Visual-LiDAR Odometry for Planetary Rover with Plane Constraints

We refined the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with Ice, Cloud, and Land Elevation Satellite 2 data to release a new dataset (IC2-GDEM). It has superior global elevation quality to ASTER GDEM. Its seamless integration with historical ASTER GDEM datasets is essential for longitudinal environmental studies. As a complementary data source to other GDEMs, it enables more reliable and comprehensive scientific discoveries.

Rainfall-runoff simulation based on deep learning has become popular approaches in recent years with the drawback of significant time required for training. The study proposes an incremental learning method to accelerate rainfall-runoff simulation with deep learning models. The method was tested in Yangtze River and Han River drainage basin. The results show the feasibility, robustness, suitability and potential applications in flood emergency decision-making of the method.

To better understand the glacier melting features in the SETP, multisource satellite observations including ASTER DEM, ICESat, ICESat-2 and CryoSat-2 are used in this study. We found the glacier melting rate of the entire SETP is during 2000–2022. And the glacier melting has accelerated at a rate of 31.2 % in the recent decade. A comprehensive comparison among the related existing studies revealed that our estimates have a finer temporal scale and less estimation uncertainty.

Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.