北京林业大学学报2026，Vol.48Issue(3)：128-139,12.DOI:10.12171/j.1000-1522.20250279
HWCFU-Net:融合多源遥感数据的像元级台风灾后森林冠层高度损失评估
HWCFU-Net:pixel-level assessment of post-typhoon forest canopy height loss using multi-source remote sensing data
摘要
Abstract
[Objective]Typhoon-induced disturbances severely disrupt forest canopy structure,degrade ecosystem services,and diminish carbon sequestration capacity.There is an urgent need for efficient and accurate methods to assess typhoon-related forest damage.Current approaches face two major challenges:(1)the inability to characterize spatial heterogeneity and gradient-based canopy responses at the pixel scale under typhoon stress,and(2)technical limitations in inversion accuracy and spatial continuity caused by scale/noise heterogeneity in multi-source remote sensing data and detail loss in optical imagery.To address these issues,this study aims to develop a pixel-level inversion framework that integrates multi-source remote sensing data for fine-scale detection of canopy height changes before and after typhoons,and to investigate how elevation gradients and urban boundaries modulate the spatial patterns of canopy loss.[Method]We propose an enhanced U-Net architecture—Hierarchical Wavelet-enhanced and Contextual Feature-integrated U-Net(HWCFU-Net).The model incorporates a hierarchical feature enhancement module based on discrete wavelet transform to strengthen both high-and low-frequency information representation,thereby mitigating multi-source data heterogeneity.It further introduces a hierarchical contextual feature integration unit that employs multi-order depthwise separable convolutions to optimize multi-scale feature transmission and selection.A pixel-wise regression strategy is adopted to independently model each pixel and directly predict continuous canopy height values,overcoming the limitations of traditional whole-image single-label or zonal averaging approaches.We integrated GEDI and ICESat-2 LiDAR data with Sentinel-1/2 optical–radar observations to construct a multi-source spatiotemporal feature set.Six pre-and post-disaster experimental scenarios were established using three representative typhoons in 2019:Lekima,Kammuri,and Hagibis.The model was systematically compared against six state-of-the-art methods:U-Net,U-Net++,AttentionRes-UNet,TSNN,Y-NET,and Random Forest.[Result]HWCFU-Net consistently outperformed all benchmarks across all experimental scenarios,achieving coefficients of determination(R2)ranging from 0.62 to 0.71 and root mean square errors(RMSE)between 3.98 and 4.87 m.Compared to deep learning baselines,it improved R2 by 0.01–0.14.Against Random Forest,R2 increased by 0.01–0.09 and RMSE decreased by 0.13–1.03 m.The highest accuracy was achieved in the pre-typhoon Lekima scenario(R2=0.71,RMSE=3.98 m),demonstrating the model's robustness and generalization capability.Spatial analysis revealed heterogeneous canopy loss patterns:low-elevation broadleaf forests suffered the greatest damage due to shallow root systems and low wind resistance,while mid-to high-elevation coniferous forests experienced relatively minor losses.Forests near urban areas endured stronger wind shear induced by surface roughness and building-induced turbulence or canyon effects,with damage intensity significantly attenuating with distance from cities.Short-term increases in local vegetation indices suggested compensatory growth triggered by post-typhoon rainfall and improved moisture conditions.Elevation gradients significantly shaped loss distribution by influencing forest composition and structural stability,while urban boundaries amplified typhoon wind fields.[Conclusion]The study demonstrates that elevation gradient effects and urban boundary effects jointly govern the spatial heterogeneity of forest canopy loss.The proposed pixel-level inversion method effectively addresses data heterogeneity and detail loss,substantially improving disaster assessment accuracy.It provides a reliable theoretical foundation and technical support for forest disaster mitigation and ecological adaptation planning.关键词
台风灾害/森林冠层高度/像元级反演/多源遥感融合/深度学习/空间异质性/海拔梯度/城市边界效应/HWCFU-NetKey words
typhoon disaster/forest canopy height/pixel-level inversion/multi-source remote sensing fusion/deep learning/spatial heterogeneity/elevation gradient/urban boundary effect/HWCFU-Net分类
农业科技引用本文复制引用
俞云航,张礼,陈帮乾,云挺..HWCFU-Net:融合多源遥感数据的像元级台风灾后森林冠层高度损失评估[J].北京林业大学学报,2026,48(3):128-139,12.基金项目
国家自然科学基金项目(32371876、32271877),国家重点研发计划(2022YFE0128100),江苏省自然科学基金面上项目(BK20221337),中国热带农业科学院橡胶研究所开放课题计划(RRI-KLOF202502). （32371876、32271877）
