期刊论文

Zhou, C

英文

Forests

1999-4907

2023

14

10

1985-

10.3390/f14101985

Conceptualization, X.S. and C.Z.; funding acquisition, C.Z.; methodology, X.S., C.Z., J.X. and Z.O.; resources, C.Z., J.X. and Y.L.; supervision, C.Z., J.X. and Y.L.; writing—original draft, X.S.; writing—review and editing, X.S., C.Z., Z.O. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 42074016 and 42030112) and the Hunan Provincial Natural Science Foundation of China (No. 2021JJ30244).

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The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear rela...