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Updated: Jan 10, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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机器学习和基于过程的模拟在阿拉斯加州的活性层厚度的时空变化

Sagar Gautam1, Umakant Mishra2, Sarah N Scott3

  • 1Bioscience Division, Sandia National Laboratory, Livermore, CA, 94550, USA. sgautam@sandia.gov.

Scientific reports
|November 27, 2025
PubMed
概括

阿拉斯加的永久土退化正在加速,增加了活跃层厚度 (ALT). 机器学习模型显示未来的ALT增加比传统方法要小,这对于北极适应战略至关重要.

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