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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-G-2025
https://doi.org/10.5194/isprs-annals-X-G-2025-559-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-G-2025-559-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 Jul 2025
| 11 Jul 2025
Integrating Phenological Priors with Deep Spatio-Temporal Features for tree species mapping
Zhanyu Ma
LIESMARS, Wuhan University, Wuhan, China
Ningning Zhu
LIESMARS, Wuhan University, Wuhan, China
Zhen Dong
LIESMARS, Wuhan University, Wuhan, China
Ruibo Chen
Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing, Nanning, China
Bisheng Yang
LIESMARS, Wuhan University, Wuhan, China
Zhe Chen
LIESMARS, Wuhan University, Wuhan, China
Chen Long
LIESMARS, Wuhan University, Wuhan, China
Ruifei Ding
LIESMARS, Wuhan University, Wuhan, China
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This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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Surface crevasses play an important role in glacier flow, iceberg calving, and meltwater routing, yet their depths remain poorly quantified at catchment or larger scales. We use ICESat-2 laser altimetry to estimate crevasse depths on Sermeq Kujalleq (Jakobshavn Isbræ) in 2019. Over ten thousand crevasses are mapped from along-track detection. Results show deepest crevasses occur ~20 km inland, demonstrating the potential of ICESat-2 for high-resolution crevasse depth mapping.
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