Researchers at University of Utah Target Machine Learning (Evaluating the Perfor mance of Airborne and Spaceborne Lidar for Mapping Biomass In the United States’ Largest Dry Woodland Ecosystem)

犹他大学的研究人员目标机器学习（评估机载和星载激光雷达在绘制美国最大的干林地生态系统生物量图方面的性能）

Robotics & Machine Learning Daily News2024，Issue(Jul.4) ：44-45.

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Researchers at University of Utah Target Machine Learning (Evaluating the Perfor mance of Airborne and Spaceborne Lidar for Mapping Biomass In the United States’ Largest Dry Woodland Ecosystem)

犹他大学的研究人员目标机器学习（评估机载和星载激光雷达在绘制美国最大的干林地生态系统生物量图方面的性能）

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摘要

由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在已经可用。根据NewsRx e Ditors在犹他州盐湖城的新闻报道，研究表明：“遥感精确量化地上生物量(AGB)的能力因生态系统而异。鉴于其在全球碳动态中的重要作用，在旱地生态系统中获得准确、空间和时间上明确的AG B估计值具有独特的价值。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting out of Salt Lake City, Utah, by NewsRx e ditors, research stated, “The ability of remote sensing to accurately quantify l ive aboveground biomass (AGB) varies by ecosystem. Given their important role in global carbon dynamics, deriving accurate, spatially and temporally explicit AG B estimates in dryland ecosystems is uniquely valuable.”

Key words

Salt Lake City/Utah/United States/Nor th and Central America/Cyborgs/Emerging Technologies/Machine Learning/Remote Sensing/University of Utah

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出版年

2024

Robotics & Machine Learning Daily News

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