: Functional Trait Diversity and Dynamics. Scientific Reports , 14 : 25212. https://doi.org/10.1038/s41598⁃024⁃76484⁃w

Chaurasia, A., Culurciello, E., 2017. LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. 2017 IEEE Visual Communications and Image Processing (VCIP). St. Petersburg. https://doi.org/10.1109/vcip.2017.8305148

El⁃Husseini, H., Chowyuk, A. N., Gustafson, R. R., et al., 2023. Roadside Vegetated Filter Strips to Simultaneously Lower Stormwater Pollution Loadings and Improve Economics of Biorefinery Feedstocks. Journal of Environmental Management , 347 : 119168. https://doi.org/10.1016/j.jenvman.2023.119168

Gao, B. C., 1996. NDWI: A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment , 58 ( 3 ): 257 - 266 . https://doi.org/10.1016/S0034⁃4257(96)00067⁃3

Gitelson, A., Merzlyak, M. N., 1994. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology , 143 ( 3 ): 286 - 292 . https://doi.org/10.1016/S0176⁃1617(11)81633⁃0

Haralick, R. M., Shanmugam, K., Dinstein, I., 1973. Textural Features for Image Classification. IEEE Transactions on Systems , Man , and Cybernetics , SMC⁃3(6): 610-621. https://doi.o rg/10.1109/TSMC.1973.4309314

Jin, S. M., Sader, S. A., 2005. Comparison of Time Series Tasseled Cap Wetness and the Normalized Difference Moisture Index in Detecting Forest Disturbances. Remote Sensing of Environment , 94 ( 3 ): 364 - 372 . https://doi.org/10.1016/j.rse.2004.10.012

Kaloyianni, M., Feidantsis, K., Nteli, I., et al., 2019. Biochemical and Molecular Responses of Cyprinids in Two Mediterranean Lacustrine Ecosystems: Opportunities for Ecological Assessment and Biomonitoring. Aquatic Toxicology , 211 : 105 - 115 . https://doi.org/10.1016/j.aquatox.2019.03.021

Karisa, J. F., Obura, D. O., Chen, C. A., 2020. Spatial Heterogeneity of Coral Reef Benthic Communities in Kenya. PLoS One , 15 ( 8 ): e0237397 . https://doi.org/10.1371/journal.pone.0237397

Ķēniņš, R., 2020. Land Cover Classification Using very High Spatial Resolution Remote Sensing Data and Deep Learning. Latvian Journal of Physics and Technical Sciences , 57(1-2): 71-77. https://doi.org/10.2478/lpts⁃2020⁃0009

Khosravi, V., Gholizadeh, A., Saberioon, M., 2022. Soil Toxic Elements Determination Using Integration of Sentinel⁃2 and Landsat⁃8 Images: Effect of Fusion Techniques on Model Performance. Environmental Pollution , 310 : 119828. https://doi.org/10.1016/j.envpol.2022.119828

Kumar, A., Kumar, P., Patil, M., et al., 2024. Disturbance and Vegetational Structure in an Urban Forest of Indian Siwaliks: An Ecological Assessment. Environmental Monitoring and Assessment , 196 (8): 691. https://doi.org/10.1007/s10661⁃024⁃12801⁃0

Magidi, J., Nhamo, L., Mpandeli, S., et al., 2021. Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine. Remote Sensing , 13 (5): 876. https://doi.org/10.3390/rs13050876

Matyukira, C., Mhangara, P., 2024. Advances in Vegetation Mapping through Remote Sensing and Machine Learning Techniques: A Scientometric Review. European Journal of Remote Sensing , 57 (1): 2422330. https://doi.org/10.1080/22797254.2024.2422330

Mohammadimanesh, F., Salehi, B., Mahdianpari, M., et al., 2019. A New Fully Convolutional Neural Network for Semantic Segmentation of Polarimetric SAR Imagery in Complex Land Cover Ecosystem. ISPRS Journal of Photogrammetry and Remote Sensing , 151 : 223 - 236 . https://doi.org/10.1016/j.isprsjprs.2019.03.015

Namazi, F., Ezoji, M., Parmehr, E. G., 2023. Paddy Rice Mapping in Fragmented Lands by Improved Phenology Curve and Correlation Measurements on Sentinel⁃2 Imagery in Google Earth Engine. Environmental Monitoring and Assessment , 195 (10): 1220. https://doi.org/10.1007/s10661⁃023⁃11808⁃3

Qi, J., Chehbouni, A., Huete, A. R., et al., 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing of Environment , 48 ( 2 ): 119 - 126 . https://doi.org/10.1016/0034⁃4257(94)90134⁃1

Ronneberger, O., Fischer, P., Brox, T., 2015. U⁃Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer⁃Assisted Intervention⁃MICCAI 2015. Springer, Cham, 234-241.

Rouse, J. W., Haas, R. H., Schell, J. A., et al., 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. NASA Special Publication , 351(1), 309-317.

Salomonson, V. V., Appel, I., 2004. Estimating Fractional Snow Cover from MODIS Using the Normalized Difference Snow Index. Remote Sensing of Environment, 89 ( 3 ): 351 - 360 . https://doi.org/10.1016/j.rse.2003.10.016

Santana, F. C., Francelino, M. R., Siqueira, R. G., et al., 2025. Sentinel⁃2 Imagery Coupled with Machine Learning to Modelling Water Turbidity in the Doce River Basin, Brazil. Environmental Monitoring and Assessment, 197 (4): 459. https://doi.org/10.1007/s10661⁃025⁃13918⁃6

Shelhamer, E., Long, J., Darrell, T., 2017. Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 ( 4 ): 640 - 651 . https://doi.org/10.1109/TPAMI.2016.2572683

Shen, Y. J., Chen, X., Peng, J. B., et al., 2024. Background Characteristics of Ecological Geological Environment System in Qinling Mountains and Assumption of Its Theoretical System. Earth Science, 49 ( 6 ): 2103 - 2119 (in Chinese with English abstract) .

Song, S., Wang, S. H., Gong, Y., et al., 2024. The Past and Future Dynamics of Ecological Resilience and Its Spatial Response Analysis to Natural and Anthropogenic Factors in Southwest China with Typical Karst. Scientific Reports, 14 : 19166. https://doi.org/10.1038/s41598⁃024⁃70139⁃6

Tsegaye, L., Bharti, R., 2022. The Impacts of LULC and Climate Change Scenarios on the Hydrology and Sediment Yield of Rib Watershed, Ethiopia. Environmental Monitoring and Assessment , 194 (10): 717. https://doi.org/10.1007/s10661⁃022⁃10391⁃3

Wang, B., Fan, D. L., 2019. Research Progress of Deep Learning in Classification and Recognition of Remote Sensing Images. Bulletin of Surveying and Mapping , (2): 99-102, 136 (in Chinese with English abstract) .

Wang, D. Z., Qiu, P. H., Fang, Y. M., 2015. Scale Effect of Li⁃Xiang Railway Construction Impact on Landscape Pattern and Its Ecological Risk. Chinese Journal of Applied Ecology , 26 ( 8 ): 2493 - 503 (in Chinese with English abstract) .

Wang, Q. Q., Yu, G. A., Li, Z. W., et al., 2025. Assessing the Ecohydrological Impact of Hydropower⁃Induced Flow Regulation on Fish Habitats in the Jinsha River. Journal of Environmental Management , 381 : 125303. https://doi.org/10.1016/j.jenvman.2025.125303

Wen, Y. T., Cai, H., Han, D., 2024. Driving Factors Analysis of Spatial⁃Temporal Evolution of Vegetation Ecosystem in Rocky Desertification Restoration Area of Guizhou Province, China. Environmental Science and Pollution Research , 31 ( 9 ): 13122 - 13140 . https://doi.org/10.1007/s11356⁃024⁃31934⁃w

Xiao, Y., Guo, Y. H., Yin, G. D., et al., 2022. UAV Multispectral Image⁃Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China. Remote Sensing , 14 (14): 3272. https://doi.org/10.3390/rs14143272

Xuan, B.X., Wei, X.Y., Mao, X.F., et al., 2025. Research on Ecosystem Services in the Xining⁃Haidong Metropolitan Area under Different Land Use Scenarios: Based on the GMOP⁃PLUS⁃InVEST Model. Earth Science , Online. https://link.cnki.net /urlid/42.1874.p.20250624.1606.002 (in Chinese with English abstract) .

Yadav, P. K., Jha, P., Joy, M. S., et al., 2024. Ecosystem Health Assessment of East Kolkata Wetlands, India: Implications for Environmental Sustainability. Journal of Environmental Management , 366 : 121809. https://doi.org/10.1016/j.jenvman.2024.121809

Zha, Y., Gao, J., Ni, S., 2003. Use of Normalized Difference Built⁃up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing , 24 ( 3 ): 583 - 594 . https://doi.org/10.1080/01431160304987

Zhang, B., 2018. Remotely Sensed Big Data Era and Intelligent Information Extraction. Geomatics and Information Science of Wuhan University , 43 ( 12 ): 1861 - 1871 (in Chinese with English abstract) .

Zhang, Y. G., Cong, J., Lu, H., et al., 2015. Soil Bacterial Diversity Patterns and Drivers along an Elevational Gradient on Shennongjia Mountain, China. Microbial Biotechnology , 8 ( 4 ): 739 - 746 . https://doi.org/10.1111/1751⁃7915.12288

Zhang, Z. H., Zhang, G. X., Song, S. F., et al., 2020. Spatial Heterogeneity Influences of Environmental Control and Informal Regulation on Air Pollutant Emissi ons in China. International Journal of Environmental Research and Public Health , 17 (13): 4857. https://doi.org/10.3390/ijerph17134857

Zhou, D., Song, W., 2021. Identifying Ecological Corridors and Networks in Mountainous Areas. International Journal of Environmental Research and Public Health , 18 (9): 4797. https://doi.org/10.3390/ijerph18094797

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