DEM error verified by airborne data.

The accuracy of digital elevation models (DEMs) in forested areas plays a crucial role in canopy height monitoring and ecological sensitivity analysis. Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. Therefore, a CNN-LightGBM hybrid model is proposed in this paper, with four different types of forests (tropical rainforest, coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest) selected as study sites to validate the performance of the hybrid model in correcting COP30DEM in different forest area DEMs. In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. This choice is based on LightGBM’s leaf-growth strategy and histogram linking methods, which are effective in reducing the data’s memory footprint and utilising more of the data without sacrificing speed. The study uses elevation values from ICESat-2 as ground truth, covering several parameters including COP30DEM, canopy height, forest coverage, slope, terrain roughness and relief amplitude. To validate the superiority of the CNN-LightGBM hybrid model in DEMs correction compared to other models, a test of LightGBM model, CNN-SVR model, and SVR model is conducted within the same sample space. To prevent issues such as overfitting or underfitting during model training, although common meta-heuristic optimisation algorithms can alleviate these problems to a certain extent, they still have some shortcomings. To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R2 (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. The FA-SSA-CNN-LightGBM model has the highest accuracy, with an RMSE of 1.09 meters, and a reduction of more than 30% of the RMSE when compared to the LightGBM and other hybrid models. Compared to other forested area DEMs (such as FABDEM and GEDI), its accuracy is improved by more than 50%, and the performance is significantly better than other commonly used DEMs in forested areas, indicating the feasibility of this method in correcting elevation errors in forested area DEMs and its significant importance in advancing global topographic mapping.

数字高程模型（Digital Elevation Model，DEM）在林区的精度，对林冠高度监测与生态敏感性分析具有至关重要的作用。尽管近年来针对DEMs开展了大量研究，但受林冠遮挡、地形复杂、信号穿透能力有限等因素影响，林区DEM仍存在显著误差，给基于DEM的后续分析带来了诸多挑战。为此，本文提出一种CNN-LightGBM混合模型，选取热带雨林、针叶林、针阔混交林、阔叶林四类典型林型作为研究样地，以验证该模型对不同林区COP30DEM的校正性能。在本文提出的混合模型中，采用卷积神经网络（Convolutional Neural Network，CNN）的Densenet架构作为基础模块，以LightGBM作为主模型。该架构选择基于LightGBM的叶生长策略与直方图关联方法，可在不牺牲运算速度的前提下，有效降低数据内存占用，充分挖掘数据信息。本研究以ICESat-2获取的高程值作为地面真值，所用到的特征参数涵盖COP30DEM、林冠高度、森林覆盖率、坡度、地形粗糙度与地形起伏度。为验证CNN-LightGBM混合模型在DEM校正中的优越性，本文在相同样本空间内，分别开展了LightGBM模型、CNN-SVR模型与支持向量回归（Support Vector Regression，SVR）模型的对比实验。为避免模型训练过程中出现过拟合或欠拟合等问题，尽管常见的元启发式优化算法可在一定程度上缓解此类问题，但仍存在一定缺陷。为此，本文引入一种融合烟花算法（Fireworks Algorithm，FA）摄取策略的改进麻雀搜索算法（Sparrow Search Algorithm，SSA），以提升解的多样性与全局搜索能力，即基于烟花算法的麻雀搜索优化算法（FA-SSA算法）。通过多模型对比，并结合机载激光雷达（Light Detection and Ranging，LiDAR）参考数据集进行验证，结果表明：CNN-LightGBM模型的决定系数（R-Square，R²）较其他模型提升0.05以上，实验表现更优。其中FA-SSA-CNN-LightGBM模型精度最高，均方根误差（Root Mean Square Error，RMSE）达1.09米，相较于LightGBM及其他混合模型，RMSE降低幅度超过30%。与FABDEM、GEDI等其他林区DEM产品相比，其精度提升幅度超过50%，性能显著优于当前林区常用的各类DEM产品，证实该方法在校正林区DEM高程误差方面具备可行性，同时对推进全球地形制图具有重要意义。