Abstract
The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.
Highlights
The Earth surface survey provides fundamental and useful geo-information for terrestrial ecosystems, global climate monitoring, and landform mapping [1,2,3,4]
Compared to the five other methods, the flexible method had a lower standard deviation (3.592◦), lower root-mean-square error (RMSE) (3.596◦), and higher R2 (0.829) in the terrain slope estimation. This indicated that the estimation of the terrain slope was improved by considering the footprint shape, orientation, and terrain aspect
For the high-eccentricity footprints, the flexible method significantly reduced the RMSE of the terrain slope estimation by ~0.8◦ when compared with methods 3, 4, and 5 and by ~1.1◦ compared with methods 1 and 2
Summary
The Earth surface survey provides fundamental and useful geo-information for terrestrial ecosystems, global climate monitoring, and landform mapping [1,2,3,4]. Development of space-borne remote sensing techniques has addressed this challenge to some extent [8,9,10]. The optical stereo photogrammetry and Synthetic Aperture Radar have generated the available global digital elevation models (DEMs) [10,11]. The vertical accuracies of these DEMs are not high at ~15 m [12,13,14]. The space-borne LiDAR has become the most promising technique for accurately measuring the terrain characteristics on a global scale [21,22]
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