周鹏飞, 王艳霞, 周汝良. 基于ICESat–2的横断山区NASA DEM高程精度评价与误差校正[J]. 西南林业大学学报(自然科学), 2024, 44(5): 1–11. DOI: 10.11929/j.swfu.202308033
引用本文: 周鹏飞, 王艳霞, 周汝良. 基于ICESat–2的横断山区NASA DEM高程精度评价与误差校正[J]. 西南林业大学学报(自然科学), 2024, 44(5): 1–11. DOI: 10.11929/j.swfu.202308033
Zhou Pengfei, Wang Yanxia, Zhou Ruliang. Evaluation of NASA DEM Elevation Accuracy and Error Correction in the Hengduan Mountain Region Based on ICESat–2[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202308033
Citation: Zhou Pengfei, Wang Yanxia, Zhou Ruliang. Evaluation of NASA DEM Elevation Accuracy and Error Correction in the Hengduan Mountain Region Based on ICESat–2[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202308033

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基于ICESat–2的横断山区NASA DEM高程精度评价与误差校正

Evaluation of NASA DEM Elevation Accuracy and Error Correction in the Hengduan Mountain Region Based on ICESat–2

  • 摘要: 以星载激光雷达ICESat–2为参考数据源,将NASA DEM作为实验数据,分析了其在横断山区中西部复杂地形地貌区的整体高程精度;同时,研究了DEM误差与地表变量(如坡度、坡向、起伏度、植被覆盖度、土地利用类型等)之间的关系。为校正NASA DEM的高程误差,引入随机森林、反向传播神经网络、极度梯度提升树模型和轻量级梯度提升模型等4种机器学习方法,结合地表变量构建了高程误差校正模型并对校正后的NASA DEM进行精度分析。结果表明:NASA DEM的高程平均误差为–3.5 m,平均绝对误差为8.2 m,均方根误差为11.7 m,高程误差符合正态分布。NASA DEM的高程精度随着坡度、起伏度和植被覆盖度的增大而降低,不同坡向和土地利用类型下的高程精度存在较大差异。4种机器学习模型均能校正NASA DEM的高程精度,校正后的平均误差、平均绝对误差和均方根误差均有所改善;RF、XGBoost和LightGBM校正后的平均误差均为0 m;BPNN校正后平均绝对误差最低为6.9 m;BPNN和XGBoost校正后均方根误差最低均为10.6 m;BPNN校正后的R2值最高;BPNN在不同土地利用类型下的校正精度表现最佳。本研究结果为地学研究中选择和应用NASA DEM时提供精度上的参考依据,并通过引入多种机器学习模型,为NASA DEM及其他全球DEM产品高程误差校正研究提供了有效的方法论。

     

    Abstract: Using the Ice, Cloud, and land Elevation Satellite-2(ICESat-2) as a reference data source, the study analyzed the overall elevation accuracy of NASA's Digital Elevation Model(DEM) in the complex terrain of the central and western parts of the Hengduan Mountains. It also investigated the relationship between DEM errors and surface variables such as slope, aspect, roughness, vegetation cover, and land use types. Subsequently, to correct elevation errors in NASA DEM, four machine learning methods were introduced: Random Forest, Back Propagation Neural Network, Extreme Gradient Boosting, and Light Gradient Boosting Model. These were combined with surface variables to construct an elevation error correction model, and the corrected NASA DEM was analyzed for accuracy. The results showed that: The average elevation error of NASA DEM was –3.5 m, the mean absolute error was 8.2 m, and the root mean square error was 11.7 m, with elevation errors following a normal distribution. The elevation accuracy of NASA DEM decreased with increasing slope, roughness, and vegetation cover, and varied significantly across different aspects and land use types. All four machine learning models were able to correct the elevation accuracy of NASA DEM, improving the average error, mean absolute error, and root mean square error; the average errors after correction by RF, XGBoost, and LightGBM were all 0 m; the lowest mean absolute error after BPNN correction was 6.9 m; the lowest root mean square error after BPNN and XGBoost correction was 10.6 m; BPNN achieved the highest R2 value post-correction; and BPNN showed the best correction accuracy across different land use types. These results provide precision references for the selection and application of NASA DEM in geoscience research and, through the introduction of various machine learning models, offer effective methodologies for the elevation error correction research of NASA DEM and other global DEM products.

     

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