文献类型：期刊文献

英文题名：Integrating Kernel-Based Vegetation Indices and Ensemble Learning for Mangrove Canopy Height Mapping Using GEDI and Sentinel Data

作者：Lai, Peilin[1];Chen, Yang[1];Chen, Wenqian[1];Ma, Lixia[2];Chen, Weijie[1];Fu, Dongyang[1];Liu, Dazhao[1];Tian, Kai[3]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China;[3]Guangdong Ocean Univ, Coll Chem & Environm, Zhanjiang 524088, Peoples R China

年份：2026

卷号：18

期号：16

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001860260400001)、、EI(收录号：20263521403104)、Scopus(收录号：2-s2.0-105048707492)、WOS

基金：This research was funded by the Guangdong Ocean University Research Start-up Fund Project for Introduced Doctoral Talents, grant number 060302112313, and the Guangxi Science and Technology Program, grant number FN2600640391.

语种：英文

外文关键词：mangrove canopy height; kernel-based vegetation indices; GEDI; Sentinel-1; Sentinel-2; DeepForest; spectral saturation; ensemble prediction dispersion

外文摘要：Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Mangrove canopy height (MCH) is a fundamental structural variable for monitoring ecosystem health and quantifying carbon stocks. However, MCH retrieval from optical satellite imagery is often constrained by spectral saturation in dense stands and environmental noise in intertidal zones. This study investigates the utility of kernel-based spectral features (KVIs) as non-linear topological enhancements for MCH estimation by integrating GEDI spaceborne LiDAR with Sentinel-2 and Sentinel-1 data across three mangrove ecosystems along the South China coast. Utilizing four regression models under spatial cross-validation, we evaluated the performance of traditional indices, KVIs, and integrated feature sets against GEDI reference measurements. Results indicate that traditional optical indices exhibit limited linear sensitivity to MCH. Rather than serving as universal accuracy boosters, KVIs function as non-linear stabilizers by redistributing spectral values in Hilbert space, which effectively enhances feature representation in high biomass stands and mitigates background noise. Furthermore, model comparisons reveal that while traditional indices yield competitive baseline accuracy in specific architectures (e.g., 1D-CNN), kernel-based features provide nuanced advantages in spatial stability and ensemble dispersion reduction. Ultimately, this study demonstrates that kernel-based features enhance model robustness under rigorous cross-validation, providing a reliable structural foundation for large-scale ecological monitoring and regional carbon dynamics assessments in heterogeneous coastal environments.